Heterogeneous side effects of cortical inactivation in behaving animals

  1. Ariana R Andrei
  2. Samantha Debes
  3. Mircea Chelaru
  4. Xiaoqin Liu
  5. Elsa Rodarte
  6. John L Spudich
  7. Roger Janz
  8. Valentin Dragoi  Is a corresponding author
  1. Department of Neurobiology and Anatomy, McGovern Medical School, University of Texas, United States
  2. Department of Neurology, McGovern Medical School, University of Texas, United States
  3. Center for Membrane Biology, Department of Biochemistry and Molecular Biology, McGovern Medical School, University of Texas, United States


Cortical inactivation represents a key causal manipulation allowing the study of cortical circuits and their impact on behavior. A key assumption in inactivation studies is that the neurons in the target area become silent while the surrounding cortical tissue is only negligibly impacted. However, individual neurons are embedded in complex local circuits composed of excitatory and inhibitory cells with connections extending hundreds of microns. This raises the possibility that silencing one part of the network could induce complex, unpredictable activity changes in neurons outside the targeted inactivation zone. These off-target side effects can potentially complicate interpretations of inactivation manipulations, especially when they are related to changes in behavior. Here, we demonstrate that optogenetic inactivation of glutamatergic neurons in the superficial layers of monkey primary visual cortex (V1) induces robust suppression at the light-targeted site, but destabilizes stimulus responses in the neighboring, untargeted network. We identified four types of stimulus-evoked neuronal responses within a cortical column, ranging from full suppression to facilitation, and a mixture of both. Mixed responses were most prominent in middle and deep cortical layers. These results demonstrate that response modulation driven by lateral network connectivity is diversely implemented throughout a cortical column. Importantly, consistent behavioral changes induced by optogenetic inactivation were only achieved when cumulative network activity was homogeneously suppressed. Therefore, careful consideration of the full range of network changes outside the inactivated cortical region is required, as heterogeneous side effects can confound interpretation of inactivation experiments.


Determining causal relationships between neuronal circuits and behavior represents a fundamental goal of systems neuroscience. Establishing causality, and not just correlation, is difficult and is typically done by externally modulating neural activity, that is, using optogenetics, thermal cooling, or pharmacological agents, and observing the effects on behavior. In a typical cortical inactivation experiment, a particular neural population is silenced, and if a behavioral impairment is observed, that specific population is believed to be causally involved in modulating that specific behavior. Cortical neurons, however, are embedded in densely interconnected local networks spanning hundreds of microns (Douglas and Martin, 2004; Hirsch and Martinez, 2006; Stettler et al., 2002). These local connections are crucial for contextually modulating neural responses, and they underlie canonical cortical computations such as divisive normalization (Carandini and Heeger, 2011) and surround suppression (Adesnik et al., 2012; Angelucci et al., 2017; Trott and Born, 2015). It is unknown how focal suppression influences activity in the local network (Figure 1A).

Figure 1 with 1 supplement see all
Optogenetic suppression yields heterogeneous responses in the distal network.

(A) Left, glutamatergic neurons were targeted using a lentivirus construct, containing the gene for GtACR2, under the control of an α-CamKII promoter. Right, optogenetic suppression of a focal neural population may result in a spread of suppression across the network (‘Suppression only’) or in a heterogeneous response in the network (‘Mixed effects’). (B) Immunohistochemical analysis performed on biopsied tissue from one monkey, after experiments were complete, confirmed that expression of green fluorescent protein (GFP)-tagged GtACR2 was confined exclusively to excitatory neurons. White arrowheads indicate neurons immuno-positive for NeuN (pan-neuronal marker, top left panel), α-CamKII (glutamatergic neuron marker, top right panel), and GFP (GtACR2 marker, bottom left panel). Right, bar graph shows counts for immuno-positive cells counted across five separate sections. No inhibitory neurons (NeuN+/CamKII-) were found to be positive for GFP. Scale bar is 25 μm. (C) Animals viewed 300-ms contrast-varying oriented gratings on a computer monitor. Optogenetic suppression was present on 50% of trials in a randomly interleaved manner. Light duration was 300 ms and was synchronized with stimulus presentation. (D) Electrophysiological recordings using laminar electrodes were made either proximal to the fiber optic location or at distal sites ~300 μm away. (E) Neural responses near the light source. Population responses (n = 48) with (blue trace) and without (black trace) laser activation of GtACR2, while animals fixated. Horizontal black bar shows time of light on. Error envelopes show sem. (F–I) Examples of neural responses recorded distal from the fiber optic, across four visual stimulus conditions (columns). Columns are arranged with increasing stimulus contrast (0%, leftmost column, to 100%, rightmost column). Black traces show the mean firing rate on control trials. Colored traces show mean firing rates on laser trials. Horizontal black line shows timing of the visual stimulus and light on; red arrowheads mark regions of interest.

In principle, silencing a neural population could likely cause substantial activity disruption in the neighboring connected network. In small-brained animals, such as rodents and fruit flies, it is physically possible to silence an entire cortical area with a small amount of light (Li et al., 2019; Mauss et al., 2017). However, for deep-brain structures, where light penetration is more difficult, or in animals with larger brains, such as monkeys, complete inactivation of a brain region is often not possible (Acker et al., 2016; Galvan et al., 2017; Gerits et al., 2012). In these latter scenarios, off-target effects of inactivation manipulations must be considered when interpreting behavioral results since nearby neurons are equally likely to contribute to any observed behavioral changes. Such off-target network effects are particularly troublesome for studies that do not record neural activity during light-induced suppression, but rather assume that the net impact on the targeted network is equivalent to the effect of the opsin on an individual cell. However, despite the importance of these issues, the indirect effects of optogenetic silencing on the neighboring neural population activity and on animal’s behavior are largely unknown.

Here, we explicitly examined off-target, network-mediated effects through population recordings of neural activity across the cortical layers of primary visual cortex (V1) of behaving monkeys while optogenetically inactivating a local population of excitatory cells using the suppressive opsin GtACR2 (Figure 1A). We found that optogenetic suppression unmasks highly heterogeneous effects in the lateral columnar networks ranging from full suppression to full facilitation. Furthermore, the degree of heterogeneity of indirect network effects following optogenetic inactivation varies as a function of cortical layer. A computational model indicates that these diverse responses can be explained by differences in the strength of local intracortical inputs during distal inactivation, and are likely due to the same circuitry that mediates normalization processes, such as contrast gain control. Finally, we show that behavioral performance is consistently impaired only when the net activities of both the on- and off-target sites are uniformly suppressed, while heterogeneous off-target activity resulted in heterogeneous behavioral changes. Such off-target, network-mediated effects provide a viable explanation for the diversity of results reported in optogenetic studies in non-human primates (Tremblay et al., 2020).


Optogenetic suppression of V1 responses

To examine whether focal silencing of a population of V1 neurons leads to uniform or heterogeneous off-target responses in the neighboring local network, GtACR2, a chloride-conducting channelrhodopsin, was expressed in populations of glutamatergic cells (see ‘Materials and methods’). GtACR2 has been shown to be more sensitive to light and produce stronger hyperpolarizing currents than other inhibitory opsins (Govorunova et al., 2015). To our knowledge, this is the first usage of this opsin in the monkey brain. To quantify the specificity of gene expression using this novel construct, we developed a novel biopsy technique to extract cortical tissue for immunohistochemical analysis (see ‘Materials and methods’). Gene expression was robust and specific to neurons expressing glutamatergic marker α-CamKII (Figure 1B), with 97% of cells positive for α-CamKII also positive for green fluorescent protein (GFP, co-expressed with GtACR2), and no neurons (NeuN+, a pan-neuronal nuclear antigen marker) that were negative for α-CamKII and positive for GFP (corresponding to inhibitory cells).

Monkeys performed a contrast detection task in which optogenetic suppression was randomly interleaved on 50% of trials (Figure 1C, see ‘Materials and methods’). Extracellular recordings were made using multi-contact laminar electrodes and light was emitted by an independently movable fiber optic (Figure 1D). We positioned the fiber optic such as to inactivate the superficial layers of V1 (Andrei et al., 2019) and measured the amount of direct suppression (the light source was coupled to the recording electrode; Figure 1D–E). As expected, light activation reduced firing rates by 24.9% ± 2.1% compared to control trials (mean ± sem, over the entire 300 ms light stimulation interval; 18.1 ± 2.1 sp/s control trials, 11.6 ± 1.4 laser trials, p = 0.0064, Wilcoxon rank sum test, n = 41). The direct suppression was long-lasting, and responses returned to baseline 387.9 ms ± 38.7 ms after light offset (median ± sem), consistent with previously measured kinetics of GtACR2 (Govorunova et al., 2015).

