Since the seminal work of Ramón y Cajal it has become clear that neuronal circuits consist of a large variety of neuronal elements. Such diversity encompasses virtually all aspects of neuronal biology, including morphological, genetic, physiological, and functional properties. Over recent years, major advances have been made toward the systematic classification of neuronal cell types (Ascoli et al., 2008; Scala et al., 2020; Tasic et al., 2016; Zeisel et al., 2015). From these research lines, it has become increasingly clear that a combinatorial approach – integrating morphological, molecular, electrophysiological, and functional properties – is required for neuronal cell-type classification.

As inventories of cell types within brain circuits are beginning to emerge, the next challenge is to understand how these cell types contribute to neural circuit computations during behavior. Bridging this gap is a methodological challenge, since current in vivo techniques for neural circuit analysis – for example optogenetic tagging and Ca²⁺ imaging (Anikeeva et al., 2011; Ghosh et al., 2011; Lima et al., 2009) – provide only limited information about the recorded neuronal elements. These methods largely rely upon the genetically restricted expression of Channelrhodopsin (ChR2) or Ca²⁺ indicators – for example via Cre driver lines (Adesnik, 2018; Gafford and Ressler, 2016; Kravitz et al., 2013). In the case of ‘optogenetic tagging’, recordings are performed with a dual optical and electrical probe (referred to as ‘optrode’) and short-latency spikes elicited upon illumination provide information about the identity of the recorded units. One limitation of this approach is that ‘cell types’ are defined by single features, like genetic marker expression or projection target. Additional structural features – like morphology, local and long-range axonal projections, and gene expression profiles – are not accessible with these techniques, thereby making it difficult to map neuronal activity (assessed in vivo) to the multidimensional cell-type classification schemes (being developed in vitro).

In vivo single-cell identification techniques can in principle provide the necessary anatomical resolution for multidimensional classification of the recorded neurons. For example, the juxtacellular method (Pinault, 1996; Pinault, 1994) allows recording of single neurons in awake behaving animals, along with post hoc morphological analysis and molecular phenotyping of the recorded cells (Averkin et al., 2016; Diamantaki et al., 2018; Katona et al., 2014; Lapray et al., 2012; Tang et al., 2014b; Valero et al., 2015). However, a significant limitation of these methods is that neurons are typically sampled by ‘blind’ procedures; hence, recordings cannot be targeted to a predefined cell class, which makes it extremely challenging – if not impossible – to efficiently sample sparse neuronal elements within a given circuit.

To circumvent this limitation, an elegant approach has been recently developed by combining single-cell identification techniques with optogenetic tagging (Katz et al., 2013; Muñoz et al., 2014). This approach enabled ChR2-assisted targeting of genetically defined cell classes; in addition, by means of juxtacellular labeling and post hoc morphological/molecular analysis, it also provided access to the multidimensional neuronal features required for precise cell-type classification (Muñoz et al., 2017). In its current form however, the applicability of this method is limited to mechanically stable preparations (e.g., anesthetized or head-restrained animals), thereby preventing structure–function analysis in freely moving animals, engaged in ethologically relevant behaviors.

In the present study, we aimed at bridging this gap by combining the single-cell opto-tagging method (Katz et al., 2013; Muñoz et al., 2014) with the juxtacellular procedures that we have recently established in freely moving mice (Diamantaki et al., 2018). This approach enabled us to efficiently target juxtacellular recording and labeling to predefined, genetically tagged cell classes in awake animals, engaged in natural exploratory behavior.

