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Long-term stability of cortical ensembles

  1. Jesús Pérez-Ortega  Is a corresponding author
  2. Tzitzitlini Alejandre-García
  3. Rafael Yuste
  1. Department of Biological Sciences, Columbia University, United States
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Cite this article as: eLife 2021;10:e64449 doi: 10.7554/eLife.64449

Abstract

Neuronal ensembles, coactive groups of neurons found in spontaneous and evoked cortical activity, are causally related to memories and perception, but it is still unknown how stable or flexible they are over time. We used two-photon multiplane calcium imaging to track over weeks the activity of the same pyramidal neurons in layer 2/3 of the visual cortex from awake mice and recorded their spontaneous and visually evoked responses. Less than half of the neurons remained active across any two imaging sessions. These stable neurons formed ensembles that lasted weeks, but some ensembles were also transient and appeared only in one single session. Stable ensembles preserved most of their neurons for up to 46 days, our longest imaged period, and these ‘core’ cells had stronger functional connectivity. Our results demonstrate that neuronal ensembles can last for weeks and could, in principle, serve as a substrate for long-lasting representation of perceptual states or memories.

Introduction

Neuronal ensembles, defined as a group of neurons that fire together, are thought to underlie the neural representations of memories, perceptions, thoughts, motor programs, computations, or mental states (Lorente de No, 1938; Hebb, 1949; Cossart et al., 2003; Ikegaya et al., 2004; Sasaki et al., 2007; Buzsáki, 2010; Shepherd and Grillner, 2010; Yuste, 2015; Stringer et al., 2019b; Carillo-Reid and Yuste, 2020). Using two-photon calcium imaging, ensembles have been found in mouse visual cortex during spontaneous activity and after visual stimulation (Cossart et al., 2003; Miller et al., 2014; Carrillo-Reid et al., 2015b; Stringer et al., 2019c). The optogenetic activation of the ensembles can lead to behavioral effects consistent with the hypothesis that they represent perceptual or memory states (Carrillo-Reid et al., 2019; Marshel et al., 2019). Interestingly, while single-cell tuning remains stable in visual cortex (Ranson, 2017; Jeon et al., 2018), a representational drift occurs across days (Deitch et al., 2020). Both stability and flexibility in different brain areas have been reported using single-cell recordings (Lütcke et al., 2013; Ziv et al., 2013; Driscoll et al., 2017; Gonzalez et al., 2019; Rule and Harvey, 2019). However, there is a lack of multineuronal studies on cortical activity across days. Thus, we asked whether ensembles are preserved across days and how flexible they are, that is, how many neurons firing together on one day continue to do so in the following days and how many of them stop firing together. We also explored whether the stability or flexibility of ensembles are different between spontaneous and visually evoked activity. To study this, we performed longitudinal calcium imaging experiments using two-photon multiplane microscopy in visual cortex of awake mice and measured the responses of the same neurons for up to 46 days. Functional connectivity based on neuronal coactivity was used to detect neuronal ensembles. We found that more than 50% of ensembles during spontaneous and visually evoked activity are stable. The rest of the ensembles (transient ensembles) appeared in only one session with no difference in the number of neurons or functional structure compared to stable ensembles. Analyzing stable ensembles, we found that ~68% of their neurons were preserved over weeks (stable neurons), whereas the rest were not (flexible neurons). Functional connectivity analysis revealed that stable neurons were more connected than neurons which were eventually lost. Our results reveal long-term stability, over several weeks, of ensembles built, mostly, by neurons that are more functionally connected.

Results

Experimental and analysis rationale

We performed two-photon calcium imaging of pyramidal cells in layer 2/3 of the visual cortex from six transgenic mice (GCaMP6s, n = 4 animals; GCaMP6f, n = 2) through a cranial window to examine the stability of ensembles under visually evoked and spontaneous activity. We head-fixed mice in front of a blue screen monitor, and they were free to run on a wheel (Figure 1A). We first measured the spontaneous neuronal activity in response to a static blue screen (Figure 1B). Then, we recorded visually evoked activity by displaying 50 times a single-orientation blue drifting gratings stimulus (2 s each) with a static blue screen between presentations (at 1–5 s random intervals; Figure 1C). For either spontaneous or evoked activity, we recorded three sessions each day. To track the same neurons across days, multiplane calcium imaging was performed. A reference plane (0 µm) from day 1 was first located, then two extra planes 5 µm apart were recorded, above (–5 µm) and below (+ 5 µm) the reference plane (Figure 1D, left). After imaging, maximum intensity projection frames were created from the three planes to assemble a single video per session (Figure 1D, right). We then identified regions of interest (ROIs) of neuronal activity and kept neurons with peak signal-to-noise ratio (PSNR) >18 dB (Figure 1E, left). Calcium signals from each ROI were then deconvolved for spike inference and thresholded to generate a binarized signal, which we used to build spike raster plots (Figure 1E, right). We then analyzed all ROIs to build a binary raster plot (Figure 1F) and recorded the activity of the same neurons on days 2, 10, and 43 or 46 (Figure 1G). Some animals were imaged on day 43 and others on day 46, but we combined the data from those days (days 43–46; Figure 1—source data 1).

Figure 1 with 5 supplements see all
Experimental protocol.

(A) Experimental setup: a mouse with a cranial window is placed on a treadmill in front of a blue screen monitor, and it is head-fixed under the two-photon microscope for calcium imagin. (B) Static blue screen was used to record spontaneous activity during 5 min, three sessions per day, 5 min apart between them. (C) Visual stimulation protocol constituted of 50 repetitions of a 2 s single-orientation drifting gratings with a mean static screen between each of them during 1–5 s randomly to record evoked activity for 5 min, three sessions per day, 5 min apart between them. (D) Strategy to image the same neurons in the field of view on different days: (left) a single plane was carefully located in a reference recorded position (day 1), then two extra planes also were imaged separated 5 µm up and down. Three planes were imaged in a period of 81 ms. (Right) Maximum intensity projection was obtained from the three planes generating a single frame. Scale bar: 50 µm. (E left) Detection of regions of interest (ROIs; gray shapes) based on Suite2P algorithm, green ROI is used as a representative example, scale bar: 50 µm; (up right) extraction of calcium signal (ΔF/F0) with peak signal-to-noise ratio (PSNR) >18 dB; (middle right) spike inference using foopsi algorithm; (bottom right) a binary signal obtained by thresholding spike inference, which is used to represent the active frames of the neuron. (F) Raster plot built with binary signals from active neurons recorded simultaneously. Each row represents the activity of a single neuron, black dots represent the activity of the neuron. (G) Center of an example image at 4× zoom (white square on D right) recorded in the same location up to 46 days after the first day of recording. Note that image at day 46 is noisier than on the first days. Scale bar: 12.5 µm. (H) Count of active neurons in different days. The number of active neurons identified on day 1 decreased significantly on days 43–46 during spontaneous and evoked activity (p=0.023 and p=0.013, respectively). (I) Percentage of single-neuron activity, that is, percentage of frames a neuron was active. The average activity of all neurons was ~15% on all days during spontaneous and evoked activity. (J) Merge of active neuron ROIs from two sessions: first session from day 1 (green in four panels) versus a second single session from same day 1 (5 min later), one session from days 2, 10, and 46 (from left to right, respectively, magenta). The intersection of active neurons in both sessions is in gray color. Scale bar: 50 µm. (K) Percentage of active neurons between two sessions: first session from day 1 (green) versus second sessions from days 1, 2, 10, and 46 (magenta). Common active neurons (gray) in both sessions were 42% ± 2% during spontaneous and 37% ± 4% during evoked activity. There were no significant differences on common active neurons between days 1 and 2, but a significant decrease on days 10 and 43–46 during spontaneous (p=2 × 10–6 and p=4 × 10–9, respectively) and evoked activity (p=2 × 10–4 and p=4 × 10–9, respectively). Data are presented as mean ± SEM. Kruskal–Wallis test with post hoc Tukey–Kramer: *p<0.05, **p<0.01, and ***p<0.001. See Figure 1—source data 2.

We first examined if recording duration influenced the number of active cells found. As neurons can become active at different times, one would expect to capture more active neurons the longer the recording session. At the same time, a very long imaging session would not be practical. To define an imaging duration that significantly captured the neuronal activity present in the imaged field, pilot experiments were carried out and data were tabulated across imaging sessions of increasing duration (Figure 1—figure supplement 1A). We found that the accumulated number of active neurons reached a plateau after a few minutes. Based on this curve, we reasoned that, with our imaging and analysis pipeline, intervals of 5 min would capture the majority of active neurons in the imaged territories and carried out the rest of the study by imaging spontaneous and evoked activity during 5 min intervals.

Less than half of neurons remained active between sessions

We then inquired if the number of active neurons was constant across time and counted active neurons in the field of view on different days. On day 1, we found an average of 83 ± 6 and 85 ± 5 (mean ± SEM) active neurons during spontaneous and evoked activity imaging periods, respectively. While the number of active neurons was similar for days 1–10, a significant decrease in the number of active neurons occurred in days 43–46, in both spontaneous and evoked activity (Figure 1H; 51 ± 10 neurons; p=0.023 and p=0.013, respectively). Differences in z-displacement were similar across days (<4 µm, see Materials and methods, Figure 1—figure supplement 2A), but mice running speed tended to increase across days (Figure 1—figure supplement 2B). These factors could not explain the decrease in the number of active neurons. However, the percentage of discarded neurons, with poor signal to noise (PSNR <18 dB), significantly increased from day 1 to days 43–46 (from 24 ± 1% to 37 ± 4%, mean ± SEM; p=0.034; Figure 1—figure supplement 2C). Therefore, the loss of active cells on days 43–46 was likely due to a decrease in imaging quality, which could be partially explained by surgical attachment-related traumas (Figure 1—figure supplement 3). At the same time, the percentage of time that a neuron was active (the fraction of frames with activity) did not change over time and was ~15.5% either for spontaneous or evoked activity (Figure 1I). On average, the level of neuronal activity remained similar across days.

We then explored if active neurons from the first session remained active in following sessions (Figure 1J). We named ‘common’ the neurons that were repeatedly active in two different sessions. Surprisingly, less than 50% of the neurons were repeatedly active across days, and this proportion became reduced over time (Figure 1K). Even 5 min later, within day 1, only 42 ± 2% and 37 ± 4 % (mean ± SEM) of neurons remained active in the subsequent imaging session during spontaneous and evoked activity (gray in Figure 1K). This low number of common neurons was not explained by the possibility that not all active neurons are completely captured in a 5 min interval as this number plateaus (Figure 1—figure supplement 1B). However, the percentage of common neurons continued to decrease monotonically across days with significant decrements on days 10 and 43–46 in both spontaneous and evoked activity (gray in Figure 1K). Despite the generalized decrease in the number of common neurons, we found a significant number of neuron which were still active 43–46 days later during spontaneous and evoked activity (17 ± 7 and 22 ± 8; mean ± SEM). Moreover, these common neurons had stable responses to locomotion correlation and tuning across days (Figure 1—figure supplements 4 and 5) consistent with previous studies (Ranson, 2017; Jeon et al., 2018). We concluded that the neurons activated spontaneously or by visual stimulation are dynamically changing, and only a small proportion of neurons were repeatedly active across sessions from minutes to weeks.

Neuronal ensembles identification based on functional connectivity

To further test whether a group of neurons remains firing together in following imaging sessions, we evaluated the common neurons between sessions. If we consider non-common neurons across days, we could conclude that neurons are no longer in the ensembles when, possibly, the neurons were instead silent or out of the field of view due to a displacement in the z-plane. Thus, we focused our analysis on the correlational properties of common neurons by identifying the neuronal ensembles they formed and then evaluating if these ensembles remained across days. To do so, we built binary raster plots of the common neurons (Figure 2A) and detected ensembles using their functional connectivity (Pérez-Ortega et al., 2016). We identify whether there was a significant functional connection between each pair of neurons to build a functional network graph (Figure 2B). Specifically, to identify a significant coactivation between every pair of neurons, we first generated 1000 spike raster surrogates by a random circular shift in time of the active frames (Figure 2C, left). Then we tabulated how many times a given pair of neurons were coactive by chance and used a 95% threshold on the cumulative probability from surrogate coactivations to define a functional connection (Figure 2C, right). The functional connections of each neuron were independent of its level of activity (R = 0.001, Figure 2—figure supplement 1). We then filtered the raster plot removing the activity without significant coactivations (Figure 2D). To explore similarities between coactivations, treating each frame as a vector (one frame bin = 81 ms), we computed the Jaccard similarity between every pair of vectors (Figure 2D, bottom). Jaccard similarity indicates the fraction of the same active neurons between two vectors, that is, while a value of 0 means that neurons from one vector are all different from those of the other vector, a value of 1 means that all neurons between both vectors are identical. To detect ensembles, we identified patterns of coactivation (i.e., clusters of vectors) by performing hierarchical clustering of all vectors by single linkage, keeping only the most similar vectors (>2/3 Jaccard similarity, red dotted line in Figure 2E). Similar vectors were clustered by Ward linkage using contrast index to determine the number of groups (i.e., neuronal ensembles; Figure 2F). Finally, we extracted the neuron identity from the ensembles and built their raster plots and spatial maps (Figure 2G; see Materials and methods for details). We identified the ensembles for every single 5 min session and evaluated if they were preserved over time.

Figure 2 with 2 supplements see all
Ensemble identification.