Heterogeneous network side effects during optogenetic inactivation

Next, we examined the off-target effects of optogenetic inactivation by measuring the changes in neural responses away from the light stimulation site (Figure 1D, left). This was done by separating the electrode from the light source by a fixed distance, ~300 μm, around the stimulation site, using a custom grid placed inside the recording chamber. This allowed us to assess whether the nearby columnar networks that were not directly inactivated by light undergo changes in population activity. We recorded a total of 214 units across 21 sessions (both single- and multi-unit responses were included: 127 units from Monkey 1 and 87 units from Monkey 2), with statistically significant differences in firing rate during laser trials compared to control (no laser) trials at one or more visual contrast conditions (Wilcoxon ranked sum test, p<0.005). Although firing rates of neurons did not change in the blank 0%, contrast condition (Figure 1F–I; laser vs control trials), the stimulus-triggered responses were highly heterogeneous. The changes in neural responses varied between robust suppression (Figure 1F) and excitation (Figure 1I), or mixed effects at various contrasts (Figure 1G–H). The lack of suppression in the blank condition indicates these responses cannot be due to direct activation of GtACR2 at the distal site. We further confirmed that both direct silencing (Figure 1E) and heterogeneous responses in the local network (Figure 1F–I) are present simultaneously by performing additional recordings using two laminar electrodes, one coupled to the light source, and the other positioned hundreds of microns away (Figure 1—figure supplement 1A-K). Further, to assess whether the suppressive responses (such as those in Figure 1F) at the distal site were not due to a small amount of scattered light originating from the fiber optic, we measured the suppression latency on laser trials in the presence of visual stimulus (Figure 1—figure supplement 1L,M).

As expected from a network-mediated effect, the suppressed cells at the off-target site took significantly longer to exhibit response inactivation (92.9 ± 1.1 ms, mean ± sem) compared to the cells at the direct site (31.2 ± 1.2 ms; p = 0.0007, Wilcoxon ranked sum test). These results demonstrate that local optogenetic suppression results in largely heterogeneous effects across neurons in the vicinity of the directly suppressed cell population. To quantify the diversity of light-induced responses, we first measured the contrast-evoked spike counts for each light-responsive neuron for laser and control trials (see ‘Materials and methods’). Next, we fitted the contrast responses with an implementation of the normalization equation tailored for optogenetics experiments (Nassi et al., 2015; Sato et al., 2014):

(1) Rc=Rm*Ro+cn+PC50+cn+Q

where R is the modeled response to visual stimulus contrast (c), Rm is the maximum firing rate, Ro is the baseline firing rate, n is the neuron’s sensitivity to contrast, C50 is the semi-saturation constant, and P and Q represent the extent to which the local network provides activation and divisive suppression, respectively. The normalization model provided good fits for the observed contrast responses (Figure 2—figure supplement 1B, C) and was able to capture the diversity of light-modulated neural responses (Figure 2A). Across the population, there was a continuum of responses (Figure 2A and C), but four basic response patterns clearly emerged following optogenetic inactivation of the nearby network (see the example cells in Figure 2A and population responses in Figure 2B; see also Figure 2—figure supplement 1A).

Figure 2 with 3 supplements see all
Four response motifs observed across cortical layers.

(A) Representative examples for the four distinct response patterns observed. Points show mean ± standard error of firing rates scaled relative to the maximum across trials. Solid lines show normalization model fits to data points. Black points/lines show basic responses. Colored points/lines show responses during optogenetic suppression of local network. (B) Population contrast responses for each response type. *p<0.05, Wilcoxon signed rank test, with false discovery rate correction. Insets show average waveforms from all cells. (C) Response types across the population. Heatmap shows differences in firing rate (laser minus control) as predicted by normalization fits for each recorded neuron. Types were classified based on the model fit pattern and ordered based on the mean change in firing rate between laser and control trials across contrasts. (D) Example current-sink density estimate for one session, used to assign layer identity. Black horizontal bar shows the time of stimulus presentation. Dashed horizontal black lines show layer boundaries (see ‘Materials and methods’). (E) Distributions of response types per layer as a percent of all cells recorded in each layer across all sessions. Black vertical line represents the median. Edges of boxes represent the 25th and 75th percentiles. Dashed lines represent the range. Red crosses represent outliers. Inset shows the percent of each response type within each layer. (F) Proportion of each response type within each layer. (G) Results of statistical comparisons of distributions shown in panel (E) (Kruskal-Wallis test, post-hoc Tukey test).

To categorize individual cell responses into specific types, two converging approaches were used, utilizing both the normalization fits and the raw firing rates across contrasts (Figure 2—figure supplement 1A) for all light-responsive neurons. For each neuron, contrast response differences between laser and control conditions were assigned to one of the four possible categories based on visual inspection. For example, cells that showed a decrease in firing rate at most contrasts on laser compared to control trials were tentatively classified as Type 1. Next, the differences in the normalization fits between the laser and control conditions were algorithmically classified into four classes based on the average difference at low contrasts (1% to c50 of each cell) and at high contrasts (90–100%). These two methods converged on the same result for 88.3% of the cells. For disagreements, the category that best matched the firing rate pattern was chosen. To validate that these four response categories were representative of the light-evoked firing rate changes of the cell populations and not an artifact of the classification procedure, we used two additional methods. First, we used a clustering algorithm (uniform manifold approximation and projection) (Meehan et al., 2021) to group the firing rate differences (laser minus control) across contrasts for all cells (Figure 2—figure supplement 1D, E). This method produced four distinct clusters for the actual data (Figure 2—figure supplement 1D), but not for the randomly labeled data (Figure 2—figure supplement 1E). Second, a linear discriminant classifier (Figure 2—figure supplement 1F) correctly classified firing rates into the ascribed response types with an overall accuracy of 74.3% ± 2.6% (mean ± sem across five training and validation implementations of the classifier), which was well above chance 33.2% ± 1.9% (p = 1.34 × 10–6, t-test, two-way, unpaired, n = 5).

Lastly, since our experimental design focused on the low-contrast range of visual stimuli (where behavioral differences are more evident), we verified that the normalization model fit with five pre-dominantly low contrasts produced comparable results to fitting with more numerous, evenly spaced contrasts. To do this, we conducted additional experiments using nine stimulus contrasts (Figure 2—figure supplement 2A-D) and recorded the evoked responses of 54 additional units. We fit neuron responses using all nine contrasts and a subsample of five contrasts (with a similar range as the optogenetics experiments). There was a high degree of correlation between the measured c50 and the slope of the fits based on nine vs five contrasts (Pearson R = 0.89, p<0.001), demonstrating that the normalization model parameters based on five contrasts were sufficient to capture close to the ‘true’ contrast response parameters of each cell.

Using this classification strategy in the distal network, we found that a significant fraction of neurons exhibited suppression across all visual stimulus contrasts (labeled Type 1, 42.5% of all light-modulated cells). However, the majority of light-responsive neurons (57.5%) in the distant network exhibited either mixed effects or facilitation—Type 2: facilitation at low contrasts and suppression at high contrasts (28.5% of cells); Type 3: suppression at low contrasts and facilitation at high contrasts (20.1% of cells); and Type 4: facilitation across all contrasts (8.9% of cells).