We demonstrate the technical feasibility of this approach in the dorsal CA1 of the mouse hippocampus. During behavior, the activity of ‘place cells’ in this region (O’Keefe, 1976) is thought to contribute to the encoding of spatial and episodic experiences, and thus to form the neural basis of ‘memory engrams’ (Tonegawa et al., 2018). Anatomically – despite its relatively simple organization – the CA1 has a remarkably diverse cellular composition. In vitro and in vivo work has distinguished more than 20 different morphofunctional interneuronal classes (Bezaire and Soltesz, 2013; Freund and Buzsáki, 1998; Klausberger and Somogyi, 2008), and recent transcriptomic studies point to a seemingly complex heterogeneity among pyramidal neurons, with at least 15 classes being identified purely based on gene expression data (Yao et al., 2021). At present it is unclear how these distinct cell types contribute to neural activity during behavior. As test ground for our technique, we focused on a well-defined dimension of pyramidal cell diversity, namely along the radial (deep-superficial) axis of the dorsal CA1 (deep, closer to stratum oriens; superficial, closer to stratum radiatum). Indeed, a growing body of evidence indicates that the anatomical location of the neurons along this axis correlates with in vivo activity patterns (Cohen et al., 2017; Danielson et al., 2016; Fattahi et al., 2018; Geiller et al., 2017; Li et al., 2017; Mizuseki et al., 2011; Sharif et al., 2021; Valero et al., 2015; for review, see Preston-Ferrer and Burgalossi, 2018; Soltesz and Losonczy, 2018). We thus took advantage of the expression of Calbindin (Calb1) – which is selective for superficially located pyramidal neurons – for testing our juxtacellular opto-tagging procedures in freely behaving mice. We found that, while animals explored a familiar environment, Calb1-negative pyramidal neurons were preferentially recruited into the place cell map, while Calb1-positive pyramidal cells were only weakly spatially modulated. These data are thus consistent with the emerging idea of an asymmetric recruitment of CA1 pyramidal cells types into the hippocampal representation.

To genetically target Calb1-positive CA1 pyramidal neurons, we took advantage of the Calb1^cre driver line (Daigle et al., 2018; Nigro et al., 2018). As expected from the distribution of Calb1-positive neurons within the CA1 pyramidal layer, injections of AAV-CAG-flex-GFP in Calb1^cre mice resulted in selective labeling of superficial pyramidal cells (Figure 1A) along with scattered, putative interneurons located primarily within the stratum oriens (not shown).

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Juxtacellular opto-tagging data, average spike latencies (source data for panels E and F).

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To record the activity from single Calb1-positive CA1 pyramidal neurons, we selectively expressed ChR2 in Calb1-positive CA1 neurons by injecting AAV-hSyn1-flex-ChR2-mCherry in the dorsal CA1 of Calb1^cre mice. We then employed the juxtacellular opto-tagging configuration (also known as ‘optopatcher’; Katz et al., 2013; Muñoz et al., 2014) – consisting of a 50-μm-core diameter optic fiber, connected to a blue laser source, placed inside the glass electrode (Figure 1B) – for recording single CA1 neurons in anesthetized animals. Once a recording was established from a putative CA1 pyramidal neuron, low-power (0.1–1 mW) brief blue light pulses (5 ms) were delivered via the optic fiber to test for light-evoked spike responses. Subsequently, the neuron was labeled via juxtacellular procedures. One representative recording is shown in Figure 1C–E. This neuron was located in the superficial pyramidal layer and its apical dendrite displayed a proximal branching point (see Figure 1C) – a characteristic morphological correlate of superficial CA1 pyramidal neurons (Bannister and Larkman, 1995; Li et al., 2017). This neuron was confirmed to be positive for the marker Calb1 as well as ChR2 (Figure 1C). Indeed, blue light pulses could reliably drive short-latency spikes (mean latency = 2.1 ± 0.12 ms; Figure 1D, E) thus confirming the Calb1-positive identity of the recorded cell.

Altogether, we recorded and labeled 19 neurons in anesthetized mice. In all cases where short-latency spiking responses were observed (mean latency <4 ms; Figure 1F) the neurons were confirmed to be Calb1-positive by immunohistochemical analysis (n = 10), therefore validating our opto-tagging strategy. Seven additional nonresponsive neurons were also juxtacellularly labeled and confirmed to be Calb1-negative pyramidal cells (not shown). In two cases, we recorded the activity of light-responsive, narrow-waveform neurons (spike peak-to-trough <0.4 ms, see Ding et al., 2020; Preston-Ferrer et al., 2016) which were positive for Calb1 immunoreactivity, and were thus classified as putative Calb1-positive interneurons (see Figure 1—figure supplement 1A–C; these neurons were not included in further analysis).