(A) Example of a raster plot from a single session from day 1 of a GCaMP6s mouse during evoked activity showing the common neurons between days 1 and 43. (B) Functional neuronal network obtained from raster in (A), where every node represents a neuron and every link represents a functional connection between neurons. The network is plotted preserving the spatial location of the neurons. (C) Method to identify a significant functional connection between neurons. Taking the activity of each pair of neurons a and b (top left), we generated 1000 surrogates by a circular shift of their activity (bottom left), in a random amount of time, to disrupt the temporal dependency. Then, a cumulative distribution probability of the surrogate coactivations for each pair of neurons is built (top right), which is used to define a threshold of the number of coactivations at 95% of chance. Then we put a functional connection between those neurons if they reach a significant number of coactivations (p<0.05), that is, a number of coactivations bigger than the threshold. (D, top) Raster (A) filtered based on functional connectivity (B). We removed activity from neurons with no significant coactivity. (Bottom) Jaccard similarity between every column vector (single frame). (E, top) Column vectors from raster in (D) sorted by hierarchical clustering using single linkage based on its Jaccard similarity (bottom). Red dotted line indicates the most similar vectors depicted by thresholding the hierarchical clustering with Jaccard similarity >2/3. (F, top) Most similar vectors in (E) sorted by hierarchical clustering using Ward linkage based on Jaccard similarity (bottom) and grouped in different ensembles (different color each) based on the contrast index. (G, top left) Same sorted vectors in (F) but here the neurons were sorted depending on their belonging ensemble (top right). (Bottom) Functional neuronal networks representing the ensembles plotted to preserve the spatial location of the neurons.

Stable neuronal ensembles can last 46 days

Using this approach, we identified an average of 4.57 ± 0.14 and 4.63 ± 0.14 (mean ± SEM) ensembles on spontaneous and evoked activity sessions, with no significant differences between them (Figure 2—figure supplement 2). We then inquired whether ensembles were preserved across days (stable) or not (transient). To do so, we compared how many neurons of an ensemble captured during the first session were present in an ensemble during a future session. We define a stable ensemble as the ensemble that maintains at least 50% of its neurons across days (Jaccard similarity ≥1/3; see Materials and methods). This criterion was chosen to set a minimum threshold of similarity to identify an ensemble in a future session, considering the possibility of capturing a rotation in its elements. Comparing two imaging sessions from either spontaneous or evoked activity, we found that some ensembles were preserved but others were not (Figure 3A,B). We termed ‘stable’ the ensembles found on day 1, which were preserved in subsequent days, and ‘transient’ all other ensembles. On day 1, stable ensembles constituted 54 ± 3% (mean ± SEM) of all ensembles during spontaneous activity, and 72 ± 4% (mean ± SEM) of ensembles during evoked activity (Figure 3C). Similar trends were observed on days 2 and 10, and stable ensembles were ~50% of all ensembles in both spontaneous or evoked activity on days 43–46 (Figure 3C). Visually evoked ensembles were more stable than spontaneous ones during the first days, but only some of them were similarly stable as spontaneous ones up to 43–46 days. This result suggests that some ensembles during evoked activity could adapt to the stimulus.

Figure 3 with 7 supplements see all
Stable and transient ensembles across days.

(A) Example of raster plot from days 1 and 43 of a GCaMP6s mouse during evoked activity showing the common neurons between days 1 and 43. (Left) Stable ensembles activity sorted by ensemble (background colors show different ensembles) and day (color lines divide the activity from each day in each ensemble). (Middle) Transient ensembles activity sorted by day. (Right) Identity structure of the neuronal ensembles. Ensemble robustness values are at the bottom of each ensemble activity. Ensemble robustness is computed per ensemble per single session. (B) Functional networks from ensembles in (A) preserving the spatial location of the neurons. Stable (top) and transient (bottom) ensembles separated by day observed. At bottom of each ensemble are values of the number of neurons and the density of the functional connectivity within the ensemble. (C) Number of stable and transient ensembles during spontaneous (left) and evoked (right) activity on all days recorded. (D) Number of neurons per ensemble with no significant difference between stable and transient ensembles during spontaneous (left) and evoked (right) activity on all days recorded. (E) Density of functional ensemble networks had no significant difference between stable and transient ensembles during spontaneous (left) and evoked (right) activity in almost all days, with one exception from evoked activity on day 10. (F) Ensemble robustness was significantly higher in stable than transient ensembles during spontaneous activity (left) and evoked (right) activity in all days, with one exception from spontaneous activity on day 10. Mann–Whitney test: *p<0.05, **p<0.01, and ***p<0.001. See Figure 3—source data 1.

Figure 3—source data 1

Statistics of stable and transient ensembles.

https://cdn.elifesciences.org/articles/64449/elife-64449-fig3-data1-v2.docx
Figure 3—source data 2

Mice and recording days from Allen Brain Observatory Visual Coding dataset.

https://cdn.elifesciences.org/articles/64449/elife-64449-fig3-data2-v2.docx
Figure 3—source data 3

Mice from Churchland Lab dataset.

https://cdn.elifesciences.org/articles/64449/elife-64449-fig3-data3-v2.docx

To test if stable ensembles were merely an artifact of the analysis, we shuffled the neuronal activity of the session on days 1, 2, 10, and 43 or 46. We found 0.8 ± 0.1 (mean ± SEM) stable ensembles in the shuffled activity compared with 2.8 ± 0.1 (mean ± SEM) stable ensembles from original data (p=1 × 10–66; Figure 3—figure supplement 1A). The total time of stable ensemble activation during a single session of spontaneous or evoked activity was significantly higher than in shuffled data (75.1 ± 2.7 s and 14.4 ± 1.3 s, respectively, mean ± SEM, p=2 × 10–57; Figure 3—figure supplement 1B). In conclusion, stable ensembles could not be explained by chance, and they were not only reactivated during the following days but also their correlation with locomotion and tuning remained unchanged across days (Figure 3—figure supplements 2 and 3). This speaks to the likelihood that neuronal ensembles are true functional circuit elements and not an epiphenomenon of the population activity or a statistical artifact.

Stable and transient ensembles have similar functional structure but differ in robustness

We also investigated potential differences, other than stability, between stable and transient ensembles, finding similar number of neurons (Figure 3D) and network ensemble density (Figure 3E). We asked if varying the threshold to define stability could change this result. When we set the strictest threshold (Jaccard similarity = 1, i.e., all the neurons remain in the same ensemble), we counted significantly less stable ensembles (p<0.05; Figure 3—figure supplement 4A). At the same time, we did not find any significant addition of stable ensembles when we reduced the threshold, even to zero (Figure 3—figure supplement 4A). This indicates that the maximum number of stable ensembles could be defined by the minimum number of ensembles found between two sessions. In all cases, regardless of the Jaccard similarity threshold used, there were no significant differences in the functional structure of the ensembles (Figure 3—figure supplement 4B,C). This result suggests that the functional structure of the ensembles is constant and independent of its stability. However, ensemble robustness, defined here as the product of ensemble duration and the similarity of its activity (see Materials and methods), was significantly higher in stable than transient ensembles (Figure 3F). This result indicates that stable ensembles are more robust than transient ones during spontaneous or evoked activity (Figure 3—video 1 and Figure 3—video 2).

Stable ensembles are formed by densely connected neurons

Finally, we examined the neuronal identity and functional connectivity of stable ensembles (Figure 4A–D). During spontaneous or evoked activity, approximately 50% of neurons belonged to only one stable ensemble (‘single’ neurons), while less than 20% of the neurons belonged to more than one ensemble (‘shared’ neurons), and the rest of the neurons were not part of any stable ensemble (Figure 4E). We inquired what happened to individual neurons of stable ensembles across days. More than 60% of neurons of a stable ensemble observed on day 1 remained active on future sessions on days 1, 2, 10, and 43–46 during spontaneous and evoked activity (‘stable’ neurons, Figure 4F). The rest of the neurons (<40%) changed to another ensemble or stopped participating in detectable ensembles (‘lost’ neurons). Interestingly, in subsequent sessions, we found ‘new’ neurons joining stable ensembles in a similar proportion as lost neurons (lost and new neurons, Figure 4F). Even when we varied the threshold to define stable ensembles, all of them preserved more than 60% of their neurons across days (stable neurons, Figure 4—figure supplement 1B). Neither stable nor lost neurons specifically belonged to one or more than one ensemble (Figure 4G). However, functional connection density between stable neurons from the same ensemble (0.71 ± 0.01, mean ± SEM) was significantly higher than density from lost neurons during spontaneous or evoked activity (0.35 ± 0.02, mean ± SEM, p=7 × 10–55, Figure 4H). Therefore, weak functional connectivity between lost neurons could explain why they are transient, and high functional connectivity indicates possible lasting stability.

Figure 4 with 3 supplements see all
Long-term stability of spontaneous and evoked ensembles.

(A) Example of spontaneous ensemble activity. Neurons are sorted based on their ensemble identity (right). (B) Example of evoked ensemble activity. Neurons are sorted based on their ensemble identity, and it is indicated if they were tuned to the visual stimulation (right). The first ~500 ms (of the 2 s) of every visual stimulation (50 per session) are marked at the bottom of the raster activity. Note that there is a particular stable neuronal ensemble (blue ensemble) mainly evoked at the onset of the visual stimulation. (C) Neuronal ensembles across days (identified independently). (Top) Neuronal identity sorted as in (A). (Bottom) Colors indicate the ensemble to which neurons belonged, white color indicates no participation in any stable ensemble. Scale bar: 50 µm. (D) Neuronal ensembles across days (identified independently). (Top) Neuronal identity sorted as in (B). (Bottom) Colors indicate the ensemble to which neurons belonged, white color indicates no participation in any stable ensemble, gray color indicates participation in more than one stable ensemble. Scale bar: 50 µm. (E) Fraction of neurons during spontaneous (top) or evoked (bottom) activity across days which participated in two or more stable ensembles (shared), only one (single), and without participation in any stable ensemble. (F) Fraction of neurons during spontaneous (top) and evoked (bottom) activity across which remained in the same ensemble (stable), changed their ensemble or stopped participating (lost), and new neurons. (G) Fraction of stable neurons during spontaneous (top) and evoked (bottom) activity from days 1, 2, 10, and 43–46 which participated in one stable ensemble (single) or more (shared). (H) Network density within stable ensembles during spontaneous (top) and evoked (bottom) activity was significantly higher in stable neurons than lost neurons (p<0.01). Density was computed from functional connectivity analyzed on day 1. Data are presented as mean ± SEM. Mann–Whitney test: **p<0.01 and ***p<0.001. See Figure 4—source data 1.

Ensemble stability is detectable using different methods and datasets

To confirm these results, we performed the same analysis pipeline (Figure 3—figure supplement 5; Figure 4—figure supplement 2) with our dataset by modifying the way to extract the ensembles and also with two publicly available datasets from the Allen Brain Observatory Visual Coding (de Vries et al., 2020) and the Churchland Lab (Musall et al., 2019). In our study, we used all single sessions (‘main,’ three sessions/day/condition with a selection of vectors), and the results were similar when we used only one session per day (‘single,’ one session/day/condition), all the raster without vector selection (‘all vectors’), and the significant population coactivations (‘coactivity peaks’; see Materials and methods). The properties of stable and transient ensembles as the neurons/ensemble and connection density remained similar (Figure 3—figure supplement 5B,C), but ensemble robustness values showed variability between methods (Figure 3—figure supplement 5D). However, the vector selection method used differentiated significantly between stable and transient ensembles, especially during spontaneous activity. In agreement with these conclusions, we found similar results in the Allen Brain Institute dataset during visually evoked activity but a lower number of ensembles and network ensemble density during spontaneous activity (Figure 3—figure supplement 5A,C). Although the neurons/ensemble increased after 1 week, network ensemble density and robustness values were consistent with our dataset (Figure 3—figure supplement 5B,C). Furthermore, there were no significant differences when we analyzed the Churchland Lab dataset, where evoked activity was relevant for performing a task (Musall et al., 2019; Figure 3—figure supplement 5 and Figure 4—figure supplement 2). In all methods and datasets, the number of stable neurons within ensembles was above 60% (Figure 4—figure supplement 2B), the connection density of the stable neurons was above 0.5, which was significantly greater than the connection density of lost neurons (Figure 4—figure supplement 2D). Finally, we also observed persistence across days of the temporal sequences of neuronal activations using the seqNMF toolbox (Mackevicius et al., 2019; Figure 4—figure supplement 3), which could be used in future studies to analyze the dynamic of the temporal structure within ensembles. It should be noted that results were similar between GCaMP6s and GCaMP6f mice, consistent with previous studies (Musall et al., 2019). We analyzed four GCaMP6s and two GCaMP6f mice in our dataset, seven GCaMP6f mice from the Allen Brain Institute dataset, and four GCaMP6f mice from Churchland Lab dataset. In summary, we detected long-term ensemble stability regardless of the method or dataset used.

Discussion

Starting with Wiesel and Hubel’s landmark studies in the 1960s, the plasticity or stability of neuronal activity of the visual cortex has been explored through single-neuron measurements such as receptive field tuning (Wiesel and Hubel, 1963; Wandell and Smirnakis, 2009; Clopath et al., 2017). Recent longitudinal studies have shown that single-neuron tuning is stable for up to 2 weeks (Ranson, 2017; Jeon et al., 2018). Moreover, single-neuron selectivity is enhanced after learning (Poort et al., 2015; Henschke et al., 2020). Here, we extend to the microcircuit level these single-neuron longitudinal studies by reporting robust stability in population activity (Shepherd and Grillner, 2010; Yuste, 2015; Bargas and Pérez-Ortega, 2017). We compared, across weeks, microcircuit properties such as functional connectivity, neuronal ensembles, and network topology. In contrast to previous studies, we also measured stability in the absence of visual stimulation. Somewhat surprisingly, we did not find any relevant differences in long-term stability between spontaneous and visually evoked activity. Indeed, we found that ensembles during spontaneous activity were also active during visually evoked activity and vice versa (see ‘S day 1 vs. E day n | E day 1 vs. S day n‘ in Figure 3—figure supplement 5 and Figure 4—figure supplement 2). In fact, more than 60% of neurons in spontaneous ensembles on day 1 were found to be tuned to a specific stimulus on the following weeks. This result is consistent with the hypothesis that sensory stimuli reactivate existing ensembles, which are already present in the spontaneous activity (Miller et al., 2014).