Response heterogeneity is not due to direct optogenetic suppression

In addition to the latency analysis described above (Figure 1—figure supplement 1L, M), there are several other reasons why the distal effects we have revealed cannot be due to direct optogenetic suppression. First, direct suppression caused by light activation of GtACR2 channels has a very characteristic pattern, comprising a strong hyperpolarization (or decreased firing rate) that persists hundreds of milliseconds after light offset. This has been demonstrated in vitro (Govorunova et al., 2015) and confirmed in our in vivo data (Figure 1E and Figure 1—figure supplement 1C). These long-lasting responses are only found at recording sites adjacent to the light source, but never at the distal recording sites. Second, the distal recording sites are located well within the area targeted by the virus injections. Injections were spaced across the cortical surface within a rectangular area spanning 1.0 × 0.7 mm (four horizontally spaced columns, with 1 µl × 5 vertically spaced injections in each column; see ‘Materials and methods’). Given that 1 µl of a lentiviral suspension has been shown to transfect cells within 1 mm3 (Han et al., 2009), glutamatergic neurons at the distal site would also be expected to express GtACR2 and should be suppressed if exposed to light. This is particularly true since GtACR2 is much more sensitive to light than traditional channelrhodopsins (Govorunova et al., 2015). This also means that the distal heterogeneous effects are not due to retrograde transport of the virus, since the neurons at the distal site are located within the injection boundaries. Thus, if light traveled to these distal sites, the transfected cells should exhibit characteristic GtACR2-mediated suppression, and this effect would be most apparent in the absence of a visual stimulus. However, we never observed cells exhibiting the characteristic long-lasting suppression at distal recording sites. Further, neural responses at the distal sites had no significant light-induced modulation in the absence of the visual stimulation (Figure 2B, Figure 2—figure supplement 1A; the 0-contrast condition). This latter finding is consistent with the network-based modulation activated by the visual stimulus, but cannot be attributed to direct activation of GtACR2 channels by stray light. Lastly, blue light (473 nm wavelength) is known to scatter rapidly in brain tissue (Diester et al., 2011), with virtually no light penetrating laterally beyond the collimated area (Li et al., 2019). We confirmed the absence of direct light activation at the distal site by performing experiments with simultaneous recordings at both the light-application site and a second distal site (Figure 1—figure supplement 1). In these experiments, we confirmed that direct, long-lasting suppression, regardless of stimulus contrast, was only found along the electrode adjacent to the light source (Figure 1—figure supplement 1C, n = 12). Despite being located within the injection-targeted area, not such characteristic, direct-light-mediated responses were found at the distal sites. The responses along the distal probe were only of the stimulus-dependent, heterogeneous variety (Figure 1—figure supplement 1B, n = 5; n = 3 Type 2 and n = 2 Type 3 response motifs).

Laminar distribution of response types

We next asked whether there is a laminar specificity to the different response type patterns induced by the suppression of the nearby cell population in the superficial layers. In principle, local intracortical connections could be involved in transmitting the direct optogenetic suppressive effects observed at the light stimulation site to nearby cortical columns. However, since local connections are distance-dependent, the suppression of cortical responses at the light stimulation site could result in direct suppression in the superficial layers of adjacent microcolumns, whereas the effects at larger depths could be mixed.

To measure these effects, we identified V1 layers using a standard current-sink density (CSD) analysis (Hansen et al., 2012) based on the local field potentials recorded in response to a large (5°) full-contrast dynamic visual stimulus presented for 1600 ms (Figure 2D, see ‘Materials and methods’). A clear laminar profile could be identified in 15/21 sessions. Allocating units in these sessions to their respective layers (n = 24, 51, 80 for supragranular (SG), granular (G), and infragranular (IG), respectively) reveals different proportions of response types across layers (Figure 2E–G). Specifically, Type 1 responses dominate in SG, while all types are evenly distributed in G and IG (Figure 2E and Figure 2—figure supplement 3). Type 1 (purely suppressed) units were evenly distributed throughout all layers, while Type 2, 3, and 4 responses were primarily found in G and IG. These cumulative population results were confirmed by subsequent analyses restricted to sessions in which light-modulated units were simultaneously recorded across all three laminar divisions (n = 8 sessions, Figure 2—figure supplement 3). Further, given the considerable variability in the thickness of the granular layer across anatomical studies (ranging between 360 and 580 microns from our literature survey), to check whether our 400-µm granular layer convention impacted the laminar distributions of response types, we also redefined granular as spanning 300 µm. However, this did not substantially alter our previous results (Figure 2—figure supplement 3C, D).

Response heterogeneity can be explained by variations in network connectivity

Critically, all four response types were found simultaneously within individual recording sessions, when visual stimulus properties and light delivery parameters were identical (Figure 3A). This means that the overall diversity of responses cannot be explained by subtle differences in experimental parameters that can vary across sessions. All sessions with multiple simultaneously recorded neurons had more than one response type (data from individual sessions are shown in Figure 3—figure supplement 1).

Figure 3 with 2 supplements see all
| Response type distributions are stable across sessions and subjects.

(A) Proportion of neurons in each category type recorded in each session. Horizontal black line shows the mean across sessions. (B) Proportion of neurons in each category across sessions in each monkey. *p<0.05, Kruskal-Wallis test, post-hoc Tukey test. (C) Sample-size effect. Sessions with higher overall cell counts have a greater proportion of Type 2–4 cells and fewer Type 1 cells. The y-axis represents the percent of each response type as a function of the total number of cells recorded in each session (n = 22). Lines represent linear fits of each response type. (D–F) Normalization model fit parameters for each response type, showing the divisive (D) and additive (E) components as well as the slope (F). (G) Contrast that elicits 50% of the maximum response (c50), measured from fits. Bars show the mean and standard error of each group. *p<0.05, **p<0.001, Kruskal-Wallis test, with post-hoc Tukey test.

The overall proportions of each response type were consistent within sessions (Figure 3A) and similar across monkeys (Figure 3B; p<0.0001, Kruskal-Wallis test, post-hoc Tukey test; within-type comparisons were not significantly different across monkeys, p>0.05). However, there was some variation in the types of responses observed day-to-day (Figure 3A, Figure 3—figure supplement 1B). Variability across sessions could be due to something trivial, such as sample-size effects (Figure 3C; rarer Type 3–4 responses were more likely to be found in sessions where more cells were simultaneously recorded), or due to other factors that could vary across sessions (Figure 3—figure supplement 1). To address these latter possible sources of heterogeneity across sessions, we considered whether the patterns of heterogeneity were correlated with four measurable factors. Sessions were organized either according to the proportion of Type 1 responses (Figure 3—figure supplement 1, left column) or in their chronological recording order (Figure 3—figure supplement 1, right column). We then asked whether the distribution of response types could be correlated with cumulative damage to the cortex (as indicated by the chronological order; Figure 3—figure supplement 1F), differences in laminar sampling (Figure 3—figure supplement 1C, H), recording depth (Figure 3—figure supplement 1D, I), or the identity of fiber optic used for light delivery on specific days (Figure 3—figure supplement 1E, J; laser power was the same across sessions). However, none of these factors correlated with heterogeneous response patterns observed in each session.

In primates, stimulus representation is anisotropically represented across the cortical surface. Our acute recordings sampling different neural populations could be located within different network orientation preference contexts of V1 (i.e., the column’s proximity to pinwheel centers would influence the range of orientations represented in a given population). The visual stimulus orientation was optimized for the recorded cell population but could vary for individual cells within a session. We first examined whether the distribution of orientation preferences across neurons varied across sessions. More diversity of orientation preferences would be expected closer to pinwheel centers. However, we found no difference in either the distributions of orientation preferences observed in each session (Figure 3—figure supplement 2A; p = 0.497, t-test, two-way) or the orientation selectivity of the cells (indicative of the sharpness of tuning) across sessions (Figure 3—figure supplement 2B; p = 0.600, t-test, two-way).

Next, we examined the tuning of the response types (Figure 3—figure supplement 2C, D) but found no differences across types with respect to the difference between the cell’s preferred orientation and that of the visual stimulus (Figure 3—figure supplement 2C; p = 0.06, Kruskal-Wallis test). Nor was there a difference across response types in the sharpness of tuning (Figure 3—figure supplement 2D; p = 0.12, Kruskal-Wallis test). Since cells within the suppressed column would also have largely overlapping orientation preferences, differences in orientation preference between the recorded and suppressed cortical columns fail to explain the heterogeneous responses found within sessions. Further, we did not measure any significant difference in population tuning properties across sessions, which would have been indicative of sampling functionally different portions of the V1 network. We conclude that differences in orientation preference do not adequately explain cross-session variability within our data.