One known challenge of optogenetic tagging experiments is to reliably distinguish directly versus indirectly excited neurons (e.g., Beyeler et al., 2016; Zutshi et al., 2018). The short latency of our light-responsive neurons (Figure 1F) together with post hoc validation by juxtacellular labeling (Figure 1C), provide strong support for direct light activation. To further support these observations and benchmark our opto-tagging strategy, we tested our approach with presynaptic input activation. To this end, we injected AAV-CAG-ChR2-mCherry in the CA3 of wild-type mice (Figure 2A). By these means, we obtained strong labeling of CA3 axon terminals (Schaffer collaterals), extending into the CA1 strata radiatum and oriens (Figure 2B). We then juxtacellularly recorded from single postsynaptic CA1 pyramidal neurons while simultaneously activating presynaptic CA3 terminals. A representative recording from a light-responsive identified CA1 pyramidal neuron is shown in Figure 2B–D. In this neuron, photostimulation at ~1.3 mW was sufficient for inducing suprathreshold spiking (Figure 2C), although with lower efficiency and longer latencies (7.4 ± 0.8 ms) as compared to the directly light-activated neurons (see Figure 1C–F). At the population level, the response latencies of indirectly light-activated CA1 neurons were significantly longer than those of directly light-activated CA1 Calb1-positive cells (direct light activation, mean latency = 2.7 ± 0.3 ms, n = 10; indirect light activation, mean latency = 7.0 ± 1.8 ms, n = 11; p = 0.0010; Figure 2E), thereby further validating our latency-based criteria for cell-type classification.

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Juxtacellular opto-tagging data, average spike latencies (source data for panels D,E and I,J).

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The above data indicate that synaptically activated neurons can be reliably discriminated from directly light-activated cells, at least when ‘weak and unreliable’ synapses – like typical cortical synapses, including Schaffer terminals (Csicsvari et al., 2000; Sayer et al., 1990; Sayer et al., 1989) – are activated. As additional challenge, we next asked whether our latency-based criteria would still be valid under conditions of ‘strong and reliable’ synaptic activation. To this end, we performed a subset of experiments on the hippocampal mossy fiber synapse, which is among the strongest and most reliable connections in the nervous system (Henze et al., 2002; Neubrandt et al., 2018; Vyleta et al., 2016). We selectively expressed ChR2 in dentate gyrus granule cells by injecting AAV-hSyn1-flex-ChR2-mCherry (or AAV-hSyn1-flex-oChIEF-TdTomato) in the dentate gyrus of Calb1^cre mice (Figure 2F). By these means, we obtained strong labeling of presynaptic mossy fibers terminals, extending into the CA3 field (Figure 2F, G). We then juxtacellularly recorded from single postsynaptic CA3 pyramidal neurons while simultaneously activating presynaptic mossy fibers at different light stimulation frequencies (e.g., 3–40 Hz; see details in Materials and Methods). Two representative recordings from a nonresponsive and a responsive identified CA3 pyramidal neuron are shown in Figure 2G–I. In the first neuron, no spiking activity was observed upon light illumination; however, a prominent light-induced facilitation of local field potential amplitude was observed – consistent with the known facilitation of neurotransmitter release at mossy terminal (Capogna, 1998; Vyleta et al., 2016; Zucca et al., 2017). In the second neuron, photostimulation was sufficient for inducing suprathreshold spiking (Figure 2H), whose probability increased as a function of stimulus number, consistent with presynaptic facilitation of mossy inputs. Notably, the response latencies of synaptically activated CA3 neurons (mean latency = 8.1 ± 2.4 ms, n = 7) were significantly longer than that of directly light-activated CA1 Calb1-positive cells (Figure 2J).