We could not track the activity of every single neuron over weeks with sufficient signal to noise (PSNR >18 dB, Figure 1H) since image quality decreased over days (Figure 1G). This could be due to the repeated experimental procedures on the same cortical location, decreased transgene expression, laser photobleaching, or surgical-attachment-related microtraumas (Figure 1—figure supplement 3). Nevertheless, our method was sufficient to capture similar average activity from the neurons with sufficient levels of signal to noise over weeks (Figure 1I), agreeing with the hypothesis that cortical circuits maintain a basic homeostatic activity level, even in spite of perturbations (Mrsic-Flogel et al., 2007; Lütcke et al., 2013; Hengen et al., 2013; Clopath et al., 2017). However, we found that single-neuron responses were variable during the same repeated stimulation (Montijn et al., 2016; Stringer et al., 2019a). Indeed, the chance of finding any given neuron also active in a future session (‘common neurons’) was less than 60%, even 5 min later (Figure 1K; Tolias et al., 2007). This result is consistent with transient silencing of neurons (Prsa et al., 2017), which could be a neuronal correlate of the learning enhancement in deep neural networks (Srivastava et al., 2014; Rule and Harvey, 2019). We also found a continuous decrease in ‘common’ neurons, which could be explained by the loss of neurons (Figure 1H) together with small z-plane displacement across days (Figure 1—figure supplement 2A). If one could maintain imaging quality and focus, we would expect a consistent number of common neurons over weeks, regardless of the time recorded.

Multiplane two-photon calcium imaging allowed tracking the same neurons and identifying neuronal ensembles across days. Stable ensembles were preserved across weeks and 68% of their neurons continued to be active, while the rest of the neurons were replaced by new ones (Figure 4F). The consistent number of neurons preserving their interactions within ensembles across weeks does not support a representational drift in cortical responses at the single neuronal level (Driscoll et al., 2017, Rule and Harvey, 2019; Deitch et al., 2020). In fact, this could be precisely one of the functions of ensembles: to maintain a stable functional state in the midst of an ongoing homeostatic replacement of the activity of individual neuronal elements (Mrsic-Flogel et al., 2007; Lütcke et al., 2013; Hengen et al., 2013; Clopath et al., 2017). The stability of ensembles could be based on the stability of dendritic spines (Yuste and Bonhoeffer, 2001). The weak connectivity of flexible neurons could be mediated by small spines, which appear and disappear over days (Holtmaat et al., 2005), while the high connectivity of stable neurons could be maintained by large spines, which last for months (Holtmaat et al., 2005; Grutzendler et al., 2002).

One limitation of this study is that we did not image the activity of GABAergic interneurons, which are at least 28 different types based on the morphoelectric and transcriptomic classifications (Yuste et al., 2020; Gouwens et al., 2020; Yao et al., 2021). Parvalbumin (PV) interneurons could stabilize the cortical circuit while somatostatin (SOM) and vasoactive intestinal peptide (VIP) interneurons could modulate the gain of pyramidal neurons (Bos et al., 2020; Millman et al., 2020). Moreover, single-neuron statistics showed that PVs in the visual cortex undergo faster homeostasis (Hengen et al., 2013) and are more stable than pyramidal cells (Ranson, 2017). Further studies are needed to evaluate the stability within interneuron interactions and the interactions between interneurons and pyramidal neurons.

In spite of the incomplete sampling of circuit activity over time and potential alterations on the circuit due to repeated experimental procedures, our results indicate that ensembles can be robust and last several weeks. While there is a significant state of flux in cortical activity at any given moment, there is a subset of neurons that remain active through weeks and form neuronal ensembles. The stability of ensembles that we report could be an underestimate since we only measured snapshots of cortical activity. These stable ensembles, which also have some rotation of their individual neuronal components, appear anchored by core neurons. Specifically, 68% of neurons remain consistently active within ensembles up to 46 days later (Figure 4F) and had stronger functional connectivity (Figure 4H). This analysis was robust, even after changing the threshold to define stable ensembles (Figure 4—figure supplement 1B-D). These core neurons could be ‘anchor’ cells, which would maintain stable neural representations and help to maintain them after perturbations (Rose et al., 2016; Clopath et al., 2017). At the same time, these core neurons could be pattern completion cells, capable of triggering neuronal ensembles (Carrillo-Reid et al., 2016; Carrillo-Reid et al., 2019). The stronger internal functional connectivity of stable neurons could be mediated by short- or long-term synaptic plasticity (Carrillo-Reid et al., 2015a; Hoshiba et al., 2017) and may underlie the representation of memories. Since neuronal ensembles in the visual cortex have been associated with perceptual states or memories (Carrillo-Reid et al., 2019; Marshel et al., 2019), stable neuronal ensembles could represent long-term memories and transient ensembles could illustrate the emergence of new memories or the degradation of existing ones. Future experiments, perhaps using holographic optogenetics (Yang et al., 2018) during memory tasks, could test this hypothesis and explore the potential link between the stability of ensembles and the persistence of memories.

Materials and methods

Key resources table
Reagent type (species) or resourceDesignationSource or referenceIdentifiersAdditional information
Strain, strain background (mice)Slc17a7-IRES2-CreJAX stock # 023527VGlut1
Strain, strain background (mice)TIT2L-GC6s-ICL-tTA2JAX stock # 031562TIGRE2.0 Ai162
Strain, strain background (mice)TIT2L-GC6f-ICL-tTA2JAX stock # 030328TIGRE2.0 Ai148
Software, algorithmDrifting gratings generator for visual stimulationThis paperhttps://www.mathworks.com/matlabcentral/fileexchange/78670-drifting-gratings-generator-for-visual-stimulation
Software, algorithmETL controller for volumetric imagingThis paperhttps://www.mathworks.com/matlabcentral/fileexchange/78245-etl-controller-for-volumetric-imaging
Software, algorithmCatrex GUIThis paperhttps://github.com/PerezOrtegaJ/Catrex_GUI
(copy archived at swh:1:rev:2ffc0749535be40ca2331f4c969a82fbfff102d4)
Software, algorithmNeuronal Ensemble AnalysisThis paperhttps://github.com/PerezOrtegaJ/Neural_Ensemble_Analysis
(copy archived at swh:1:rev:9d37fd031dfbdb4eb69faa449d0a6416267a7d4f)

Animals

Experiments were performed on transgenic mice Vglut1 (Slc17a7-IRES2-Cre, JAX stock # 023527) crossed with TIGRE2.0 Ai162 (TIT2L-GC6s-ICL-tTA2, JAX stock # 031562) or Ai148 (TIT2L-GC6f-ICL-tTA2, JAX stock # 030328) maintained in C57BL/6J congenic background. Mice were housed on a 12 hr light-dark cycle with food and water ad libitum. Head-plate procedure and a cranial window were executed after 50 days of age. Mice’s health was checked daily. All experimental procedures were carried out in accordance with the US National Institutes of Health and Columbia University Institutional Animal Care and Use Committee.

Head-plate procedure and cranial window

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Adult transgenic mice GCaMP6s (n = 4) and GCaMP6f (n = 2, none had aberrant activity, Figure 1—figure supplements 2 and 3; Daigle et al., 2018) were anesthetized with isoflurane (1.5–2%). Body temperature was maintained at 37°C with a heating pad and eyes were moisturized with eye ointment. Dexamethasone sodium phosphate (0.6 mg/kg) and enrofloxacin (5 mg/kg) were administered subcutaneously. Carprofen (5 mg/kg) was administered intraperitoneally. A custom-designed titanium head-plate was attached to the skull using dental cement. Then, a craniotomy was made of 3 mm in diameter with a center at 2.1 mm lateral and 3.4 mm posterior from bregma. A 3 mm circular coverslip was implanted and sealed using cyanoacrylate and cement. After surgery, animals received carprofen injections for 2 days as postoperative pain medication. Mice were allowed to recover for 5 days with food and water ad libitum.

Visual stimulation

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Visual stimuli were generated using a custom-made app on MATLAB (Pérez-Ortega, 2020a) displaying on an LCD monitor positioned 15 cm from the right eye at 45° to the long axis of the animal. The red and green channels of the monitor were disabled to avoid light contamination in the imaging photomultiplier (PMT), only the blue channel was enabled (Kuznetsova et al., 2021). We used two protocols to display in the monitor. The first was in the absence of visual stimulation, the monitor was displaying a static blue screen, and we used it to record spontaneous activity during 5 min per session. The second protocol was for visual stimulation consisting of full sinusoidal gratings (100% contrast, 0.13 cycles/deg, 5 cycles/s) drifting in a single direction per mouse (0° or 270°) presented for 2 s, followed by a random amount between 1 and 5 s of mean luminescence. The visual stimulus is presented 50 times during 5 min per session. We performed three consecutive sessions (5 min apart) per protocol per day of the experiment. See Figure 1—source data 1 for detailed sessions recorded per mouse.

Multiplane two-photon calcium imaging

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Imaging experiments were performed from 20 to 150 days after the head-plate procedure. Each mouse was placed on a treadmill with its head fixed under the two-photon microscope (Ultima IV, Bruker). Animals were acclimated to the head restraint for periods between 5 and 15 min for at least 2 days and exposed to visual stimulation sessions before the recordings presented here. The imaging setup was completely enclosed with blackout fabric to avoid light contamination leaking into the PMT. An imaging laser (Ti:sapphire, λ = 920 nm, Chameleon Ultra II, Coherent) was used to excite a genetically encoded calcium indicator (GCaMP6s or GCaMP6f). The laser beam on the sample (30–60 mW) was controlled by a high-speed resonant galvanometer scanning an XY plane (256 × 256 pixels) at 17.7 ms (frame period) covering a field of view of 312 × 312 µm using a 25× objective (NA 1.05, XLPlan N, Olympus). An electrically tunable lens (ETL) was used to change the focus (z-axis) during the recording. We recorded consecutively three planes at different depths (–5, 0, and 5 µm from the reference z-axis) waiting 9.3 ms between planes for ETL to stabilize the focus. Thus, we collected three frames, one per depth, every 81 ms for 5 min (single session, 3704 frames per plane). Imaging was controlled by Prairie View and ETL was synchronized using a DAQ (USB-6008, NI) controlled by a custom-made app on MATLAB (Pérez-Ortega, 2020b).

Recording same neuronal region through days

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On the first day of the experiment, we recorded the vascularization of the pia at 10× and 25× using bright-light microscopy. We fixed the depth to 140 µm from pia to record a reference image (calcium imaging) and a second reference image of the center of the field of view using an extra 4× optical zoom (day 1 in Figure 1G). We carefully preserved unchanged the position of the microscope and the base we placed the mice. For the following days of recording, we looked for matching the reference image of vascularization at 10×, then 25×. After that, we looked at 140 µm depth from pia trying to match the reference image on the x- and y-axis, then we used a 4× extra optical zoom to finely match the second reference image on the z-axis (days 2, 10, and 43 or 46 in Figure 1G). We performed multiplane imaging to record three planes – one reference plane, one 5 µm above, and one 5 µm below – in order to amend the potential tilt or some z-displacement. We evaluated the z-plane where neuronal position maximally matched between two sessions and estimated an overall z-displacement of 2.5 ± 0.2, 3.3 ± 0.2, and 3.8 ± 0.1 µm (mean ± SEM on days 2, 10, and 43–46, respectively). The z-displacement difference between days 2, 10, and 43–46 was not significant (p2vs10 = 0.83, p2vs43-46 = 0.67 and p10vs43-46 = 0.98; Kruskal–Wallis test with post hoc Tukey–Kramer). In the end, we extracted the maximum intensity projection from the three planes resulting in a single video for each session (Figure 1D).

Neuronal activity extraction

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We used a custom-made graphical user interface (GUI) on MATLAB (Pérez-Ortega, 2020c) to extract the binary raster activity from every single session video (5 min, 3704 frames). First, we performed a non-rigid motion correction taking as a reference the mean of the 185 frames (5%) with fewer motion artifacts. Then, we searched the ROIs with a modified version of the Suite2P algorithm (Pachitariu et al., 2016, Figure 1E). ROIs were preserved if they fulfill the following criteria (fixing radius to 4 µm): 0.5 * π * radius^2 < area < 4 * π * radius^2; roundness >0.2; perimeter <3.5 * π * radius; eccentricity <0.9 and overlapping <60%. Calcium signal from each ROI was extracted measuring the changes in fluorescence with respect to its local neuropil (Fraw — Fn)/Fn, where Fraw is the signal from the ROI and Fn is the signal of its local neuropil 10 times ROI radius. ROI local neuropil is not including the signal from ROIs if presented within the area. Then we computed the PSNR = 20 · log10(max(Fraw — Fn)/σn), where max represents a maximum function and σn represents the standard deviation of the local neuropil. We evaluated the ROIs again keeping them if PSNR >18 dB. Then we smoothed the calcium signal with a 1 s window average to perform a spike inference using the foopsi algorithm (Friedrich and Paninski, 2016). We binarized the spike inference signal, placing 1 if there were spikes inferred and 0 if not. We placed all binarized signals from every ROI in a N × F raster matrix, where N is the number of active neurons and F the number of frames. This matrix is visualized as a raster plot, where the ones in the matrix are the dots representing the active frames of the neurons (Figure 1F).

Tracking neurons across days

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We computed a rigid and then a non-rigid motion correction between the binary image of the ROIs shape between a single session of day 1 and a single session from days 1, 2, 10, 43, or 46 (Figure 1J). Then, we looked for the intersection (in pixels) between ROIs of the neurons from two sessions (intersection >0.5 * π * radius^2) and evaluate the Euclidean distance between centroids of the ROIs intersected keeping it if the distance < radius. We used the raster matrix only with the tracked neurons between sessions.