Having ruled out the above factors, and given that suppressing the local network consistently produces the four basic simultaneous response types within a column, we reasoned that the response heterogeneity revealed here is most likely due to variations in functional connectivity patterns between the lateral network and individual cells within a distal cortical column. We examined this issue by quantifying the differences in the normalization model fit parameters associated with each response class (Figure 3D–G). Specifically, model parameters P (additive, Figure 3E) and Q (divisive, Figure 3D) represent the input provided by the local network, while n (slope, Figure 3F) and c50 (semi-saturation constant; measured from the fits in Figure 3G; model parameter value in Figure 2—figure supplement 2G) represent the intrinsic stimulus responsivity. Note that P and Q are measured on the trials with optogenetic suppression (‘laser trials’). This analysis revealed distinct stimulus response properties associated with each class. For example, Type 1 cells were sensitive to a broader range of stimulus contrasts (evident by the lower slope, Figure 3F, and higher c50, Figure 3G) and were more strongly modulated by the local network (Figure 3D–E) compared to Type 2 cells. Type 1 cells were also less sensitive to low-contrast stimuli compared to Type 2 or Type 4 cells (evident from the higher c50 value, Figure 3G). The Type 2 class displayed the lowest values for the network inputs (Figure 3D–E) compared to other cell classes. We also quantified the portion of cells in each type that did not exhibit saturating responses at high contrasts (Figure 2—figure supplement 2E, L), and thus maintained sensitivity in that range. These cells comprised the majority of Type 3 responses (54.8%) and were least represented in the Type 2 group (12.2%) (Figure 2—figure supplement 2H, I). Interestingly, the stimulus responses (Figure 3F–G) of Type 1, 3, and 4 classes were statistically similar but varied mostly in their additive and divisive network inputs (Figure 3D–E).

Altogether, these results suggest that intracortical connections control the heterogeneity of responses within the local network. This is surprising for two reasons. First, they reveal that normalization by the local population is not uniformly applied on columnar neurons. That is, individual neurons receive different degrees of normalization from their local network. Second, the presence of four consistent response patterns (Types 1–4) suggests that normalization could come in four ‘motifs’ mediated by four underlying connectivity profiles between columnar neurons and their local network. We further asked what type of intracortical interactions could possibly explain the four response types observed after the optogenetic suppression of neurons located at distal locations.

Heterogeneous response types can be captured by a simple network model

We devised a small-scale firing-rate model in which the response of a model neuron is determined by a linear combination of feedforward excitatory drive and local recurrent excitatory and inhibitory inputs (Figure 4A). The output neuron represents the experimental recording site at the distal location (Figure 1D, left), while the local network represents the activity of the suppressed network at the light source (Figure 1D, right). The optogenetic suppression of the nearby network was modeled as a constant reduction in the firing rates of network excitatory cells (equivalent to a 12.9 sp/s decrease in firing, as observed experimentally; Figure 1E). We tested the impact of two possible factors that could differentially modulate neuronal activity to generate the heterogeneous response types observed experimentally. First, the stimulus sensitivity of the network drive to individual cells could vary, i.e., the different response types could be due to differences in the cumulative contrast response function of the normalization pool impacting each neuron. Here, the sensitivity variable, implemented as a multiplicative gain, refers to the dynamic range of the stimulus response across contrasts (Figure 4A, right). Second, the balance of excitatory and inhibitory currents (‘E/I ratio’) could vary slightly across response types. In our model, the amount of inhibitory synaptic current was determined by scaling the excitatory current by the E/I ratio. Since E/I ratio is tightly controlled in cortical networks, we hypothesized that even minor fluctuations could produce the diversity of observed responses.

Figure 4 with 1 supplement see all
Model replicates heterogeneous responses to optical suppression of local network by altering network stimulus sensitivity.

(A) Cartoon model (left). The response of the output neuron is calculated as a linear summation of a feedforward and a local network component. Feedforward drive is purely excitatory, while the local network is represented as a mixture of excitation and inhibition currents. Inhibition followed excitation according to a fixed ratio (‘E–I ratio’). The gain of the excitatory current is modulated by a multiplicative parameter (‘sensitivity’). (Right) The firing rate of the output neuron as network sensitivity is varied. Solid colored traces show model responses on control trials. Dashed lines represent optogenetic suppression trials. (B–E) Varying the sensitivity of the network input while holding E/I ratio stable produces the four basic response motifs observed experimentally. The parameters used to generate these responses are shown in panels (F, G). (F, G) Differences in the model firing rate between laser and control trials for low-contrast (F) and high-contrast stimuli (G), while varying the local network sensitivity (y-axis) and E/I ratio (x-axis). Overlaid numbers represent the parameter combinations for generating the response types in panels (B–E). (H) Boundaries of parameter space dividing the four response types based on changes in firing rate associated with optogenetic suppression for low (H)- and high (G)-contrast conditions.

Our crucial observation was that modulating local network sensitivity alone (while keeping feedforward input and E/I ratio fixed) can reproduce all four types of responses observed experimentally (Figure 4B–E). In other words, for a given feedforward input (such that from the lateral geniculate nucleus), our model neuron could be coaxed to reproduce all four response motifs when the local network input was suppressed. Which response motif was modeled depended on how strongly the network input was driven by the stimulus. We also tested the effect of altering the sensitivity of the feedforward gain (rather than the local excitatory network gain), but this manipulation did not reproduce all four experimentally observed response motifs (Figure 4—figure supplement 1). Next, we examined the combined effects of altering both the network sensitivity and E/I ratio. Figure 4F–G shows the difference in the model neuron responses between control and laser-suppression conditions across the range of sensitivity and E/I ratio parameters tested, both for the low (Figure 4F)- and high (Figure 4G)-contrast stimuli (the superimposed numbers on the heatmaps correspond to the conditions under which the examples in Figure 4B–E were obtained; ‘1’ refers to model Type 1 in Figure 4B, etc.). The difference in responses between the low (Figure 4F)- and high (Figure 4G)-contrast conditions yielded a feature-space map revealing the combinations of sensitivity and E/I ratios under which various response types can be generated (Figure 4H). We found that for a limited range of E/I ratios (1.95–2), all four response types can be generated solely by altering the local network sensitivity. However, there is no equivalent range of network sensitivity values that could produce all four response types if only the E/I ratio is modulated.

These modeling results indicate that differences in the stimulus sensitivity of network inputs to individual neurons, located within a column, are sufficient to account for the heterogeneous contrast responses experimentally observed following inactivation of the lateral network (Figure 1F–I), but differences in E/I ratios are not. When the network sensitivity is low, Type 1 responses dominate, but as stimulus sensitivity increases, Type 2, 3, and 4 responses emerge. Since Type 1 responses are dominant, the majority of neurons receive network input with relatively low stimulus sensitivity. This is consistent with the standard normalization model, where the normalizing pool consists of a broadly tuned population. Surprisingly, our results demonstrate that the normalization is applied idiosyncratically to neurons within a population and can account for the heterogeneous continuum of experimentally observed responses. One limitation of our simple model is that it does not distinguish between individual network inputs but represents the cumulative synaptic current from the entire local network. Further, our experiments only utilized static oriented gratings of varying contrasts. Thus, it is quite possible that varying other visual features represented in V1 (such as spatial frequency or color) could reveal different patterns of local connectivity.

Relationship between response heterogeneity and behavioral performance

We next asked whether the heterogeneity of neural responses observed during optogenetic suppression of the distal network can influence behavior. The prevalent idea in neuroscience studies is that inactivating sensory cortical responses should yield a marked change, typically suppressive, in behavioral performance. However, our results in Figures 13 raise the issue of whether the diversity of response changes during cortical inactivation could be associated with diverse changes in behavioral performance. We directly examined this issue by training monkeys to perform a contrast detection task (see ‘Materials and methods’) while inactivating cortical responses as previously described (Figure 1C). Specifically, animals detected oriented gratings presented for 300 ms at various contrasts. While the orientation of the stimulus was optimized for the distal recording site, the size of the stimulus was large enough to cover the receptive fields of both the direct and indirect sites, and thus the activity at both sites could contribute to the behavioral choice. Control and laser trials, and contrast conditions were randomly interleaved. Laser stimulation was delivered as one continuous pulse for 300 ms and was synchronized with the visual stimulus.

Across daily sessions and contrast conditions, we found that behavioral performance was significantly changed on 57.9% of laser suppression trials compared to control trials (31.6% impaired and 26.3% facilitated; Figure 5—figure supplement 1A, B). This raises the possibility that these diverse changes could be explained, at least in part, by the diversity of neural responses outside the target inactivated area. To investigate how the changes in behavioral performance correlate with the observed neural activity changes following optogenetic inactivation, we divided sessions based on the proportions of response types within individual sessions, and if more than 50% of the recorded units in a session were categorized as Type 1, we labeled the session as Type 1 session.