Altogether, this dataset shows the technical feasibility of combining the opto-juxtacellular approach for single-cell recording, labeling, and presynaptic input manipulations. Moreover, the longer spike latencies in indirectly activated neurons compared to the directly activated ones (Figure 2J) further corroborate the validity of our latency-based opto-tagging approach.

Based on these results, we took advantage of the juxtacellular opto-tagging approach for assessing the in vivo activity patterns of Calb1-positive and Calb1-negative CA1 pyramidal neurons. To this end, we adapted juxtacellular opto-tagging procedures (Figure 1) to freely moving mice. Animals were implanted with miniaturized components (see Figure 3A; Diamantaki et al., 2018) and a 50-μm-core optic fiber was inserted within the glass electrode. The use of a reversible fiber fixation strategy, together with a mechanical micropositioning system (see Figure 3—figure supplement 1; details in Materials and Methods) allowed us to perform multiple recording/opto-tagging trials within individual animals.

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Electrophysiological properties of Calb1-positive and Calb1-negative neurons (source data for panels I-K and Figure 3—figure supplement 2K).

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Using these juxtacellular opto-tagging procedures, we monitored the activity of single CA1 pyramidal neurons while mice explored a familiar maze. All neurons included in the present study displayed low firing rates (<10 Hz; average firing rate, 2.16 ± 2.06 Hz; n = 54) and often fired complex spikes (average burst index, 0.28 ± 0.18, n = 45) – features classically associated with principal cell identity (O’Keefe and Dostrovsky, 1971; Ranck, 1973). All histologically identified neurons were classified as pyramidal neurons (see Materials and Methods), thus further confirming our electrophysiological classification criteria.

As in the anesthetized dataset (Figure 1), neurons recorded in behaving mice were classified as Calb1-positive if short-latency spikes (<4 ms) were reliably evoked by the light pulse (n = 15; Figure 3B). In a subset of recordings, juxtacellular labeling was performed and the cytochemical identity of the recorded neuron was assessed via Calb1 immunoreactivity (Figure 3C–H). Neurons were classified as Calb1-negative neurons (n = 39) if they did not respond to the light stimulus or were located in the deep pyramidal layer and were negative for Calb1 expression (see Materials and Methods). In two cases, we recorded from light-responsive, putative Calb1-positive interneurons, which fired at high rates during exploration (>10 Hz average firing rates; see Figure 1—figure supplement 1D–I; these neurons were not included in further analysis).

During spatial exploration, Calb1-positive and Calb1-negative pyramidal cells displayed similar average firing rates (Calb1-positive, 2.17 ± 2.02 Hz, n = 15; Calb1-negative, 2.15 ± 2.10 Hz, n = 39; p = 0.86) and tendency to fire spike bursts (‘burst index’; Calb1-positive, 0.29 ± 0.21, n = 13; Calb1-negative, 0.29 ± 0.18, n = 32; p = 0.97). However, these two cell types differed remarkably in the degree of spatial modulation. While the large majority of Calb1-negative neurons displayed spatially localized firing patterns, which were homogeneously distributed over the available space (Figure 3I), the spiking activity of Calb1-positive neurons was less spatially modulated (Figure 3J) (n = 26 Calb1-negative and n = 12 Calb1-positive neurons included in the spatial analysis; see details in Material and Methods). To quantify these differences, we computed spatial information content (Skaggs et al., 1993) and the sparsity index – a metric of how compact the place field is relative to the recording arena (Jung et al., 1994). Indeed, we found that Calb1-positive neurons conveyed significantly less spatial information (Calb1-positive, 0.65 ± 0.35 bits/spike, n = 12; Calb1-negative, 1.56 ± 0.79 bits/spike, n = 26; p = 0.0003) and displayed a higher sparsity index (Calb1-positive, 0.53 ± 0.15, n = 12; Calb1-negative, 0.31 ± 0.17, n = 26; p = 0.0003) (Figure 3K), indicating a more diffuse firing pattern compared to Calb1-negative cells. To further confirm these observations, we performed a spatial stability analysis by calculating the Pearson’s correlation coefficient for rate maps computed for the two halves of each recording (Figure 3—figure supplement 2). Indeed, in line with their weaker spatial tuning (Figure 3K), spatial firing in Calb1-positive neurons was significantly less stable than in Calb1-negative cells (mean correlation coefficient, Calb1-positive, 0.33 ± 0.35; n = 12; Calb1-negative, 0.68 ± 0.31; n = 26; p = 0.0019; see Figure 3—figure supplement 2). Altogether, these findings are in line with previous observations (Danielson et al., 2016; Geiller et al., 2017; Mizuseki et al., 2011; Oliva et al., 2016; Sharif et al., 2021) and indicate that in the dorsal CA1, place cells are preferentially contributed by deep (Calb1-negative) pyramidal neurons, while Calb1-positive neurons are only weakly spatially modulated.