Identification of neuronal ensembles based on functional connectivity

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To analyze neuronal ensembles from raster activity, we used a custom-made GUI on MATLAB (Pérez-Ortega, 2020d) Functional connectivity represents the significant coactivity between every pair of neurons from a raster matrix. The number of coactivations Coab between neuron a and b was computed counting in how many single frames were both neurons simultaneously active. To identify significance, we generated 1000 surrogates of neurons a and b by random circular shifting their activity in time to disrupt their temporal dependency. We counted the number of surrogate coactivations Sab,i in each iteration i, building a cumulative distribution of Sab selecting a threshold Tab of coactivations at 95%. If the actual number of coactivations Coab is above threshold Tab, we put a functional connection between neuron a and b (Figure 2C). Doing this with every pair of neurons, we got a functional neuronal network, where every node is a neuron and every link represents a significant coactivity between them (Figure 2B). We used the functional connectivity to rebuild the raster matrix to keep the significant coactivity of the neurons. To do so, we identified the active neurons of every single frame and looked at their functional connectivity; if a neuron has no connection, its activity was removed from that frame. At the end, we also removed the frames with less than three coactive neurons (Figure 2D). Then we computed the Jaccard similarity between all single frames (column vectors) of the rebuilt raster matrix. A hierarchical clustering tree with a single linkage was obtained to identify the more similar vectors by keeping the branch with more than 2/3 of Jaccard similarity (Figure 2E). Using this threshold, we clustered most similar coactivations and filtered non-similar and infrequent coactivations, but similar results were obtained without selecting vectors (all vectors in Figure 3—figure supplement 5; Figure 4—figure supplement 2). With the more similar vectors, we performed hierarchical clustering with Ward linkage and grouped based on a contrast index (Beggs and Plenz, 2004). Each group of column vectors is the activity of the neuronal ensemble (Figure 2F), that is, the raster matrix Ej of an ensemble j of size N × Fj, where N is the number of neurons and Fj is the number of frames where the ensemble j was active. The time window reported for finding maximum functional coactivity is between 20 and 25 ms (Buzsáki, 2010; Juárez-Vidales et al., 2021), so one single frame period (81 ms) in our recordings is enough to find ensemble coactivations.

Detection of coactivity peaks to identify ensembles

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Alternatively, we analyzed our dataset using a method to extract neuronal ensembles based on significant population coactivity (Pérez-Ortega et al., 2016). Briefly, we obtained a 1 × F vector, where F is the number of frames, by summing the coactive neurons from the raster matrix E. Then, we perform 1000 surrogated raster matrices by randomly circular shifting in time the activity of every single neuron and computing the coactivity given by chance. We determined a significant coactivity threshold (p<0.05) from surrogated coactivity, and the vectors above this threshold were clustered to extract neuronal ensembles.

Demixing neuronal ensemble identity

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The neuronal ensemble activity Ej was used to identify the participation of each neuron in ensemble j. We computed the functional connectivity similarly as described above, but incorporating the correlation of the neurons between the times where the ensemble was active. To do so, we got a binary vector Vj representing the times where the ensemble j was active (1) or not (0). Vector Vj was of size 1 × F, where F is the number of frames of the session. A Pearson correlation coefficient Pj,a between vector Vj and the activity of neuron a was computed. Then we got an ensemble weight Wj,ab between neurons a and b in ensemble j, which integrates their correlation with the ensemble j and their number of coactivations as follows: Wj,ab = Pj,a · Pj,b · Coab. To identify significance, we generated 1000 surrogates of neurons a and b shuffling their activity as described before, and assigning randomly a value from the correlation with the ensemble j (Pj). Then we compute the surrogate weight SWj,ab,i in each iteration i, building a cumulative distribution of SWj,ab selecting a threshold TWj,ab of coactivations at 95%. If the actual ensemble weight SWj,ab is above threshold TWj,ab, we put a functional connection between neurons a and b. A neuron is considered to be part of an ensemble if it had at least one single functional connection (Figure 2G). A neuron could be part of more than one neuronal ensemble (shared), only one ensemble (single), or in any ensemble (not participant).

Comparing neuronal ensembles between following sessions

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Taking the first session on day 1 and a second session from days 1, 2, 10, 43, or 46, we got the raster matrix from each session with only common neurons (same active neurons in both sessions). Neuronal ensembles were extracted from each raster matrix independently. We define a stable ensemble if 50% or more neurons matched between an ensemble j found in a first session and a putative same ensemble j′ found in a second session. If there is no such match, we called it a transient ensemble. We compute the Jaccard similarity between ensembles j and j′, where a value of 0 means that the neurons from j and j′ are completely different, and a value of 1 means that neurons from j and j′ are exactly the same. We used the value of 1/3 Jaccard similarity as a threshold to keep at least 50% of the same neurons.

Ensemble measures

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Ensemble network density, a fraction of present functional connections to possible connections within an ensemble. Ensemble robustness, we introduced here as robustness = similarity · activity, where similarity is the average of the Jaccard similarity between every pair of column vectors of the ensemble matrix raster Ej, and activity is the fraction of ensemble active frames to the total frames of the session. The higher the value, the higher the robustness. We computed ensemble robustness for every single session, so it would not be expected beforehand if the ensemble would be stable or not. Stability of neurons: given a stable ensemble, a ‘stable’ neuron participated during the first session on day 1 and a second session on the following days. A ‘lost’ neuron participated only in the first session but not in a second session, and a ‘new’ neuron did not participate in the first session but participate in a second session. The fraction is based on total neurons in an ensemble from day 1. Promiscuity of neurons: ‘shared’ neurons is the fraction of neurons participating in more than one ensemble; ‘single’ neurons participate in only one ensemble; and ‘not participant’ neurons do not belong to any ensemble. Tuned neurons: we consider a tuned neuron if its number of active frames during visual stimulation was significantly higher than its number of active frames during periods with no visual stimulation (p<0.05, t-test).

Allen Brain Observatory Visual Coding dataset curation

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We found seven mice recorded similarly to our settings: Slc17a7-IRES2-Cre::Camk2a-tTA::Ai93 (Vglut1), VISp structure, and 175 µm depth. The complete methodology can be found in the resource or white paper (de Vries et al., 2020; https://observatory.brain-map.org). In brief, two-photon calcium imaging of pyramidal cells in layer 2/3 of the visual cortex from GCaMP6f adult mice was performed in the same region for three different days. Not all mice were recorded in the same sequence of days, so we grouped on days 1, 2–5, and 6–8 (Figure 3—source data 2). We downloaded the motion-corrected two-photon calcium fluorescence movies (https://console.aws.amazon.com/s3/buckets/allen-brain-observatory/) and adapted the spatial and temporal resolution to match our movie features. We analyzed the periods of spontaneous activity and visually evoked activity by a natural movie (Figure 3—figure supplement 5 and Figure 4—figure supplement 2). We did not analyze the evoked activity by gratings since they were presented in only one session.

Churchland Lab dataset curation

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The experiments were recording similarly to our dataset setting: Ai93::Emx-Cre::LSL-tTA::CaMK2α-tTA, V1 structure, and 150–450 µm depth. The detailed acquisition of the experiments could be found in Musall et al., 2019. In brief, two-photon calcium imaging of pyramidal cells in layer 2/3 of the visual cortex from GCaMP6f adult mice was performed during a visual decision-making task. This dataset had experiments from 10 mice, but only 4 were recorded on V1. Experiments were performed for several days but in different regions, so we analyzed the ensemble stability within 1 day (Figure 3—source data 3). We downloaded the ‘data.mat’ files (http://labshare.cshl.edu/shares/library/repository/38599/) and adapted the temporal resolution to match ours. We created two sessions for each mouse to compare during the same day, and each session was conformed by ~40 continuous trials (~5 min). We analyzed them and compared the ensemble properties during the evoked activity on day 1 (Figure 3—figure supplement 5 and Figure 4—figure supplement 2).

Finding neuronal sequences using the seqNMF toolbox

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We used the seqNMF toolbox (https://github.com/FeeLab/seqNMF; Mackevicius et al., 2019) to detect possible neuronal patterns of sequential activation as an alternative to our method, which has a constraint to detect simultaneous neuronal activation (coactivation). We used the common neurons across days during spontaneous and evoked activity to find temporal sequences within windows of ~1 s (L = 12), a regularization parameter lambda = 0.005, and 50 iterations (Figure 4—figure supplement 3).

Data availability

Data analyzed during this study are included in the manuscript and supporting files. Links to download the code developed in MATLAB are included in Methods. Data can also be found on Dryad, under the https://doi.org/10.5061/dryad.cfxpnvx5m.

The following data sets were generated
The following previously published data sets were used
    1. Musall S
    2. Kaufman MT
    3. Juavinett AL
    4. Gluf S
    5. Churchland AK
    (2019) Churchland Lab
    ID 38599. Single-trial neural dynamics are dominated by richly varied movements.

References

    1. Clopath C
    2. Bonhoeffer T
    3. Hübener M
    4. Rose T
    (2017) Variance and invariance of neuronal long-term representations
    Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 372:161.
    https://doi.org/10.1098/rstb.2016.0161
  1. Conference
    1. Friedrich J
    2. Paninski L
    (2016)
    Fast active set methods for online spike inference from calcium imaging
    Advances in Neural Information Processing Systems. pp. 1992–2000.
    1. Srivastava N
    2. Hinton G
    3. Krizhevsky A
    4. Sutskever I
    5. Salakhutdinov R
    (2014)
    Dropout: A simple way to prevent neural networks from overfitting
    Journal of Machine Learning Research 15:1929–1958.

Decision letter

  1. Timothy O'Leary
    Reviewing Editor; University of Cambridge, United Kingdom
  2. Tirin Moore
    Senior Editor; Stanford University, United States
  3. Carsen Stringer
    Reviewer; Howard Hughes Medical Institute, United States
  4. Laura N Driscoll
    Reviewer; Stanford University, United States

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Acceptance summary:

Cortical circuits are subject to ongoing plasticity during an animal's lifetime that has the potential to alter responses to external stimuli over days and weeks. Pérez-Ortega and colleagues examines whether coincident firing of neurons in mouse visual cortex is preserved over a long timescale (one month) in response to repeated stimuli. The authors find that in spite of significant variability in the responsiveness at the population level, subsets of identified neurons maintain coordinated firing. Such cell assemblies may provide a stable substrate for representing visual stimuli.

Decision letter after peer review:

Thank you for submitting your article "Long-term stability of cortical ensembles" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Tirin Moore as the Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Carsen Stringer (Reviewer #1); Laura N Driscoll (Reviewer #2).

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

This work examines whether coincident firing of neurons in visual cortex is preserved over a long timescale (one month) which is important because it provides insight into the stability and plasticity of neural circuits and visual representations. The authors find that subsets of identified neurons maintain coordinated firing despite some degree of flux in the firin activity across the population.

All reviewers agreed that the question is important but found the analysis lacked depth and there were some technical issues in the experiments that should be addressed with a fuller discussion and potentially additional analysis to eliminate confounds/artefacts. In general, and in light of earlier work (some of which is not cited) the conclusions need to be more circumspect. Specifically:

– There were concerns about movement/loss of cells/calcium indicator artefacts over this long imaging period that should be accounted for more rigorously.

– The analysis applies a somewhat arbitrary criterion for stability (50% of cells remain in responsive in an assembly). This threshold should be systematically explored and justified more carefully.

– The wider literature on this topic should be more thoroughly cited, limitations of the study should be transparently laid out, claims about the overall stability found in this population response and its relevance to memories and behaviour should be moderated in line with the comments below.

Reviewer #1 (Recommendations for the authors):

Perez-Ortega and colleagues performed rigorous experiments to determine if the activity of neurons in visual cortex is similar across days, in particular comparing spontaneous activity in the absence of visual stimuli across days, which was previously not examined to my knowledge. The paper claims that evoked ensembles are more stable than spontaneous ensembles, but more convincing quantitative analyses are required to support these claims.

– There is only one mention of prior work with multi-day imaging in visual cortex (Ranson 2017). Another related study to cite and compare your results to would be Jeon, …, Kuhlman 2018 (and I think a comment about how similar/different your results are from this study + Ranson would be useful for the reader). I would also recommend mentioning that there are studies that have observed differences in evoked activity across learning in V1 (e.g. Poort, Khan et al., 2015; Henschke, Dylda et al., 2020). Do you think there was adaptation across days to the stimulus that you repeated?

– Some GCaMP6f mice have aberrant cortical activity (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5604087/). In the raw data (Figure 1F) it doesn't look present, but it would be useful to show more time and sort the neurons by their first PC weights perhaps to see the activity structure.

– The approach of 3 plane imaging taking the maximum projection seems useful for tracking cells across days. There is a claim that some cells are no longer found / no longer active. Based on Figure 1G it appears there may have been some Z-movement from day 10 to day 46. This Z movement may explain some of the lost active cells. As a sanity check I would recommend plotting the Z-plane on which the cells were maximally active on day 1 vs the Z-plane on which the cells were maximally active on day n.

– There is an emphasis on analyzing the data as ensembles but I think this may be missing other slow, gradual changes. The definition of stable is at least 50% of neurons were preserved across days. However, the fitting procedure of finding ensembles may produce different ensembles even if those neurons are still correlated to each other. I would recommend two possible additional analyses: (1) compare the correlation matrices for common neurons across days (unless there are too few neurons for this); (2) look at changes in single neuron statistics across days. For (2) this may include reliability of neural responses to the visual stimuli, the weights of the neuron onto the first principal component of spontaneous activity, or the correlation of a neuron with running speed. I think these results may solidify your ensemble result (evoked-related statistics change less across time).Reviewer #2 (Recommendations for the authors):

Overall I think the authors collected an interesting dataset. Analyses should be adjusted to include all cells rather than sub-selecting for stability. Additionally, the language needs to be adjusted to better reflect the data. I wish there was any behavioral data included, but if the authors compare their data to publicly available data in V1 for a single recording session during a visually guided task, these concerns could be quelled a bit. Reject, but I would reconsider if the following suggested changes are made.