First, we examined the sessions containing predominantly Type 1, purely suppressive, responses at distal cortical locations (Figure 5A; p = 2.30 × 10–6, one-way Kruskal-Wallis test, df = 3, Chi-sq = 28.94, post-hoc t-test). Across the 10 sessions dominated by Type 1 responses, we found that detection performance was impaired on laser trials (target reports on laser trials: 9.3 ± 1.9%; control trials: 19.5 ± 5.1%, mean ± sem) for the lowest contrast stimuli (Figure 5B; p = 0.04, t-test, paired, two-way), while detection performance did not change during the blank condition (Figure 5C) or for higher contrasts (p>0.1, Figure 5—figure supplement 1C). The fact that behavioral changes are only observed for the low-contrast stimuli is not surprising and is consistent with previous studies (Afraz et al., 2006; Andrei et al., 2019; Bisley et al., 2001; Ditterich et al., 2003), as sub-optimal stimuli better reveal subtle changes to psychometric curves. Second, we examined the remaining sessions (n = 11) with more balanced proportions of the four response types, with Type 1 responses occurring as often as other response types (Figure 5D; Types 1–3 are uniformly distributed, with more Type 2 than Type 4 cells, p = 0.022, one-way Kruskal-Wallis test, df = 3, Chi-sq = 9.67, post-hoc t-test). However, in the sessions in which V1 responses were highly heterogeneous, detection performance remained unchanged between laser and control trials both for the low (Figure 5E–F; p = 0.40, t-test, paired, two-way) and high contrasts (Figure 5—figure supplement 1D). Importantly, performance on control trials was not significantly different between Type 1 dominant sessions and the remaining sessions at this contrast (p = 0.33, two-way t-test). For a subset of these mixed-type sessions, where Type 2 responses were dominant ( >35% of Type 2 cells recorded, n = 6 sessions; Figure 5—figure supplement 1F, p = 0.0032, one-way Kruskal-Wallis, df = 3, Chi-sq = 13.8, post-hoc t-test), we found a significant impairment in detection performance at the 20% contrast in laser trials compared to control (Figure 5—figure supplement 1E, G, H; p = 0.016, t-test, paired, two-way), consistent with the range of contrasts for which the Type 2 cells are suppressed by light (Figure 2A and B). Furthermore, although strongly facilitated detection performance was often evident (Figure 5—figure supplement 1A, points below the diagonal), facilitated responses were not systematically consistent across sessions and contrasts, and did not meet our statistical criteria for significance. We could not assess the behavioral performance for Type 3 or Type 4 response types because those types were rare and were always outnumbered by Type 1 or Type 2 responses.

Figure 5 with 2 supplements see all
Consistent impairment in behavioral performance occurs only when optogenetic inactivation is associated with homogeneous response suppression in V1.

(A) Proportions of cell response types found in those sessions containing >50% Type 1 responses. Dots show individual sessions. Bar graphs show mean ± sem. **p = 2.30 × 10–6, one-way Kruskal-Wallis test (df = 3, Chi-sq = 28.94, post-hoc t-test). (B) Task performance in Type 1 dominant sessions showing % trials in which animals reported the presence of a visual stimulus on laser (blue) and control (gray) trials. *p = 0.04, t-test, paired, two-way. Session counts per contrast, ascending (left to right), are 10, 7, 10, 9, 3, and 8. Points show mean ± sem. Fits are third-order polynomials. Inset bar graph shows performance difference (laser minus control) across contrasts. (C) Target reports for Type 1 dominant sessions (n = 10) for 0% (open gray circles) and 3.5% (red filled circles) contrast. The unity line (solid gray diagonal) shows ± sem behavior responses across all contrasts in control condition (flanking dashed diagonal lines). (D) Same conventions as in panel (A), but for the sessions with mixed response types; *p = 0.022, one-way Kruskal-Wallis test (df = 3, Chi-sq = 9.67, post-hoc t-test). (E) Same conventions as in panel (B), showing no change in behavioral performance across laser (red) and control (gray) trials. Session counts per contrast, ascending (left to right), are 11, 5, 10, 11, 6, and 9. (F) Same conventions as in panel (C), but for mixed-type dominant sessions (n = 11).

As small changes in eye position could have a significant impact on V1 responses (McFarland et al., 2016), we examined whether the behavioral performance differences between the two types of sessions that we analyzed separately (Figure 5B and E) could be accounted for by significant differences in eye position (Figure 5—figure supplement 2). However, across sessions and stimulus conditions, we did not observe a significant difference in eye position (Figure 5—figure supplement 2A, B, D, E; median p-value = 0.493, Wilcoxon ranked sum test) or pupil size (Figure 5—figure supplement 2C, F; median p-value = 0.498, Wilcoxon ranked sum test) between laser and control conditions. Importantly, there were no statistically significant differences (p>0.1) in eye movements and pupil size between Type 1 dominant and mixed-type sessions. Rather, both fixation locations (see ‘Materials and methods’), eye movements, and pupil size were remarkably stable across sessions. Altogether, these results indicate that consistent behavioral changes were only observed when neuronal responses at the optogenetically targeted site and at the off-target distal sites were both suppressed. When the distal neural population is dominated by heterogeneous changes in firing rates, changes in behavioral choices are mixed and inconsistent across sessions.


Contrary to the commonly held assumption that cortical inactivation has a negligible impact on surrounding cortical tissue, our study reveals highly heterogeneous changes in neural population responses hundreds of microns away. The most surprising effect is the uncovering of response facilitation and mixture of facilitation and suppression in the middle and deep cortical layers of the distal network, with significant consequences for behavioral performance. Since the laminar structure is ubiquitous across the sensory cortex, these results may be broadly applicable across multiple cortical areas and species.

Our study demonstrates that focal optogenetic suppression induces complex changes in the functional properties of nearby neural populations. Contrary to our expectation, cortical inactivation did not result in a simple distance-based decay effect of suppression as a function of proximity to light source. Instead, we found a mix of facilitatory and suppressive effects at distal cortical sites surrounding the directly inactivated region, which were related to changes in behavioral performance. Indeed, the relationship between potential off-target effects of cortical inactivation and behavior has not been previously studied. Previous causal investigations assumed that cortical inactivation silences neurons in the target area while the surrounding tissue is only negligibly impacted. However, our findings that off-target side effects are highly heterogeneous complicate the interpretations of inactivation manipulations, especially when they are related to changes in behavior.

We have shown that behavioral performance is consistently impaired only when the net activity of both on- and off-target sites is suppressive (Figure 5B). In contrast, heterogeneous off-target responses result in heterogeneous changes in behavioral performance (Figure 5—figure supplement 1G). Furthermore, complex changes in responses during suppression of distal cortical activity were predominantly present in deeper cortical layers, and hence studies limiting electrophysiological (Nassi et al., 2015; Sato et al., 2014) and optical recordings (Michel et al., 2018) to superficial layers would underestimate these important network effects. We suggest that the mixed off-target effects found here could possibly explain the highly variable changes in behavioral performance during optogenetic inactivation, particularly observed in non-human primate studies (Dai et al., 2014). Therefore, new strategies need to be developed to limit, or at least monitor, such off-target network effects. One potential approach for future inactivation studies, particularly those involving behavioral measurements, would be the use of a secondary recording electrode distal to the targeted illumination site to record any side effects produced in the local network.

Furthermore, our results provide strong experimental support for prior untested hypotheses that the local network is a source of normalizing drive to cortical neurons. Normalization has been proposed to be one of the canonical computations in the cortex (Carandini and Heeger, 2011); yet it presently lacks a clear mechanism. We have shown here that the local network does indeed provide normalizing inputs to neurons within a column. However, this normalizing input is not uniform, but rather it is idiosyncratically tailored for each neuron. We have shown that the heterogeneous normalizing effects fall along a continuum, with four basic motifs. This diversity of normalizing responses has not been previously addressed. Type 3 and Type 4 response motifs, in particular, with their rather surprising instances of response augmentation at high contrasts, have never been previously described. These four types of responses might serve functionally distinct roles in stimulus encoding. For example, normalization has been shown to stabilize neural responses and thus reduce variability to repeated stimulus presentations (Coen-Cagli and Solomon, 2019). The heterogeneous responses may also reflect the mechanisms by which the network retains sensitivity over the entire range of contrasts, despite the fact that most cells saturate their responses at high contrasts. In other words, although our model is able to generate all four responses solely by modulating the local network inputs to a single cell, it is also possible that the heterogeneity could arise from differences in the intrinsic contrast response properties across cells. Further work is required to understand why the local network modulates neurons within a column so differently.

Heterogeneity of normalization has not been well studied, nor is it well defined for populations of neurons. The denominator of the normalization equation represents the cumulative activity of the local network (or ‘normalization pool’). Since the model is implemented for one neuron at a time, the model is agnostic to whether the normalization pool is the same or different for individual neurons. At the site of light application, Nassi et al., 2015 showed heterogeneous responses, owing to evoked normalization in the network, but because neurons were mostly recorded one at a time, they could comment on the source of the heterogeneity, since all responses were not simultaneously present.