A mechanistic dissection of neural circuits requires resolving the contribution of individual neuronal elements to in vivo function, neural computation and behavior. Significant experimental efforts have been undertaken, aimed at resolving neuronal diversity within brain circuits. However, due to methodological limitations, it is still largely unknown whether and how the individual neuronal cell classes – typically identified in vitro – contribute to in vivo activity during natural behavior.

The advent of ‘genetic tagging’ has revolutionized neural circuit research. By restricting the expression of ChR2 (or Ca²⁺ indicators) to a specific subset of neurons, it has become possible to link in vivo activity patterns to the underlying neuronal elements. In freely behaving animals, the combination of extracellular recordings with optogenetic tagging (e.g., Anikeeva et al., 2011; Lima et al., 2009) has become a gold-standard approach for neural circuit dissection. However, while the implementation of the ‘opto-tagging’ method is rather straightforward, there are two important limitations to be considered. First, cell-type identification is indirect, since it is inferred from the spiking responses to the light stimuli. While a number of criteria have been proposed for distinguishing directly versus indirectly light-activated neurons, this distinction is often far from trivial, especially within networks with high recurrent connectivity (Beyeler et al., 2016; Roux et al., 2014; Zutshi et al., 2018). The reliability of optogenetic identification is further complicated by the challenge of ‘spike sorting’ during periods of highly synchronous activity – occurring for example during photostimulation – since the superimposition of multiple, co-occurring spike waveforms can make the detection of individual spikes difficult and unreliable (Roux et al., 2014; Stark et al., 2012). While a number of strategies have been proposed for alleviating these problems (Roux et al., 2014; Royer et al., 2010; Stark et al., 2012; Wu et al., 2015), obtaining ‘ground truth’ validation of opto-tagging procedures is particularly challenging, especially in freely behaving animals. A second important limitation is that opto-tagging approaches do not provide access to structural neuronal features – like for example morphology and molecular expression patterns – which are necessary for unequivocal cell-type identification.

In the present study, we aimed at filling these methodological gaps by combining optogenetic tagging with juxtacellular procedures in freely moving mice. This combination of techniques enables targeting of juxtacellular recordings to a genetically predefined cell class; hence, it represents a significant advance over current juxtacellular protocols, where individual neurons are blindly sampled within a target structure (Averkin et al., 2016; Diamantaki et al., 2018; Katona et al., 2014; Lapray et al., 2012; Tang et al., 2014a; Valero et al., 2015). We provide proof-of-principle validation of our method by recording from Calb1-positive pyramidal neurons (Figure 3) in the mouse CA1. In a limited number of cases, we monitored the activity of Calb1-positive interneurons, whose somata were located outside of the pyramidal layer (Figure 1—figure supplement 1). These neurons fired at high rates during spatial exploration, and did not display the ‘fast-spiking’ phenotype which is often associated to perisomatic-targeting CA1 interneurons (‘theta cells’; O’Keefe and Dostrovsky, 1971; Ranck, 1973). This evidence indicates that our juxtacellular opto-tagging approach could facilitate the targeting of sparse neuronal populations – like different subtypes of GABAergic neurons (Bartos et al., 2011; Bartos and Elgueta, 2012; Klausberger, 2009; Klausberger and Somogyi, 2008) – whose blind sampling in freely moving animals, along with post hoc immunohistochemical phenotyping, is known to be very challenging and labor intensive (Averkin et al., 2016; Katona et al., 2020; Lagler et al., 2016; Lapray et al., 2012).