1. In general the language of this paper and title seem to mismatch the results. The fraction of cells that were 'stable' as the authors say on line 112 was very small, however the authors focus extensively on this small subset for the majority of analyses in the paper. Why ignore the bulk of data (line 119)? What happens if you repeat the same analysis and keep all cells in the dataset? The general language around stability of neural ensembles should be adjusted to better reflect the data (ex: lines 157, 225).

2. There are claims in this paper about how ensembles 'implement long-term memories' in the introduction and conclusion and yet the authors never link the activity of ensembles to any behavioral or stimulus dependent feature. This language reaches far beyond the evidence provided in this paper. The introduction could provide some better framing for expectations of stability vs. drift in neural activity rather than focus on the link between ensembles and memory given that there isn't much focus on the ensembles' contribution to memory throughout. For example, the last sentence of the paper is not supported by data in the paper. Where is the link between ensembles and memory in the data? What is the evidence that transient ensembles are related to new or degraded memories? This reads as though it was the authors' hypothesis before doing the experiments and was not adjusted in light of the results.

3. There is no discussion around the alternative to stability of neuronal ensembles. What are the current theories about representational drift? For example, in Line 34 the authors present an expectation for stability without any reasoning for why there need not be stability. This lack of framing makes their job of explaining results in line 217 more difficult. There is a possibility that the most stable cells aren't more important – what is the evidence that they are? Does an ensemble need a core? Would be interesting to include some discussion on the possibility of a drifting readout (Line 223). [https://doi.org/10.1016/j.conb.2019.08.005]

4. How do activations in V1 in this dataset compare to other data collected from V1 while the animal is performing a task (where for example the angle of the gradings is relevant to how the mouse should respond)? I would be interested to know if the authors compared statistics of their ensembles to publicly available data recorded in V1 during a visually guided behavior. Are the ensembles tuned to anything in particular? Could they be related to movement? [http://repository.cshl.edu/id/eprint/38599/]

5. The authors provide some hypotheses as to why fewer cells are active in the later imaging sessions (dead/dying cells?). This is worrisome in regards to how much it might have affected the imaged area's biology. One alternative hypothesis is that the animal is more familiar with the environment/ not running as much etc. Have the authors collected any behavioral data to compare over time?

6. How much do the results change when you vary the 50% threshold of preserved neurons within an ensemble (Line 146)? Does it make sense to call an ensemble stable when 50% of the cells change? Especially given that the cells analyzed as contributing to an ensemble are already sub-selected to be within the small population of stable cells (Line 119)?

7. Cells are referred to as 'stable' when they're active on 3 different sessions that are separated in time. However, the authors find a smaller number of cells are stable over extended time (43-46 days later). If we extrapolate this over more time, would we expect these cells to continue to be stable? Given these concerns, it might make more sense to qualify the language around stability by the timespan over which these cells were studied.

8. Filtering frames to only coactive neurons for ensemble identification seems strange to me. Authors may be overestimating the extent of coactivation. What happens when you don't do this? How much do the results change when you don't subselect for Jaccard similarity? I would be interested to see how the results vary as you vary this threshold (Line 136).

9. The term 'evoked activity' is misleading because the authors don't link these activations to the visual stimulus. There's no task, so the mice could be paying little attention to the stimulus. Should we really consider this activity to be visually driven? Could the authors provide any evidence of this?

10. A method like seqNMF could reveal ensembles that are offset in time. This looser temporal constraint could potentially reveal more structure. This should be run on the entire dataset (without stability sub-selection). I suggest this as a potential alternative or supplement to the method described by the authors. [https://elifesciences.org/articles/38471]Reviewer #3 (Recommendations for the authors):

Neuronal ensembles have been shown by this lab and others to constitute one basic functional unit for the representation of information in cortical circuits. It is therefore important to determine how stable these blocks of representation might be. If these ensembles were preserved across time and sensory stimuli, this would indicate a significant degree of structure underlying cortical representations. In a first attempt to address these important issues, this manuscript analyzes the long-term stability of ensembles of coactive neurons in the layer 2/3 of mouse visual cortex across several days. Ensembles were recorded during periods of spontaneous activity as well as during visual stimulation (evoked). For this, the authors record spontaneous and evoked activity using two-photon calcium imaging one, ten and 40 days after the first recording session. In order to maximize overlap between successive imaging sessions, the authors record three planes separated by 5 microns almost simultaneously (9ms interval) using an electrically-tunable lens. They show that ensembles extracted during visual stimulation periods are more stable on days 2 and 10 than those computed during spontaneous activity. Stable ensembles display a higher "robustness" (a parameter that quantifies how many times a given ensemble is repeated and how similar these repeats are). Neurons displaying stable membership are more functionally connected than unstable ones. It is concluded that such observed stability of spontaneous and evoked ensembles across weeks could provide a mechanism for memories. Long-term calcium imaging within the same population of neurons is a real challenge that the authors seem to overcome in the study. The conclusions are important, my main concern relates to the number of experiments and analyses supporting these findings as detailed below.

Number of experiments and statistics: According to Table 1, two mice with GCamP6f have been through the complete imaging protocol (days 1,2, 10 and 43) but none with the 6s, since 3 missed the intermediate measure (day 10) and one the last point (day 40+). Therefore five mice have been recorded over weeks with two different indicators, but only two were sampled on day 10. One mouse was only recorded until day 10. Altogether, this is quite a low sampling, but the experiments are certainly difficult. However, the total number of experiments analyzed is higher, due to the repeat of 3 sessions on the same mouse per day. This certainly contributes to reaching significance. However, the three samples from the same mouse are not independent points. Are the FOVs different for each session in the same mouse? If they are the same, then the statistics should be repeated but treating all experiments from the same mouse as single experiments. I would suggest repeating the analysis but using only one data point per mouse per day. Also, given that two different indicators were used (6s and 6f), one would need to see whether the statistics are the same in the two conditions.

Robustness: the authors compute this metric, as the product of ensemble duration and average of the Jaccard similarity and find that stable ensembles display higher robustness: isn't it expected that robustness is higher in stable ensembles given that stable ensembles should be observed more often?

Evoked ensembles: It seems to me that evoked ensembles are ensembles extracted during continuous imaging periods that include stimulation. However, one would expect evoked ensembles to be the cells activated time-locked to the visual stimulation. This notion only appears at the end of the paper with "tuned" neurons in Figure 4. In the discussion, authors conclude lines 205-207 that "sensory stimulus reactivate existing ensembles". I do not think this is supported by the analysis performed here. For this, I believe that one would need to compare, within the same mouse the amount of overlap between spontaneous ensembles and "tuned neurons".

How representative are the illustrated examples in Figures 2 and 3? The authors report that about 20 neurons remain active from day 1 to 46 but their main figures display example rasterplots with more than 60 neurons, which is three times more than the average. Is this example representative? Which indicator was used? Is there a difference in stability between 6f and 6s?

Rasterplot filtering: The authors chose to restrict their ensemble analysis to frames with "significant coactivation". Why not use a statistical threshold to determine the number of cells above which a coactivation is significant instead of arbitrarily setting this number to three coactive neurons? In cases of high activity this number may be below significance.

Demixing neuronal identity: The authors assign a neuron to an ensemble if it displays at least a functional connection with another neuron. They use reshuffling to test significance of functional links but still it seems that highly active neurons are more likely to display a high functional connectivity degree and therefore to be stable members of a given ensemble with that definition of ensemble membership. What is the justification to define membership based on pairwise functional connectivity? The finding that core ensemble members display a high functional degree may be just a property reflecting a property of highly active neurons (as previously described by Mizuseki et al., 2013).

Type of neurons imaged: The authors use Vglut1-Cre mice, therefore they are excluding GABAergic cells from their study, this should be clearly mentioned and even discussed.

Volumetric imaging: I am not sure one can say that "volumetric imaging" was performed here, rather this is multi-plane imaging.

Mouse behavior: there is little detail concerning mouse behavior, are mice allowed to run? What is the correlation between ensemble activation and running?

Abstract: the authors should say that 46 days is the longest period they have been recording, otherwise it gives the wrong impression that after 46 days ensembles are no longer stable. Also "most visually evoked ensembles" should be replaced by "ensembles observed during periods of visual stimulation" (see above). "In stable ensembles most neurons still belonged to the same ensemble after weeks": how could ensembles be stable otherwise?

Discussion: I found the discussion quite succinct. It lacks discussing circuit mechanisms for assembly stability and plasticity (role of interneurons for example?), limitations and possible biases in the analysis and placing results in the perspective of other studies analyzing the long-term stability of neuronal dynamics.

https://doi.org/10.7554/eLife.64449.sa1

Author response

This work examines whether coincident firing of neurons in visual cortex is preserved over a long timescale (one month) which is important because it provides insight into the stability and plasticity of neural circuits and visual representations. The authors find that subsets of identified neurons maintain coordinated firing despite some degree of flux in the firin activity across the population.

All reviewers agreed that the question is important but found the analysis lacked depth and there were some technical issues in the experiments that should be addressed with a fuller discussion and potentially additional analysis to eliminate confounds/artefacts. In general, and in light of earlier work (some of which is not cited) the conclusions need to be more circumspect. Specifically:

– There were concerns about movement/loss of cells/calcium indicator artefacts over this long imaging period that should be accounted for more rigorously.

– The analysis applies a somewhat arbitrary criterion for stability (50% of cells remain in responsive in an assembly). This threshold should be systematically explored and justified more carefully.

– The wider literature on this topic should be more thoroughly cited, limitations of the study should be transparently laid out, claims about the overall stability found in this population response and its relevance to memories and behaviour should be moderated in line with the comments below.

Reviewer #1 (Recommendations for the authors):

Perez-Ortega and colleagues performed rigorous experiments to determine if the activity of neurons in visual cortex is similar across days, in particular comparing spontaneous activity in the absence of visual stimuli across days, which was previously not examined to my knowledge. The paper claims that evoked ensembles are more stable than spontaneous ensembles, but more convincing quantitative analyses are required to support these claims.

We are grateful to this reviewer for the careful and thoughtful constructive comments.

– There is only one mention of prior work with multi-day imaging in visual cortex (Ranson 2017). Another related study to cite and compare your results to would be Jeon, Kuhlman 2018 (and I think a comment about how similar/different your results are from this study + Ranson would be useful for the reader). I would also recommend mentioning that there are studies that have observed differences in evoked activity across learning in V1 (e.g. Poort, Khan et al., 2015; Henschke, Dylda et al., 2020). Do you think there was adaptation across days to the stimulus that you repeated?

We now cite Ranson 2017 and Jeon et al., 2018 as well and we agree with the reviewer’s comment. We highlight that our study differs from previous studies because of its multineuronal level of analysis. We measured the locomotion correlation and the tuning of the common neurons across days and the results were similar to those of previous studies. We have added two supplementary figures with analysis (Figure 1 – Supplement Figure 4-5). Please see lines 119-121 in the Results of the revised manuscript:

“Moreover, these common neurons had stable responses to locomotion correlation and tuning across days (Figure 1 — Figure supplement 4-5) consistent with previous studies (Ranson, 2017; Jeon et al., 2018).”

Also, we now cite Poort et al., (2015) and Henschke et al., (2020) and discuss their contributions. Please see lines 264-270 in the Discussion of the revised manuscript:

“Recent longitudinal studies have shown that single-neuron tuning is stable for up to two weeks (Ranson, 2017; Jeon et al., 2018). Moreover, single-neuron selectivity is enhanced after learning (Poort et al., 2015; Henschke et al., 2020). Here, we extend to the microcircuit level these single-neuron longitudinal studies, by examining the stability of neuronal population (Shepherd and Grillner, 2010; Yuste, 2015; Bargas and Pérez-Ortega, 2017). We compared, across weeks microcircuit properties such as functional connectivity, neuronal ensembles, and network topology.”

We have added as well that there is a possible ensemble adaptation during evoked activity. Please see lines 170-173 in the Results:

“Visually-evoked ensembles were more stable than spontaneous ones during the first days, but only some of them were similarly stable as spontaneous ones up to 43-46 days. This result suggests that some ensembles during evoked activity could be adapted to the stimulus that was repeated.”

– Some GCaMP6f mice have aberrant cortical activity (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5604087/). In the raw data (Figure 1F) it doesn't look present, but it would be useful to show more time and sort the neurons by their first PC weights perhaps to see the activity structure.

We appreciate this query. Despite the fact that our VGlut1-Cre::Ai148 mice are a different strain from those described by Steinmetz et al., (2017), it still possible that they present aberrant activity due to GCaMP6f expression (40%; Daigle et al., 2018). However, the GCaMP6f mice analyzed in this study did not present aberrant cortical activity. The new two supplement figures (see the previous comment 1) illustrate normal neuronal activity in these GCaMP6f mice (Figure 1 —figure supplement 4-5).

We also have added this clarification in the Methods (lines 355-356 of the revised manuscript):

Adult transgenic mice GCaMP6s (n = 4) and GCaMP6f (n = 2, none had aberrant activity, Figure 1 – Supplement Figure 4-5; Daigle et al., 2018)…”

– The approach of 3 plane imaging taking the maximum projection seems useful for tracking cells across days. There is a claim that some cells are no longer found / no longer active. Based on Figure 1G it appears there may have been some Z-movement from day 10 to day 46. This Z movement may explain some of the lost active cells. As a sanity check I would recommend plotting the Z-plane on which the cells were maximally active on day 1 vs the Z-plane on which the cells were maximally active on day n.