Our experimental results are focused on columnar interactions separated by a short range of about 300 µm distance. However, it is quite possible that some of the heterogeneity is produced by network connections that span much further. Computational studies have found that tuning curves can be sharpened when short-range connections are primarily excitatory and longer-range connections are predominantly inhibitory, creating Mexican-hat-shaped interactions between columns (Ben-Yishai et al., 1995; Spiridon and Gerstner, 2009). It is quite possible that some of the effects recorded at 300 µm distance could be due to activity changes that occurred at a longer distance ( > 500 µm) primarily mediated by long-range inhibitory projections.

The degree of off-target response heterogeneity exhibits session-by-session variability. Indeed, although the types of neuronal responses outside the directly inactivated cortical region were consistent across sessions and monkeys (Figure 3A–B), there was variability in the distribution of response types, that is, some sessions were dominated by Type 1 responses, whereas others contained more mixed response types (Figure 3—figure supplement 1). This variability could be partially attributed to random sampling inherent in acute electrophysiological recordings (Figure 3C; see also Figure 3—figure supplement 1). However, the fact that we observed a significant difference in task performance between the Type 1 dominant sessions relative to the more heterogeneous ‘mixed-type’ sessions strongly suggests that these cross-session variations are due to differences in local circuit connectivity patterns (Chelaru and Dragoi, 2008; Keller and Martin, 2015). Indeed, it has been shown that the responses of nearby neurons located within the same functional column exhibit a high degree of heterogeneity in their receptive field properties (Hubel and Wiesel, 1962; Lee et al., 1998; Ringach et al., 2002), and this heterogeneity has been frequently linked to the heterogeneity of local excitatory and inhibitory intracortical circuits (Chelaru and Dragoi, 2008; Landau et al., 2016; Padmanabhan and Urban, 2010). Additionally, while our model (Figure 4) shows that for a given E/I ratio, all four response types can be generated, excitatory and inhibitory currents can be differentially tuned, and the E/I ratio can vary depending on stimulus orientation (Wilson et al., 2018). In our model, changing the E/I ratio alters the distribution of response types observed (Figure 4H), which could also explain the variations of response types across sessions. This raises the possibility that heterogeneities in the strength of local intracortical inputs to V1 neurons, which control the different response types after optogenetic suppression of the distal network, can explain the ‘cross-session’ variability in response types shown here.

The exact network mechanism involved in generating the response heterogeneity revealed here is unknown. We reasoned that the source of contrast gain modulation at off-target sites after optogenetic inactivation is the change in the strength of local network inputs. In mouse visual cortex, parvalbumin-positive (PV) interneurons are believed to be critically involved in the gain modulation of nearby pyramidal neurons (Atallah et al., 2012; Wilson et al., 2012). However, PV interneurons in mouse auditory cortex do not modulate the overall gain of local pyramidal cells in a stimulus-dependent manner, as would be required by contrast normalization mechanisms (Cooke et al., 2020). The basis of our heterogeneous responses in the lateral network could be explained by the horizontal connections made by PV interneurons. For instance, axons of basket cells (morphologically defined PV cells [Scala et al., 2020]) in cat visual cortex can target neurons up to 1360 μm away and do not obey orientation domain boundaries, with 57% of projections synapsing at sites with orientation preferences differing by >30° from the location of the basket cell’s soma (Kisvárday et al., 1994). One possibility is that the optogenetic suppression of excitatory neurons in our experiments might have led to a decreased drive to nearby PV cells, which in turn reduces inhibition on both local and distant pyramidal cells that they target. The heterogeneous responses in the lateral network could thus result from a combination of local excitatory and inhibitory inputs. This is captured by our model (Figure 4), which shows that varying the net response gain (‘sensitivity’) of the local network input to a model cell can reproduce all four response types. This hypothesis is consistent with the observed continuum of responses observed within each response class (Figure 2B).

Previous studies silencing cortical populations, particularly those using optogenetic tools, have primarily focused on the light-targeted zone without investigating collateral effects on the nearby local network (Diester et al., 2011; Fetsch et al., 2018; Han et al., 2011; Nassi et al., 2015; Ohayon et al., 2013; Trouche et al., 2016). Crook and Eysel, 1992 have previously shown that inactivating visual cortex, using gamma-aminobutyric acid (GABA) microiontophoresis, could substantially broaden tuning curves of neurons located 600 microns away, but only if the inactivated site had a preferred orientation drastically different from the recording site. A recent study by Li et al., 2019 examined the effects of cortical inactivation on more distant cortical sites by comparing the spatiotemporal profiles of several suppressive opsin constructs in awake, but otherwise passive, mice. This study thus measured differences in spontaneous activity in the absence of sensory inputs. Similarly, we found that in the absence of a visual stimulus (the 0% contrast condition), firing rates in the distal network are not significantly influenced by optogenetic suppression (Figure 1F–I, Figure 2—figure supplement 1). Heterogeneous responses emerged only when optogenetic suppression was paired with a visual stimulus. This detail is important because the presence of stimulus in visual cortex activates otherwise minimal local network connections to produce characteristic response features such as contrast normalization and near-surround suppression. Further, stimulus-paired optogenetic manipulations are commonly employed in experimental designs that closely monitor behavioral responses (Acker et al., 2016; Andrei et al., 2019). Our findings demonstrate that focal optogenetic suppression has unpredictable effects in the surrounding network. While this finding provides a potent method with which to investigate local circuits, it adds complexity to interpreting the causal relationship to behavior.

Materials and methods

Animal subjects

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This study was performed in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. All of the animals were handled according to the approved institutional animal care and use committee (IACUC) protocols of the University of Texas, Houston. The protocol was approved by the Committee on the Ethics of Animal Experiments of the University of Houston (protocol number: AWC-20–0075). Two male rhesus monkeys (Macaca mulatta) were trained for a contrast detection task and were implanted with a 19 mm titanium chamber (Crist Instruments) over V1. All surgery was performed under general anesthesia, and every effort was made to minimize suffering.

Viral vector preparation and injection

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The anion channelrhodopsin GtACR2 (Forli et al., 2018; Govorunova et al., 2015; Mauss et al., 2017; Mohammad et al., 2017) was packaged in a lentiviral construct under the control of the promoter α-CamKII (LV-CaMKIIα-GtACR2-GFP). The vector was prepared by the University of North Carolina Vector Core, with a titer of 109 IU/ml. Four V1 columns, and five sites per column (spaced every 250 µm), were targeted for injection per animal. The virus was injected using a 29 gauge needle attached to a Hamilton syringe. The syringe was advanced using a computer-interfaced micromanipulator (NAN Instruments). For each injected column, the needle and syringe were lowered to the deepest site at which neural activity had been recorded in the previous days. After a 15min waiting period (5 min waiting period for every subsequent site within the column), the plunger was independently advanced, delivering 1 µl of the viral suspension over a 10 min period. After a 5 min waiting period, the needle was retracted upward to the next site. The process was repeated for each additional site within the column and then for each additional column. A custom-built grid (Crist Instruments) was used during the injection as well as during subsequent recordings to precisely target the tissue expressing the virus.

Cortical biopsy and immunohistochemical tissue staining

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In order to avoid unnecessary animal sacrifice, we developed a method to biopsy cortical tissue in awake macaques. After concluding all experiments, a transfected column of the cortex was biopsied using an aseptic aspiration technique. A small amount of lidocaine was applied to the cortical surface, and an 18 gauge needle attached to a syringe filled with phosphate-buffered saline (PBS) was positioned over the biopsy site (recording site significantly modulated by optical stimulation). The syringe was then lowered to a predetermined depth (previous recording site with laser-responsive cells) with a computer-interfaced micromanipulator (NAN instruments). The syringe was then rotated in order to cut surrounding tissue connections. After a 5 min waiting period, the plunger was manually retracted at a very slow speed until tissue is apparent in the PBS of the syringe. The syringe and needle were immediately retracted and the biopsied tissue was fixed in paraformaldehyde (1% solution) for 6 hr. This technique provides an adequate amount of tissue with minimal impact to the animal.