Our combined opto-juxtacellular method also provides several advantages over ‘conventional’ extracellular opto-tagging approaches. First, it enables direct validation of opto-tagging criteria, in that single ChR2-responsive (or nonresponsive) neurons can not only be recorded, but also labeled by juxtacellular procedures, thereby enabling post hoc verification of ChR2 and/or marker expression. Second, the large amplitude juxtacellular spikes (typically >1–2 mV peak-to-peak amplitudes, Herfst et al., 2012; Tang et al., 2014a) allow unequivocal spike identification, thereby providing ‘ground truth’ spiking signals that are not dependent on the performance of spike sorting algorithms, especially during photostimulation periods. Third, post hoc morphological and molecular expression analysis of the recorded/labeled cells provides access to structural features that are typically not accessible with alternative techniques, thereby enabling unequivocal cell-type classification. Moreover, our data also provide proof-of-principle evidence that our juxtacellular recording/labeling procedures can also be used in combination with presynaptic input manipulations (Figure 2). This approach can be particularly useful for mapping synaptic inputs onto identified cell types, like for example the distinct subtypes of CA2 and CA3 pyramidal neurons (Helton et al., 2019; Hunt et al., 2018; Marissal et al., 2012; Raus Balind et al., 2019) or dentate granule cells (Diamantaki et al., 2016; Erwin et al., 2020; Zhang et al., 2020) which have been classified based on dendritic morphological criteria. We envision that the future combination with single-cell stimulation (Diamantaki et al., 2018; Diamantaki et al., 2016), will allow pairing between pre- and postsynaptic activity (Nicoll and Schmitz, 2005; Salin et al., 1996; Toth et al., 2000), thus enabling the study of single-cell plasticity mechanisms, so far limited to simplified preparations, to the intact system during natural behaviors.

While it is technically possible to perform multiple opto-juxtacellular recordings within individual experiments (Figure 3—figure supplement 1), our method still remains laborious and of limited output compared to alternative techniques (e.g., extracellular recordings). Hence, it should not be seen as high throughput, but rather as complementary to existing in vivo recording methods. We envision that opto-juxtacellular datasets – although limited – could still provide a valuable complement to current cell-type classification approaches, for example by offering the possibility of building a classifier, which could then be used for assigning cell identity to ‘blind’ extracellular units (as in e.g. GoodSmith et al., 2017; Tang et al., 2014b).

Recent in vitro and in vivo work has shown that the CA1 pyramidal layer can be subdivided into two sublayers (superficial and deep) according to morphological, molecular, and electrophysiological criteria (Baimbridge and Miller, 1982; Slomianka et al., 2011; Preston-Ferrer and Burgalossi, 2018; Soltesz and Losonczy, 2018). Deep and superficial pyramidal cells have been shown to display distinct in vivo activity patterns relative to local field potential oscillations (Navas-Olive et al., 2020; Valero et al., 2015) and distinct tendencies to express place fields (Danielson et al., 2016; Geiller et al., 2017; Mizuseki et al., 2011; Navas-Olive et al., 2020; Sharif et al., 2021). However, most of the previous in vivo observations were based upon the anatomical assignment of extracellular recording locations (but see Valero et al., 2015; Navas-Olive et al., 2020). As a testing ground for our technique, we recorded, opto-tagged, and labeled single CA1 pyramidal neurons in freely moving mice – thus validating and extending previous observations by mapping functional deep-superficial gradients onto the underlying neuronal elements. We found that Calb1-positive pyramidal neurons were only weakly spatially modulated, while ‘place cells’ were preferentially recruited from the Calb1-negative population (Figure 3). While this finding is in line with previous work (e.g., Danielson et al., 2016; Geiller et al., 2017; Mizuseki et al., 2011; Navas-Olive et al., 2020; Sharif et al., 2021), we acknowledge that it rests on a limited number of observations (Figure 3I–K). Notably, inputs from the medial and lateral entorhinal cortices are known to preferentially target deep and superficial CA1 pyramidal cells, respectively (Li et al., 2017; Masurkar et al., 2017); hence, the weaker spatial selectivity of upstream lateral entorhinal inputs (Hargreaves et al., 2005) might account for the weaker spatial tuning of Calb1-positive CA1 pyramidal neurons.