We appreciate this comment. We have performed the suggested analysis by the reviewer and added the z-displacement from day 1 to the following days. As suggested by the reviewer, we estimated the displacement identifying where neurons were maximally matching between two sessions. We observe no significant displacement between days, so this does not explain the monotonic decrease in neuron number across days. We have added the following paragraph in the Methods (lines 409-416 of the revised manuscript):

“We performed multiplane imaging to record 3 planes: one reference plane, one 5 µm above, and one 5 µm below in order to amend the potential tilt or some z-displacement. We evaluated the z-plane where neuronal position maximally matched between two sessions and estimated an overall z-displacement of 2.5 <milestone-start />±<milestone-end /> 0.2, 3.3 <milestone-start />±<milestone-end /> 0.2, and 3.8 <milestone-start />±<milestone-end /> 0.1 µm (mean <milestone-start />±<milestone-end /> SEM on days 2, 10, and 43-46, respectively). The z-displacement difference between days 2, 10, and 43-46 was not significant (p2vs10 = 0.83, p2vs43-46 = 0.67 and p10vs43-46 = 0.98; Kruskal-Wallis test with post hoc Tukey-Kramer). In the end, we extracted the maximum intensity projection from the 3 planes resulting in a single video for each session (Figure 1D).”

However, we counted the number of neurons discarded due to poor signal to noise (< 18dB), and we found a significant increase of discarded neurons only from day 1 to day 43-46 (24 ± 1% and 37 ± 4%, respectively; p = 0.034). This indicated that the loss of active neurons is mainly due to decrease in imaging quality due to lesser SNR, perhaps due to decrease expression of experimentally-related microtraumas.

We added the following paragraph in the Results (lines 93-101 of the revised manuscript):

Differences in z-displacement were similar across days (< 4 µm, see Methods, Figure 1 — Figure Supplement 2A), and mice running speed tended to increase across days (Figure 1 — Figure Supplement 2B). Both factors could not utterly explain the decrease in the number of active neurons. Moreover, the percentage of discarded neurons, with poor signal to noise (PSNR < 18 dB), significantly increased from day 1 to day 43-46 (from 24 ± 1% to 37 ± 4%, mean ± SEM; p = 0.034; Figure 1 — Figure Supplement 2C). Probably, the loss of active cells on day 43-46 was due to a decrease in imaging quality, which could be partially explained by surgical-attachment-related traumas (Figure 1 — Figure supplement 3)”

We added two supplements (Figure 1 — Figure Supplement 2-3).

– There is an emphasis on analyzing the data as ensembles but I think this may be missing other slow, gradual changes. The definition of stable is at least 50% of neurons were preserved across days. However, the fitting procedure of finding ensembles may produce different ensembles even if those neurons are still correlated to each other. I would recommend two possible additional analyses: (1) compare the correlation matrices for common neurons across days (unless there are too few neurons for this); (2) look at changes in single neuron statistics across days. For (2) this may include reliability of neural responses to the visual stimuli, the weights of the neuron onto the first principal component of spontaneous activity, or the correlation of a neuron with running speed. I think these results may solidify your ensemble result (evoked-related statistics change less across time).

We thank the reviewer for this recommendation. We have performed three additional analyses with different criteria as to what represents an ensemble. As suggested by the reviewer, we measured the change in correlation between common neurons across days, which remains around zero across days with no significant difference.

We also performed single-neuron changes in correlation with running speed and in response to the visual stimulation across days (please see Figure 1 —figure supplement 2-3, see previous comment 1).

Additionally, we have compared how stable ensembles change in correlation with running speed and in response to visual stimulation across days. We added two extra supplements (Figure 3 —figure supplement 2-3).

In all cases, the changes were insignificant. Therefore, stability of common neurons is confirmed and extended to longer periods of analysis (46 days) than previous studies (14 days; Ranson, 2017; Jeon et al., 2018). However, we now also show the stability at the multineuronal level, i.e., stable ensembles preserved their correlation with locomotion and tuning with the visual stimulation.

Reviewer #2 (Recommendations for the authors):

Overall I think the authors collected an interesting dataset. Analyses should be adjusted to include all cells rather than sub-selecting for stability. Additionally, the language needs to be adjusted to better reflect the data. I wish there was any behavioral data included, but if the authors compare their data to publicly available data in V1 for a single recording session during a visually guided task, these concerns could be quelled a bit.

We appreciate the conscientious review and helpful observations on our work.

1. In general the language of this paper and title seem to mismatch the results. The fraction of cells that were 'stable' as the authors say on line 112 was very small, however the authors focus extensively on this small subset for the majority of analyses in the paper. Why ignore the bulk of data (line 119)? What happens if you repeat the same analysis and keep all cells in the dataset? The general language around stability of neural ensembles should be adjusted to better reflect the data (ex: lines 157, 225).

Thank you for raising an important point that we failed to clarify. We focused the study on the stability of neuronal ensembles, i.e., we tried to test whether a group of neurons that fire together on one day will be firing together in the following days. The null hypothesis is that coactive neurons will not be coactive in followings sessions. To test it, we need to define the “common neurons”, i.e., neurons active in different sessions. If we keep all neurons, we cannot validate or falsify our hypothesis, e.g., in a case without any common neurons between two sessions. Hence, we focus on common neurons. We have clarified in the Results (lines 124-130 of the revised manuscript):

“To further test whether a group of neurons is firing together in the following sessions, we necessarily need to evaluate the common neurons between sessions. If we consider the non-common neurons across days, we could misunderstand that neurons are no longer in the ensembles when, possibly, the neurons are silent or out of the field of view due to a displacement in the z-plane. Thus, we focused our analysis on the correlational properties of the common neurons by identifying the neuronal ensembles they formed and then evaluating if these ensembles remained across days.”

Also, we clarified the text in the Results (lines 117-119 of the revised manuscript) to avoid misunderstandings when referring to common neurons, i.e., active neurons in both sessions. We edited the following paragraph:

“We concluded that the neurons activated spontaneously or by visual stimulation are dynamically changing, and only a small proportion of neurons were repeatedly active across sessions from minutes to weeks.”

However, we agree to adjust the language describing stability and also mention flexibility. We have modified the Abstract (lines 9-11 of the revised manuscript):

“Neuronal ensembles, coactive groups of neurons found in spontaneous and evoked cortical activity, are causally related to memories and perception, but it still unknown how stable or flexible they are over time.”

We also have modified the Introduction (lines 33-48 of the revised manuscript):

“Both stability and flexibility in different brain areas have been reported using single-cell statistics (Lütcke et al., 2013; Ziv et al., 2013; Driscoll et al., 2017; Gonzalez et al., 2019; Rule et al., 2019). […] Thus, we asked whether ensembles are preserved across days and how flexible they are, i.e., how many neurons firing together on one day continue to do so in the following days and how many of them stop firing together. Analyzing stable ensembles, we found that ~68 % of their neurons were preserved over weeks (stable neurons), whereas the rest were not (flexible neurons).”

2. There are claims in this paper about how ensembles 'implement long-term memories' in the introduction and conclusion and yet the authors never link the activity of ensembles to any behavioral or stimulus dependent feature. This language reaches far beyond the evidence provided in this paper. The introduction could provide some better framing for expectations of stability vs. drift in neural activity rather than focus on the link between ensembles and memory given that there isn't much focus on the ensembles' contribution to memory throughout. For example, the last sentence of the paper is not supported by data in the paper. Where is the link between ensembles and memory in the data? What is the evidence that transient ensembles are related to new or degraded memories? This reads as though it was the authors' hypothesis before doing the experiments and was not adjusted in light of the results.

We thank this opportunity to clarify this point. We now have clarified that this is just a hypothesis to motivate future studies of the possible relation between the long-term stability of ensembles and memories. We clarified the paragraphs in the abstract and discussion.

In the Abstract (lines 17-19 of the revised manuscript):

“Our results demonstrate that neuronal ensembles can last for weeks and could, in principle, serve as a substrate for long-lasting representation of perceptual states or memories.”

In the Discussion (lines 334-342 of the revised manuscript):

“The stronger internal functional connectivity of stable neurons could be mediated by short- or long-term synaptic plasticity (Carrillo-Reid et al., 2015b; Hoshiba et al., 2017) and may underlie the representation of memories. Since neuronal ensembles in the visual cortex have been associated with perceptual states or memories (Carrillo-Reid et al., 2019; Marshel et al., 2019), stable neuronal ensembles could represent long-term memories and transient ensembles could illustrate the emergence of new memories or the degradation of existing ones. Future experiments, perhaps using holographic optogenetics (Yang et al., 2018) during memory tasks, could test this hypothesis and explore the potential link between the stability of ensembles and the persistence of memories.”

We have also provided a better framing of the expectations in the Introduction (lines 29-40 of the revised manuscript):

“The optogenetic activation of the ensembles can lead to behavioral effects consistent with the hypothesis that they represent perceptual or memory states (Carrillo-Reid et al., 2019; Marshel et al., 2019). Interestingly, while single-cell tuning remains stable in visual cortex (Ranson, 2017; Jeon et al., 2018), a representational drift occurs across days (Deithch et al., 2020). Both stability and flexibility in different brain areas have been reported using single-cell statistics (Lütcke et al., 2013; Ziv et al., 2013; Driscoll et al., 2017; Gonzalez et al., 2019; Rule et al., 2019). However, there is a lack of multineuronal studies on cortical activity across days. Thus, we asked whether ensembles are preserved across days and how flexible they are, i.e., how many neurons firing together on one day continue to do so in the following days and how many of them stop firing together. We also explored whether the stability or flexibility of ensembles are different between spontaneous and visually evoked activity.”

3. There is no discussion around the alternative to stability of neuronal ensembles. What are the current theories about representational drift? For example, in Line 34 the authors present an expectation for stability without any reasoning for why there need not be stability. This lack of framing makes their job of explaining results in line 217 more difficult. There is a possibility that the most stable cells aren't more important – what is the evidence that they are? Does an ensemble need a core? Would be interesting to include some discussion on the possibility of a drifting readout (Line 223). [https://doi.org/10.1016/j.conb.2019.08.005]

We appreciate this suggestion. We have edited the discussion including current theories (lines 297-310 of the revised manuscript):

“Multiplane two-photon calcium imaging allowed tracking the same neurons and identifying neuronal ensembles across weeks. Stable ensembles were preserved across days, and 68% of their neurons continued to be active, while the rest of the neurons were replaced by new ones (Figure 4F). The consistent number of neurons preserving their interactions within ensembles across days does not support a representational drift in cortical responses at the single neuronal level (Discroll et al., 2017, Rule et al., 2019; Deithch et al., 2020). In fact, this could be precisely one of the functions of ensembles: to maintain a stable functional state in the midst of an ongoing homeostatic replacement of the activity of individual neuronal elements (Mrsic-Flogel et al., 2007; Lütcke et al., 2013; Hengen et al., 2013; Clopath et al., 2017). The stability of ensembles could be explained by the stability of dendritic spines (Yuste and Bonhoeffer, 2001). The weak connectivity of flexible neurons could be mediated by thin spines, which appear and disappear over days (Holtmaat et al., 2005), while the high connectivity of stable neurons could be maintained by thick spines, which last for months (Holtmaat et al., 2005; Grutzendler et al., 2002).”

4. How do activations in V1 in this dataset compare to other data collected from V1 while the animal is performing a task (where for example the angle of the gradings is relevant to how the mouse should respond)? I would be interested to know if the authors compared statistics of their ensembles to publicly available data recorded in V1 during a visually guided behavior. Are the ensembles tuned to anything in particular? Could they be related to movement? [http://repository.cshl.edu/id/eprint/38599/]

We appreciate these queries from the reviewer. We analyzed the publicly available data suggested by the reviewer (http://repository.cshl.edu/id/eprint/38599/), but we did not find any difference in the statistics with the behavior (evoked on day 1 “Churchland Lab dataset”, Figure 3 —figure supplement 5 and Figure 4 —figure supplement 2). Mice were recorded on different days and different regions, so we could not analyzed the same cells across days. So, further studies are needed to evaluate the stability of ensembles across days during the performance of a behavioral task.

We have added a paragraph in the Results (lines 243-257 of the revised manuscript):

“Furthermore, there were no significant differences when we analyzed the Churchland Lab dataset, where evoked activity was relevant for performing a task (Musall et al., 2019; Figure 3 — Figure Supplement 5 and Figure 4 — Figure Supplement 2). In all methods and datasets, the number of stable neurons within ensembles was above 60 % (Figure 4 — Figure Supplement 2B), the connection density of the stable neurons was above 0.5, which was significantly greater than the connection density of the lost neurons (Figure 4 — Figure Supplement 2D). In summary, we detected long-term ensemble stability regardless of the method or dataset used.”

We have added 2 extra supplement figures.

In addition, we also have performed ensemble analysis related with running speed and visual stimulation (Figure 3 – Supplement Figure 2-3). We observed that some ensembles are tuned to visual stimulus or locomotion, but the average change during locomotion or tuning of ensembles is near zero across days. Thus, the tuning and locomotion correlation of ensembles remain stable across days.

We have edited the next paragraph in the Results (lines 180-185 of the revised manuscript):

“In conclusion, stable ensembles cannot be explained by chance, and they were not only reactivated during the following days but also their correlation with locomotion and tuning remained unchanged across days (Figure 3 — Figure supplement 2-3). This speaks to the likelihood that neuronal ensembles are true functional circuit elements and not an epiphenomenon of the population activity or a statistical artifact.”

We have added 2 extra supplements.

5. The authors provide some hypotheses as to why fewer cells are active in the later imaging sessions (dead/dying cells?). This is worrisome in regards to how much it might have affected the imaged area's biology. One alternative hypothesis is that the animal is more familiar with the environment/ not running as much etc. Have the authors collected any behavioral data to compare over time?

We thank the reviewer for pointing this out. We recorded the running speed in most of our experiments. Contrary to the expectation, mice run more across days (Figure 1 — Figure Supplement 2B). At the same time, we have found a likely explanation for the decrease in active cells: we counted the number of neurons discarded due to poor signal to noise (< 18 dB), and we found a significant increase of discarded neurons only from day 1 to day 43-46 (24 ± 1% and 37 ± 4%, respectively; p = 0.034). Likely, the loss of active neurons is due to decrease in imaging quality (Figure 1 — Figure Supplement 2C) which in turn could be due to surgical-attachment-related microtraumas (Figure 1 — Figure supplement 3).