Tissue was fixed in paraformaldehyde (4%) in PBS for 1 hr and then cryoprotected in sucrose (30%) in PBS at 4°C overnight before sections were cut (20 μm). Sections were incubated with primary antibodies against NeuN (polyclonal Guinea pig [Synaptic Systems, Göttingen, Germany], used at 1:250), CamKIIα (monoclonal mouse, used at 1:500 [Dr. Neal Waxham (UT Houston)]), and GFP (polyclonal rabbit [Synaptic Systems, Göttingen, Germany], used at 1:1000) at 4°C overnight. Sections were incubated with secondary antibodies against the primary antibodies for NeuN (Alexa 647-conjugated goat anti-Guinea pig IgG, used at 1:300), CamKIIα (Alexa 568-conjugated goat anti-mouse IgG, used at 1:300), and GFP (Alexa 488-conjugated goat anti-rabbit IgG, used at 1:300) at room temperature for 1 hr. All secondary antibodies were obtained from Thermo Fisher Scientific. Sections were mounted with Vectashield mounting medium with 4', 6-diamidino-2-phenylindole (DAPI) (Vector Labs). Images were obtained using a Zeiss LSM800 confocal microscope and analyzed using the ZEN 3.0 software.

Behavioral experiments

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Monkeys completed a contrast detection task using sine-wave gratings presented at recorded cell population’s preferred orientation and a fixed spatial frequency. Stimuli were generated using Matlab (Mathworks) with Psychophysics Toolbox (Brainard, 1997) and were presented on a gray screen 19 inch cathode ray tube(CRT) monitor, located 90 cm away from the animal. On each trial, animals held a response lever and fixated on a central point (0.5°, with 1° fixation window). If fixation was broken before the end of the trial, or if the response lever was lifted before the response period, the trial was aborted. Eye position was continuously monitored using an infrared eye tracking system (EyeLink II, SR Research) with a 1 kHz sampling rate. On 50% of trials, a stimulus was presented over the receptive fields of recorded cells. Stimuli were presented at four contrast levels per session (five total contrasts were used across sessions: 3.5, 5, 10, 20, and 100%) on a gray background. Laser onset was synchronized with the stimulus onset, which both lasted 300 ms (light was continuously on during this period). Stimuli had a median size of 2.5° (± 0.6°) and a spatial frequency of 2 cycles per degree. Additionally, on half of the trials, optical stimulation was delivered simultaneously with the visual stimulus (or when the visual stimulus would have appeared on a no-stimulus trial) and was delivered equally throughout all stimulus contrast conditions. During the response period (600–1800 ms after stimulus onset), animals released the bar to signal the presence of a stimulus or continued to hold the bar to signal the absence of a stimulus in that trial. Correct behavioral responses were rewarded with five juice drops. Animals completed 128–480 trials per session (inter-trial intervals were dependent on the animal and averaged 5.0 s ± 4.7 sem).


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Extracellular recordings were completed using 16 or 24 channel laminar electrodes (U-probe, Plexon Inc), advanced by a computer-interfaced micromanipulator (NAN Instruments). Contacts were 25 µm in diameter, equally spaced (100 µm intercontact spacing) and coated in platinum iridium. Neural activity was recorded in real-time using a Multichannel Acquisition Processor system (MAP, Plexon Inc). For 20 of the 21 sessions, one laminar electrode was inserted into V1 and a separate optical fiber (see below) was inserted approximately 300 µm away from the electrode. In this manner, the optical fiber silenced one population of cells, with no direct effects on the population recorded by the laminar electrode. For 1 of the 21 sessions, two laminar electrodes were inserted, such that one recorded the activity of cells being directly suppressed by the laser (probe was as close to the optical fiber as possible) and of cells being indirectly suppressed by the laser (probe was about 300 µm away from the optical fiber). An additional seven sessions were recorded with the laminar electrode and the optical fiber as close as possible (~150 μm) in order to record the direct, light-induced suppression of GtACR2 activation. At the directly suppressed site, light-responsive cells were vertically clustered within 457.1 ± 104.3 µm (mean ± sem, n = 31 cells with maximum suppression within 30 ms of light onset), as measured by the maximum distance between electrode contacts for these units. This measurement includes both directly suppressed cells, and the nearby directly connected cells, as detecting suppression with millisecond precision is more difficult than detecting activation.

Optogenetic stimulation

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Optical activation of opsins was delivered via a 100 mW blue laser (473 nm; Laserglow Technologies) coupled to a penetrating optical fiber. The 200-μm diameter optical fiber was encased in a stainless steel cannula for mechanical support and advanced into the brain using a computer-interfaced micromanipulator (NAN Instruments). Prior to each experiment, the optical fiber was aligned to the upper third of recording contacts on the laminar electrode. Registration lines drawn onto the shafts of the electrode and fiber optic ensured that this spacing was preserved during recordings. Laser stimulation parameters were controlled by a waveform generator (Agilent Technologies). Optical stimulation was delivered as one continuous pulse for 300 ms and was synchronized with the visual stimulus. Light power output at the end of the fiber optic was periodically measured using an integrating sphere sensor (S124C, Thor Labs) and was kept consistent across sessions (~7–14 mW per mm2). Importantly, the optical fiber was scissor-cut and the direction of the light was perpendicular to the cortical surface, meaning that the maximum light power was vertical, parallel to cortical columns. The lateral position of the fiber optic was approximately 300 µm away from the probe, such that the optical stimulation affected a nearby but distinct population of cells relative to the recorded responses. The spacing between devices was achieved using guidetubes placed inside a custom grid that sat inside the chamber.

Cell classification

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Units were sorted offline (Plexon Offline Sorter). On average, per session, we were able to identify 5 (± 4) single units and 7 (± 3.5) units (single or multiunit activity) whose activity could be significantly modulated by light application. Cells that were significantly modulated by light were identified by comparing activity in trials with optical stimulation and trials without optical stimulation. The transient response (0–150 ms of optical stimulation) and sustained response (150–300 ms of optical stimulation) were independently analyzed. If either the transient or the sustained response during optical stimulation was significantly different (p<0.005, Wilcoxon ranked sum test, with Bonferroni correction for multiple comparisons) from the control activity during the same time period, it was determined that the cell could be modulated by optical stimulation and was therefore considered light responsive.

Layer identification

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CSD was performed to identify cortical layers of V1 (Hansen et al., 2012; Nigam et al., 2019; Schroeder et al., 1998). Evoked response potentials in response to a high-contrast stimulus were recorded from equally spaced laminar contacts. The second spatial derivative of the evoked response potentials was computed (iCSD toolbox [Pettersen et al., 2006] for Matlab) and the granular layer was identified by finding the first sink, measured in nA/mm3. Channels located in the primary sink were identified as the granular layer, while channels above the sink were identified as the supragranular layer and channels below the sink were identified as the infragranular layer.

Contrast response functions and normalization fits

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For all light-responsive units, we considered the 150 ms epoch containing the maximum stimulus response. To identify this epoch, stimulus responses on control (no laser) trials were divided into four non-overlapping 150 ms bins, aligned with the stimulus onset, and the bin with the maximum spike count across trials was identified. We then compared the responses on laser trials during the same epoch. For the zero-contrast (no-stimulus) condition, the epoch that most often yielded the strongest response during stimulus present trials was used. This variable epoch approach was preferred over a fixed epoch, which assumes neural responses to occur within a prescribed interval. Due to the wide range of visual contrasts utilized, many units displayed clear shifts in response latency as a function of stimulus contrast (visible in Figure 1F–I), a well-known phenomenon (Carandini et al., 1997). This approach allowed us to tailor our analyses to the peak response of each neuron. For the majority of units (72.9%), the peak response averaged across stimuli occurred in 150–300 ms after stimulus onset.

We then fit the contrast responses with the normalization equation, using a procedure similar to that by Nassi et al., 2015 and Sato et al., 2014. We first fit the firing rates in the control (no laser) condition with a hyperbolic ratio function using a least-squares method,

(2) r(c)=r0+rmaxcnc50n+cn

where r0 is the baseline firing rate, rmax is the maximum firing rate, c50 is the semi-saturation constant, and n is the slope of the function. Then, for laser trials, we used the same c50 and n parameters and solved for the remaining parameters, P and Q, using the full normalization equation (Equation 1). Quality of fits was assessed as the percentage explained variance, where R2 = [1-(error sum of squares/total sum of squares)].

Orientation selectivity

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Orientation tuning for the recorded population was measured separately prior to the contrast detection task with optogenetic suppression. For this task, monkeys fixated on a central spot on the computer screen, while a movie stimulus consisting of a sequence of 48 circular 100% contrast sinusoidal gratings (eight equidistant orientations randomly flashed at 30 Hz) was presented for a total duration of 1.6 s. The size and location of the stimuli were kept identical to the ones used in the subsequent detection task. The preferred orientation and orientation selectivity index (OSI) for each neuron were computed from Fourier components extracted from the orientation tuning curves as described previously (Dragoi et al., 2002). Variance of tuning across the population of units in individual sessions was computed using circular statistics (CircStat toolbox for MATLAB [Berens, 2009]).