In summary, we demonstrated the technical feasibility of performing ChR2-assisted juxtacellular recordings in freely moving mice. We envision that this method will be useful for resolving neuronal heterogeneity beyond ‘single-marker’ classification schemes, thus providing a valuable complement to current techniques for structure–function analysis of neuronal circuits during natural behaviors.

Data generated during this study are included in the manuscript and supporting files. Source data files have been provided for all figures, i.e. Figure 1, Figure 2, Figure 3, Figure 1- figure supplement 1, Figure 3- figure supplement 1.

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Author details

1. Lingjun Ding

   1. Institute of Neurobiology, Eberhard Karls University of Tübingen, Tübingen, Germany
   2. Werner-Reichardt Centre for Integrative Neuroscience, Tübingen, Germany
   3. Graduate Training Centre of Neuroscience – International Max-Planck Research School (IMPRS), Tübingen, Germany

   Contribution

   Data curation, Formal analysis, Investigation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-9183-7062

2. Giuseppe Balsamo

   1. Institute of Neurobiology, Eberhard Karls University of Tübingen, Tübingen, Germany
   2. Werner-Reichardt Centre for Integrative Neuroscience, Tübingen, Germany
   3. Graduate Training Centre of Neuroscience – International Max-Planck Research School (IMPRS), Tübingen, Germany

   Contribution

   Investigation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

3. Hongbiao Chen

   1. Institute of Neurobiology, Eberhard Karls University of Tübingen, Tübingen, Germany
   2. Werner-Reichardt Centre for Integrative Neuroscience, Tübingen, Germany
   3. Graduate Training Centre of Neuroscience – International Max-Planck Research School (IMPRS), Tübingen, Germany

   Contribution

   Formal analysis, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-0025-4380

4. Eduardo Blanco-Hernandez

   1. Institute of Neurobiology, Eberhard Karls University of Tübingen, Tübingen, Germany
   2. Werner-Reichardt Centre for Integrative Neuroscience, Tübingen, Germany

   Contribution

   Formal analysis, Validation, Visualization

   Competing interests

   No competing interests declared

5. Ioannis S Zouridis

   1. Institute of Neurobiology, Eberhard Karls University of Tübingen, Tübingen, Germany
   2. Werner-Reichardt Centre for Integrative Neuroscience, Tübingen, Germany
   3. Graduate Training Centre of Neuroscience – International Max-Planck Research School (IMPRS), Tübingen, Germany

   Contribution

   Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-4032-3723

6. Robert Naumann

   1. Chinese Academy of Sciences, Key Laboratory of Brain Connectome and Manipulation, The Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Nanshan, China
   2. Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, China

   Contribution

   Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

7. Patricia Preston-Ferrer

   1. Institute of Neurobiology, Eberhard Karls University of Tübingen, Tübingen, Germany
   2. Werner-Reichardt Centre for Integrative Neuroscience, Tübingen, Germany

   Contribution

   Conceptualization, Funding acquisition, Project administration, Supervision, Writing - original draft

   For correspondence

   patricia.preston@cin.uni-tuebingen.de

   Competing interests

   No competing interests declared

8. Andrea Burgalossi

   1. Institute of Neurobiology, Eberhard Karls University of Tübingen, Tübingen, Germany
   2. Werner-Reichardt Centre for Integrative Neuroscience, Tübingen, Germany

   Contribution

   Conceptualization, Funding acquisition, Project administration, Supervision, Writing - original draft

   For correspondence

   andrea.burgalossi@cin.uni-tuebingen.de

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-0039-3599

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