We added two supplementary figures.

We added the next paragraph in the Results (lines 93-101 of the revised manuscript):

“Differences in z-displacement were similar across days (< 4 µm, see Methods, Figure 1 — Figure Supplement 2A), and mice running speed tended to increase across days (Figure 1 — Figure Supplement 2B). Both factors could not utterly explain the decrease in the number of active neurons. Moreover, the percentage of discarded neurons, with poor signal to noise (PSNR < 18 dB), significantly increased from day 1 to day 43-46 (from 24 ± 1% to 37 ± 4%, mean ± SEM; p = 0.034; Figure 1 — Figure Supplement 2C). Probably, the loss of active cells on day 43-46 was due to a decrease in imaging quality, which could be partially explained by surgical-attachment-related traumas (Figure 1 — Figure supplement 3).”

6. How much do the results change when you vary the 50% threshold of preserved neurons within an ensemble (Line 146)? Does it make sense to call an ensemble stable when 50% of the cells change? Especially given that the cells analyzed as contributing to an ensemble are already sub-selected to be within the small population of stable cells (Line 119)?

We are grateful for these observations. We have performed the entire analysis by varying the threshold to define a stable ensemble from zero to one (0, 0.25, 0.33 —50 % of neurons—, 0.5, 0.75, and 1; Figure 3 — Figure Supplement 4; Figure 4 — Figure Supplement 1). There were fewer stable ensembles when we increased the threshold, as expected, but there were no significant increases in stable ensembles when we decreased this threshold, even to zero (Figure 3 — Figure supplement 4A). Regardless the threshold used, there are no differences in the functional structure of stable ensembles (Figure 3 — Figure supplement 4B-D), and they consistently preserve more than 60 % of their neurons (Figure 4 — Figure supplement 1B-D).

We have added in the Results (lines 190-199 of the revised manuscript):

“We asked if varying the threshold to define stability could change this result. If we set the strictest threshold (Jaccard similarity = 1, i.e., all the neurons remain in the same ensemble), we counted significantly less stable ensembles (P < 0.05; Figure 3 — Figure supplement 4A). On the other hand, we did not find any significant addition of stable ensembles when we reduced the threshold, even to zero (Figure 3 — Figure supplement 4A). This indicates that the maximum number of stable ensembles could be defined by the minimum number of ensembles found between two sessions. In all cases, regardless of the Jaccard similarity threshold used, there were no significant differences in the functional structure of the ensembles (Figure 3 — Figure supplement 4B-C). This result suggests that the functional structure of the ensembles is steady and independent of its stability.”

We have added two extra Supplementary Figures.

7. Cells are referred to as 'stable' when they're active on 3 different sessions that are separated in time. However, the authors find a smaller number of cells are stable over extended time (43-46 days later). If we extrapolate this over more time, would we expect these cells to continue to be stable? Given these concerns, it might make more sense to qualify the language around stability by the timespan over which these cells were studied.

We are not sure whether we fully understand what cells the reviewer is referring to, but we thank to the reviewer for this opportunity to discuss this point. The reviewer maybe referring to the common neurons (Figure 1K). In this case, we think that common neurons are reduced across days because we identified fewer active neurons (Figure 1H) and our recordings showed a displacement in the z-plane, likely losing some neurons and recording new ones (new figure showed in the response of concern 5 from this reviewer, Figure 1 — Figure Supplement 2A).

We have added a paragraph in the Discussion (lines 279-296 of the revised manuscript):

“We could not track the activity of every single neuron over weeks with sufficient signal to noise (PSNR > 18 dB, Figure 1H) since image quality decreased over days (Figure 1G). This could be due to the repeated experimental procedures on the same cortical location, decreased transgene expression, laser photobleaching, or surgical-attachment-related microtraumas (Figure 1 — Figure Supplement 3). Nevertheless, our method was sufficient to capture similar average activity from the neurons with adequate signal to noise over weeks (Figure 1I), agreeing with the hypothesis that cortical circuits maintain a basic homeostatic activity level, even in spite of perturbations (Mrsic-Flogel et al., 2007; Lütcke et al., 2013; Hengen et al., 2013; Clopath et al., 2017). However, we found that single-neuron responses were variable during the same repeated stimulation (Montijn et al., 2016; Stringer et al., 2019c). Indeed, the chance of finding any given neuron also active in a future session (”common neurons”) was less than 60 %, even 5 min later (Figure 1K; Tolias et al., 2007). This result is consistent with transient silencing of neurons (Prsa et al., 2017), which could be a neuronal correlate of the learning enhancement in deep neural networks (Srivastava et al., 2014; Rule et al., 2019). We found a continuous decrease in “common” neurons, which could be explained by the loss of neurons (Figure 1H) together with small z-plane displacement across days (Figure 1 — Figure supplement 2A). If one could maintain imaging quality and focus we would expect a consistent number of common neurons over weeks, regardless of the time recorded.”

8. Filtering frames to only coactive neurons for ensemble identification seems strange to me. Authors may be overestimating the extent of coactivation. What happens when you don't do this? How much do the results change when you don't subselect for Jaccard similarity? I would be interested to see how the results vary as you vary this threshold (Line 136).

We thank the reviewer for this suggestion. We have performed the entire analysis keeping all vectors (without selecting the most similar vectors). We added 2 supplementary figures (presented in the previous concern 4 by this reviewer; Figure 3 — Figure Supplement 5 and Figure 4 — Figure Supplement 2). In general, the statistics of ensembles are very similar. The only relevant difference was observed in the ensemble robustness, whose values were higher compared to the selection of most similar vectors and they were not significantly different between stable and transient ensembles. Without selecting vectors, the algorithm accommodates all vectors as ensemble activations, even if they are infrequent activations. This, by itself, increases the ensemble robustness, resulting in similarities between stable and transient ensembles during spontaneous activity. However, we could detected differences between stable and transient ensembles during evoked activity.

We added the following paragraph in the Results (lines 230-239 of the revised manuscript):

“In our study, we used all single sessions (“main”, 3 sessions/day/condition with a selection of vectors), and the results were similar when we used only one session per day (“single” 1 session/day/condition), all the raster without vector selection (“all vectors”), and the significant population coactivations (“coactivity peaks”; see Methods). The properties of the stable and transient ensembles as the neurons/ensemble and connection density remained with no differences (Figure 3 — Figure Supplement 5B-C), but ensemble robustness values had some variability between methods (Figure 3 — Figure Supplement 5D). However, the vector selection method proposed in this study differentiates significantly between stable and transient ensembles, specially during spontaneous activity.”

9. The term 'evoked activity' is misleading because the authors don't link these activations to the visual stimulus. There's no task, so the mice could be paying little attention to the stimulus. Should we really consider this activity to be visually driven? Could the authors provide any evidence of this?

We agree with the reviewer. There is no task in our experiments, and visual stimulation may be irrelevant to the mice. However, neuronal activity remains tuned to the visual stimulation across days. We have added one extra supplement during the stimulation sessions, which evidences the single-neuron tuning to the visual stimulation (Figure 1 —figure supplement 5).

We also have added a supplement figure in the concern 4 from this reviewer, which demonstrates the ensemble tuning to the visual stimulation (Figure 3 — Figure supplement 3).

10. A method like seqNMF could reveal ensembles that are offset in time. This looser temporal constraint could potentially reveal more structure. This should be run on the entire dataset (without stability sub-selection). I suggest this as a potential alternative or supplement to the method described by the authors. [https://elifesciences.org/articles/38471]

We agree with the reviewer. We have reanalyzed the data with the algorithm seqNMF as suggested by the reviewer, obtaining similar results. We have added a new supplementary figure showing the persistence of temporal sequences during spontaneous and evoked activity.

We now propose seqNMF as a complement of our method used in the Results (lines 249-252 of the revised manuscript):

“On the other hand, we also observed persistence across days of the temporal sequences of neuronal activations using the seqNMF toolbox (Mackevicius et al., 2019; Figure 4 — Figure Supplement 3), which we suggest in future studies to deeply analyze the dynamic of the temporal structure within ensembles.”

We also have added a new paragraph in the Methods (lines 561-567 of the revised manuscript):

“Finding neuronal sequences using the seqNMF toolbox. We used the seqNMF toolbox (https://github.com/FeeLab/seqNMF; Mackevicius et al., 2019) to detect possible neuronal patterns of sequential activation as an alternative to our method, which has a constraint to detect simultaneous neuronal activation (coactivation). We used the common neurons across days during spontaneous and evoked activity to find temporal sequences within windows of ~1 s (L = 12), a regularization parameter λ = 0.005, and 50 iterations (Figure 4 — Figure supplement 3).”

Again, we thank this reviewer for all detailed observations to strengthen our manuscript.

Reviewer #3 (Recommendations for the authors):

Neuronal ensembles have been shown by this lab and others to constitute one basic functional unit for the representation of information in cortical circuits. It is therefore important to determine how stable these blocks of representation might be. If these ensembles were preserved across time and sensory stimuli, this would indicate a significant degree of structure underlying cortical representations. In a first attempt to address these important issues, this manuscript analyzes the long-term stability of ensembles of coactive neurons in the layer 2/3 of mouse visual cortex across several days. Ensembles were recorded during periods of spontaneous activity as well as during visual stimulation (evoked). For this, the authors record spontaneous and evoked activity using two-photon calcium imaging one, ten and 40 days after the first recording session. In order to maximize overlap between successive imaging sessions, the authors record three planes separated by 5 microns almost simultaneously (9ms interval) using an electrically-tunable lens. They show that ensembles extracted during visual stimulation periods are more stable on days 2 and 10 than those computed during spontaneous activity. Stable ensembles display a higher "robustness" (a parameter that quantifies how many times a given ensemble is repeated and how similar these repeats are). Neurons displaying stable membership are more functionally connected than unstable ones. It is concluded that such observed stability of spontaneous and evoked ensembles across weeks could provide a mechanism for memories. Long-term calcium imaging within the same population of neurons is a real challenge that the authors seem to overcome in the study. The conclusions are important, my main concern relates to the number of experiments and analyses supporting these findings as detailed below.

We are very thankful to this reviewer for the useful comments on our manuscript.

Number of experiments and statistics: According to Table 1, two mice with GCamP6f have been through the complete imaging protocol (days 1,2, 10 and 43) but none with the 6s, since 3 missed the intermediate measure (day 10) and one the last point (day 40+). Therefore five mice have been recorded over weeks with two different indicators, but only two were sampled on day 10. One mouse was only recorded until day 10. Altogether, this is quite a low sampling, but the experiments are certainly difficult. However, the total number of experiments analyzed is higher, due to the repeat of 3 sessions on the same mouse per day. This certainly contributes to reaching significance. However, the three samples from the same mouse are not independent points. Are the FOVs different for each session in the same mouse? If they are the same, then the statistics should be repeated but treating all experiments from the same mouse as single experiments. I would suggest repeating the analysis but using only one data point per mouse per day. Also, given that two different indicators were used (6s and 6f), one would need to see whether the statistics are the same in the two conditions.

We appreciate the acknowledgement of the difficulty of the experiments. The fields of view are the same for each animal, so we now compute the statistics (mean ± SEM) using a single data point per mouse per day (“single (1 session/day)”, Figure 3 — Figure supplement 5 and Figure 4 — Figure supplement 2). To increase the numbers, we also have analyzed two publicly datasets available (Figure 3 — Figure supplement 5 and Figure 4 — Figure supplement 2): seven GCaMP6f mice recorded in 3 different days from The Allen Brain Institute (“Allen Brain Institute dataset”) and four GCaMP6f mice recorded in the same day during a visually guided behavior (“Churchland Lab dataset”). We also have separated the results for each indicator used (“only GCaMP6f” and “only GCaMP6s” in Figure 3 — Figure supplement 5 and Figure 4 — Figure supplement 2). In all datasets, results were similar.

We have added in the Results (lines 230-257 of the revised manuscript):

“In our study, we used all single sessions (“main”, 3 sessions/day/condition with a selection of vectors), and the results were similar when we used only one session per day (“single” 1 session/day/condition) It should be noted that the results were heterogeneous among GCaMP6s and GCaMP6f mice, consistent with previous studies (Musall et al., 2019). We analyzed four GCaMP6s and two GCaMP6f mice in our dataset, seven GCaMP6f mice from the Allen Brain Institute dataset, and four GCaMP6f mice from Churchland Lab dataset. In summary, we detected long-term ensemble stability regardless of the method or dataset used.”

We have added two supplementary figures.

Robustness: the authors compute this metric, as the product of ensemble duration and average of the Jaccard similarity and find that stable ensembles display higher robustness: isn't it expected that robustness is higher in stable ensembles given that stable ensembles should be observed more often?

We apologize for our lack of clarity. As it is an independent measure, it is not necessarily expected that robustness should be higher in stable ensemble. We can compute the robustness value in one single session, but, independently, we can identify if an ensemble is stable when it is observed in a future session. For example, two ensembles could have the same robustness value in the first session, but only one of them is active in a future session.

We have clarified the following paragraph in the Methods (lines 517-522 of the revised manuscript):

“Ensemble robustness, we introduced here as robustness = similarity · activity, where similarity is the average of the Jaccard similarity between every pair of column vectors of the ensemble matrix raster Ej, and activity is the fraction of ensemble active frames to the total frames of the session. The higher the value, the higher the robustness. We computed ensemble robustness for every single session, so it would not be expected beforehand if the ensemble would be stable or not.”

We add the following text in the legend of Figure 3A:

“Note that ensemble robustness is computed per ensemble per single session.”

Evoked ensembles: It seems to me that evoked ensembles are ensembles extracted during continuous imaging periods that include stimulation. However, one would expect evoked ensembles to be the cells activated time-locked to the visual stimulation. This notion only appears at the end of the paper with "tuned" neurons in Figure 4. In the discussion, authors conclude lines 205-207 that "sensory stimulus reactivate existing ensembles". I do not think this is supported by the analysis performed here. For this, I believe that one would need to compare, within the same mouse the amount of overlap between spontaneous ensembles and "tuned neurons".

Thank you for pointing this out. We have added the analysis of ensembles during spontaneous activity which also were active during visually evoked activity and vice versa (see “S day 1 vs E day n | E day 1 vs S day n” on Figure 3 — Figure Supplement 5 and Figure 4 — Figure Supplement 2). We also have counted the fraction of tuned neurons found across days that overlapped with the maximally matched spontaneous ensemble found on day 1. In addition and consistent with this result, we cite previous work by Miller et al., (2014).

We have added the following paragraph in the Discussion (lines 271-278 of the revised manuscript):

“Somewhat surprisingly, we did not find any relevant differences in long-term stability between spontaneous and visually evoked activity. Indeed, we found that ensembles during spontaneous activity were also active during visually evoked activity and vice versa (see “S day 1 vs E day n | E day 1 vs S day n” on Figure 3 — Figure Supplement 5 and Figure 4 — Figure Supplement 2). In fact, more than 60 % of neurons from spontaneous ensembles on day 1 were found to be tuned to a specific stimulus on following weeks. This result is consistent with the hypothesis that sensory stimuli reactivate existing ensembles, which are already present in the spontaneous activity (Miller et al., 2014).”

How representative are the illustrated examples in Figures 2 and 3? The authors report that about 20 neurons remain active from day 1 to 46 but their main figures display example rasterplots with more than 60 neurons, which is three times more than the average. Is this example representative? Which indicator was used? Is there a difference in stability between 6f and 6s?

We thank this opportunity to clarify these figures. We previously showed an example between common neurons from day 1 and 2 in the Figures 2-3, but now we have modified them showing a representative example of the common neurons between days 1 and 43 from a GCaMP6s mouse. So, we now display a representative example from one mouse GCaMP6s in Figures 2-3 and one GCaMP6f in Figure 4. It seems there are no differences in stability between GCaMP6s and GCaMP6f as we showed above in response to a previous concern by this reviewer (Figure 3 — Figure supplement 5 and Figure 4 — Figure supplement 2).

We have edited Figures 2 and 3.

Rasterplot filtering: The authors chose to restrict their ensemble analysis to frames with "significant coactivation". Why not use a statistical threshold to determine the number of cells above which a coactivation is significant instead of arbitrarily setting this number to three coactive neurons? In cases of high activity this number may be below significance.

We appreciate this observation. We have added a new analysis of the significant population coactivation peaks, and the results of stability remain similar. The results are now shown in two supplementary figures, added in a previous response to this reviewer (“coactivity peaks” in Figure 3 — Figure supplement 5 and Figure 4 — Figure supplement 2). We added the following paragraph in the Methods (lines 477-484 of the revised manuscript):

“Detection of coactivity peaks to identify ensembles. Alternatively, we analyzed our dataset using a method to extract neuronal ensembles based on significant population coactivity (Pérez-Ortega et al., 2016). Briefly, we obtained a 1 × F vector, where F is the number of frames, by summing the coactive neurons from the raster matrix E. Then, we perform 1,000 surrogated raster matrices, by randomly circular shifting in time the activity of every single neuron and computing the coactivity given by chance. We determined a significant coactivity threshold (P < 0.05) from surrogated coactivity, and the vectors above this threshold were clustered to extract neuronal ensembles.”

We have added a paragraph to the Results (lines 230-239 of the revised manuscript):

“In our study, we used all single sessions (“main”, 3 sessions/day/condition with a selection of vectors), and the results were similar when we used only one session per day (“single” 1 session/day/condition), all the raster without vector selection (“all vectors”), and the significant population coactivations (“coactivity peaks”; see Methods). The properties of the stable and transient ensembles as the neurons/ensemble and connection density remained with no differences (Figure 3 — Figure Supplement 5B-C), but ensemble robustness values had some variability between methods (Figure 3 — Figure Supplement 5D). However, the vector selection method proposed in this study differentiates significantly between stable and transient ensembles, especially during spontaneous activity.”

Demixing neuronal identity: The authors assign a neuron to an ensemble if it displays at least a functional connection with another neuron. They use reshuffling to test significance of functional links but still it seems that highly active neurons are more likely to display a high functional connectivity degree and therefore to be stable members of a given ensemble with that definition of ensemble membership. What is the justification to define membership based on pairwise functional connectivity? The finding that core ensemble members display a high functional degree may be just a property reflecting a property of highly active neurons (as previously described by Mizuseki et al., 2013).

We appreciate this query. We compute the percentage of activity versus the percentage of functional connections of 297 and 314 common neurons during spontaneous and evoked activity, respectively, from all the mice in our dataset. The coefficient of determination was near zero (R2 = 0.001), so there is no relation between neuron’s activity and its significant functional connections. We have added a new supplement (Figure 2 — Figure supplement 1).

We have added this line in the Results (lines 138-139 of the revised manuscript):

“The functional connections of each neuron were independent of its level of activity (R = 0.001, Figure 2 — Figure Supplement 1)”

Type of neurons imaged: The authors use Vglut1-Cre mice, therefore they are excluding GABAergic cells from their study, this should be clearly mentioned and even discussed.

Thanks for pointing this out. We now clarify that we recorded the activity of pyramidal neurons, excluding GABAergic neurons. Additionally, we acknowledge that this is a limitation of this study. We have modified the following paragraph in the Results (lines 56-59 of the revised manuscript):

“We performed two-photon calcium imaging of pyramidal cells in layer 2/3 of the visual cortex from six transgenic mice (GCaMP6s, n = 4 animals; and GCaMP6f, n = 2) through a cranial window to examine the stability of ensembles under visually-evoked and spontaneous activity.”

We have added in the Discussion the following paragraph (lines 311-320 of the revised manuscript):

“One limitation of this study is that we did not image the activity of GABAergic interneurons, which are at least 28 different types based on the morphoelectric and transcriptomic classifications (Yuste et al., 2020; Gouwens et al., 2020; Yao et al., 2021). Parvalbumin (PV) interneurons would stabilize the cortical circuit while somatostatin (SOM) and vasoactive intestinal peptide (VIP) interneurons would modulate the gain of pyramidal neurons according to recent models (Bos et al., 2020; Millman et al., 2020). Moreover, single-neuron statistics showed that PVs in the visual cortex undergo faster homeostasis (Hengen et al., 2013) and are more stable than pyramidal cells (Ranson, 2017). Further studies are needed to evaluate the stability within interneuron interactions and the interactions between interneurons and pyramidal neurons.”

Volumetric imaging: I am not sure one can say that "volumetric imaging" was performed here, rather this is multi-plane imaging.

We have made the change from “volumetric imaging” to “multiplane imaging” thorough out the manuscript.

Mouse behavior: there is little detail concerning mouse behavior, are mice allowed to run? What is the correlation between ensemble activation and running?

Thank you for this observation. We have performed the correlation between ensembles and the running speed of the mice during spontaneous and visually evoked activity. We have added two new supplementary figures (Figure 3 — Figure supplement 2 and Figure 3 — Figure supplement 3).

Abstract: the authors should say that 46 days is the longest period they have been recording, otherwise it gives the wrong impression that after 46 days ensembles are no longer stable. Also "most visually evoked ensembles" should be replaced by "ensembles observed during periods of visual stimulation" (see above). "In stable ensembles most neurons still belonged to the same ensemble after weeks": how could ensembles be stable otherwise?

We appreciate the suggestions. We have modified the Abstract to:

“Neuronal ensembles, coactive groups of neurons found in spontaneous and evoked cortical activity, are causally related to memories and perception, but it still unknown how stable or flexible they are over time. We used two-photon multiplane calcium imaging to track over weeks the activity of the same pyramidal neurons in layer 2/3 of the visual cortex from awake mice and recorded their spontaneous and visually-evoked responses. Less than half of the neurons were commonly active across any two imaging sessions. These “common neurons” formed stable ensembles lasting weeks, but some ensembles were also transient and appeared only in one single session. Stable ensembles preserved ~68 % of their neurons up to 46 days, our longest imaged period, and these “core” cells had stronger functional connectivity. Our results demonstrate that neuronal ensembles can last for weeks and could, in principle, serve as a substrate for long-lasting representation of perceptual states or memories.”

We have added this paragraph in the Discussion (lines 323-329 of the revised manuscript):

“While there is a significant state of flux in cortical activity at any given moment, there is a subset of neurons that remain active through weeks and form neuronal ensembles. The stability of ensembles that we report could be an underestimated since we only measured snapshots of cortical activity. These stable ensembles, which also have some rotation of their individual neuronal components, appear anchored by a core of neurons. Specifically, 68 % of neurons remain consistently active within ensembles up to 46 days later (Figure 4F) and had stronger functional connectivity (Figure 4H).”

Discussion: I found the discussion quite succinct. It lacks discussing circuit mechanisms for assembly stability and plasticity (role of interneurons for example?), limitations and possible biases in the analysis and placing results in the perspective of other studies analyzing the long-term stability of neuronal dynamics.

Thank you for this suggestion. We have extended the discussion, comparing our results with recent longitudinal studies and also discussing representational drift theories about the stability and flexibility of the cortical activity.

We have added the following paragraphs in the Discussion (lines 283-310 of the revised manuscript):

“Nevertheless, our method was sufficient to capture similar average activity from the neurons with adequate signal to noise over weeks (Figure 1I), agreeing with the hypothesis that cortical circuits maintain a basic homeostatic activity level, even in spite of perturbations (Mrsic-Flogel et al., 2007; Lütcke et al., 2013; Hengen et al., 2013; Clopath et al., 2017). However, we found that single-neuron responses were variable during the same repeated stimulation (Montijn et al., 2016; Stringer et al., 2019c). Indeed, the chance of finding any given neuron also active in a future session (”common neurons”) was less than 60 %, even 5 min later (Figure 1K; Tolias et al., 2007). This result is consistent with transient silencing of neurons (Prsa et al., 2017), which could be a neuronal correlate of the learning enhancement in deep neural networks (Srivastava et al., 2014; Rule et al., 2019).

Multiplane two-photon calcium imaging allowed tracking the same neurons and identifying neuronal ensembles across weeks. Stable ensembles were preserved across days, and 68% of their neurons continued to be active, while the rest of the neurons were replaced by new ones (Figure 4F). The consistent number of neurons preserving their interactions within ensembles across days does not support a representational drift in cortical responses at the single neuronal level (Discroll et al., 2017, Rule et al., 2019; Deithch et al., 2020). In fact, this could be precisely one of the functions of ensembles: to maintain a stable functional state in the midst of an ongoing homeostatic replacement of the activity of individual neuronal elements (Mrsic-Flogel et al., 2007; Lütcke et al., 2013; Hengen et al., 2013; Clopath et al., 2017). The stability of ensembles could be explained by the stability of dendritic spines (Yuste and Bonhoeffer, 2001). The weak connectivity of flexible neurons could be mediated by thin spines, which appear and disappear over days (Holtmaat et al., 2005), while the high connectivity of stable neurons could be maintained by thick spines, which last for months (Holtmaat et al., 2005; Grutzendler et al., 2002).”

We thank again this reviewer for the constructive comments to our manuscript.

https://doi.org/10.7554/eLife.64449.sa2

Article and author information

Author details

  1. Jesús Pérez-Ortega

    Department of Biological Sciences, Columbia University, New York, United States
    Contribution
    Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Writing – original draft, Writing – review and editing
    For correspondence
    jesus.perez@columbia.edu
    Competing interests
    None
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8502-1692
  2. Tzitzitlini Alejandre-García

    Department of Biological Sciences, Columbia University, New York, United States
    Contribution
    Conceptualization, Methodology
    Competing interests
    None
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2243-8703
  3. Rafael Yuste

    Department of Biological Sciences, Columbia University, New York, United States
    Contribution
    Conceptualization, Funding acquisition, Project administration, Resources, Writing – review and editing
    Competing interests
    None
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4206-497X

Funding

National Eye Institute (R01EY011787)

  • Rafael Yuste

National Institute of Mental Health (R01MH115900)

  • Rafael Yuste

Consejo Nacional de Ciencia y Tecnología (CVU365863)

  • Jesús Pérez-Ortega

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We thank James Holland for his assistance and members of the Yuste Lab for useful comments. This project was supported by R01EY011787 and R01MH115900. RY is an Ikerbasque Research Professor at the Donostia International Physics Center (DIPC). JP has a postdoctoral fellowship from the National Council of Science and Technology from Mexico (CONACYT). The authors have no competing financial interests to declare. JP, TA, and RY conceived the project, planned experiments, and discussed results. TA and JP performed surgeries, JP performed experiments, coded the software, and analyzed the data. JP and RY wrote the paper. RY assembled and directed the team and secured funding and resources. JP dedicates this paper to the memory of Amparo Rodríguez-Cruz.

Ethics

All experimental procedures were carried out in accordance with the US National Institutes of Health and Columbia University Institutional Animal Care and Use Committee (protocol AC-AAV3464).

Senior Editor

  1. Tirin Moore, Stanford University, United States

Reviewing Editor

  1. Timothy O'Leary, University of Cambridge, United Kingdom

Reviewers

  1. Carsen Stringer, Howard Hughes Medical Institute, United States
  2. Laura N Driscoll, Stanford University, United States

Publication history

  1. Preprint posted: October 28, 2020 (view preprint)
  2. Received: October 29, 2020
  3. Accepted: July 29, 2021
  4. Accepted Manuscript published: July 30, 2021 (version 1)
  5. Version of Record published: August 19, 2021 (version 2)

Copyright

© 2021, Pérez-Ortega et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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