Computational model

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We built a firing-rate model of a single output neuron receiving excitatory and inhibitory synaptic currents from the local network and driven by a feedforward excitatory input. The total synaptic current, Is, is given by

(3) τdIsdt=-Is+aRex-bRix+I

where τ is a time constant, a and b are positive coefficients, Rex and Rix are the aggregate excitatory and inhibitory firing rates of the local network, and Iin is the feedforward input. The firing rate R of the output neuron is computed from the synaptic currents with R = Fe(Is), where Fe is a monotonic increasing function. The model dynamics are given by

(4) τdIsdt=Is+JeeRexGJieRix+JinpS(pc);R=Fe(Is)τdIexdt=Iex+Iexin;Rex=Fe(Iex)τdIixdt=Iix+Iixin;Rix=Fi(Iix)

We used τ = 15 ms, Jee = 12 nA/sp/s, Jie = JeeE/Iratio , and Jinp = 12.5 nA/sp/s. The E/I ratio was varied between 1.75 and 2.25. The gain of the excitatory current, G, varied between 1 and 55. The stimulus S varied logarithmically with the percent stimulus contrast, pc,

(5) Spc=15+25*log10pc/65

where 2.5%<pc<100%. The stimulus was presented for 300 ms. The reported rates are steady-state rates, sampled at 150 ms. Optogenetic suppression was modeled as a reduction in Rex of 12.9 sp/s (in accordance with experimental data). For excitatory neurons, the firing rate R was computed from the synaptic current Is as R = Fe(Is):

(6) Fex=100/1+e-0.1x-50

while for inhibitory neurons we used function Fi:

(7) Fix=150/1+e-0.13x-46

By changing the local excitatory network gain G (in Equation 3) we were able to reproduce all four response types found in the experiment (see Figure 4). We examined whether changing the sensitivity of the feedforward input, rather than the local network input, could produce similar effects as altering the gain of the local excitatory network. To this end, we fixed the local excitatory network gain G to 1 and multiplied the feedforward drive (equation 5) by a gain factor that varied between 0.01 and 55. This arrangement, however, could not replicate all four contrast response types observed experimentally (see also Figure 3—figure supplement 2).

Data availability

All data generated or analyzed during this study are included in the manuscript and supporting files. Source data files are provided.


Decision letter

  1. Martin Vinck
    Reviewing Editor; Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Germany
  2. Joshua I Gold
    Senior Editor; University of Pennsylvania, United States
  3. Martin Vinck
    Reviewer; Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Germany
  4. Alessandra Angelucci

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

Acceptance summary:

The visual cortex contains an abundant of recurrent connections that are critical for computation. The authors show how local optogenetic inactivation of a column in macaque V1 leads to heterogeneous and layer-dependent activity changes mediated by lateral interactions. These changes range from full suppression to facilitation, and a mixture of both. The authors further demonstrate that these lateral interactions determine behavioral outcomes, hence suggesting that behavioral outcomes cannot be predicted based on the focal inactivation alone.

Decision letter after peer review:

Thank you for sending your article entitled "Heterogeneous side-effects of cortical inactivation in behaving animals" for peer review at eLife. Your article has been reviewed by four peer reviewers, including Martin Vinck as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by Joshua Gold as the Senior Editor.

As you will see from the reviewers, they are mixed, although three reviewers make numerous positive remarks (and also some critical comments) about the interest of the paper. Notably there are a number of concerns that need to be addressed, in particular the potential need for additional experiments related to the sampling of the contrast. Reviewers raised the concern that sampling of the contrast axis should be based on 7-8 contrasts, and that the under-sampling of contrast space in combination with low trial numbers might affect the claims about increased activity at low vs. high contrasts and the conclusions on heterogeneity. It would be important to understand how this concern will be addressed. One option could be, e.g., to validate the findings in a smaller set of experiments, if possible. Besides this point, you will find numerous other concerns to be addressed below.

Essential revisions:

Reviewer #1:

The authors use focal optogenetic inactivation together with dual laminar recordings in awake primates to study the impact of activity in one column on the activity in another column. With this technique, they investigate the nature of lateral interactions and normalization mechanisms in V1. The key findings are that: (1) Optogenetic inactivation does not yield an overall facilitation or suppression in a nearby column. (2) This is explained by heterogeneous changes in activity that are stimulus dependent, with four main types of responses, the most common one being general suppression across all stimulus contrasts, in particular in the supra-granular layers. (3) A simple E/I network can predict these four types depending on two parameters. (4) There exists a high degree of variability across sessions, however in sessions with predominantly Type-1 responses (suppression), behavioral outcome is also affected. Together, these findings suggest that the effect of optogenetic inactivation on the local circuit is highly complex, that lateral interactions are highly heterogeneous across cells and that focal optogenetics inactivation might impact behavior in a complex manner. My main comments pertain to the statistical analyses:

1. It was not entirely clear to me how the four subgroups were statistically quantified.

A. Where the individual neurons that belong to the four types statistically significant?

B. Was the distribution in four types statistically different from chance?

C. How was the choice of four categories motivated, was this done through a clustering mechanism?

2. It would be useful to discuss how the contrast-dependence of lateral interactions (with visual stimuli) fits or does not fit with the present results.

3. The nature of the model is not entirely clear: Which variables represent the local site and which variables represent the distant site? Do both have E/I neurons? What are the connections between those?

4. It would be useful to explain better what standard normalization models would predict. There is some discussion on this, but it is not clear why one would expect suppression of a distal site rather than activation at these retinotopic distances. In this context it would also be useful to discuss Mexican-hat profiles of activation/suppression in relationship to the present findings.

5. Detailing criteria for spike sorting would be useful for future studies comparing to the present findings.

6. Figure 2C misses a color bar.

7. Unless my PDF renderer has a problem, Figure S5 seems to be the wrong figure (duplicate of Figure 5) and this control should be fixed.

Reviewer #2:

Andrei and colleagues performed an optogenetic experiment in area V1 of monkeys, whereby they inactivated glutamatergic neurons using a lentiviral vector approach with CamKII as promotor and the chloride conducting GtACR2 opsin. To investigate the local network effects of the optogenetic inactivation, they recorded single and multiunit activity nearby the optic fiber and at about 300 micron distance. The major claim of the authors is that local reversible inactivation leads to unexpected or unpredictable activity changes in (nearby and) distant neurons. Neurons affected by the light were classified in 4 groups, depending on the optogenetic-induced dynamic activity changes. These 4 types of responses could be predicted based on a simple spiking model with a linear combination of excitatory feedforward drive and local recurrent excitatory and inhibitory inputs. In addition, monkeys also performed a contrast detection task, and performance was impaired only when the activity in the majority of the (recorded) neurons was suppressed.

This study yielded a number of interesting findings, but contrary to the framing of the authors, ("unexpected" "unpredicted" "most surprising" "unpredictable", "off-target effects have never been investigated…"etc.), a variety of immediate downstream "off-target" effects after optogenetic activation and inactivation have been amply described in primates -already starting with the first optogenetic study in monkeys (Han et al., 2009). The main 'selling' point of the study is unsurprising. Virtually every study so far showed, predictably, a mixture of facilitation and suppression at single unit level, independent of the type of opsin used. In general, more suppression is found with a hyperpolarizing opsin and more (or net) enhancement with depolarizing opsins. That said, the current findings whereby neighboring and distant effects at neuronal level are compared, are certainly a nice addition to the literature, as is the apparent division in four response classes. The authors should significantly tone down their language, however. Of course, this has also consequences for the impact of the paper.

This reviewer also questions the usefulness of GtARC2 due the exceedingly long after hyperpolarization, which may have contributed to the potentially stronger off-target (i.e. facilitation) effects compared to other hyperpolarizing opsins. Also, it is unfortunate that continuous stimulation was used, which prohibits a latency analysis in the distant neurons. Such an analysis would have made the discussion about direct versus indirect effects much simpler, at least compared to the current argumentation of the authors.

The authors argue twice that it is troublesome to interpret optogenetic -induced effects without measuring neuronal activity at distant sites during light exposure. This reviewer does not argue about the usefulness of concurrent recordings. However, it is almost impossible to cover all sites which may show off-target effects. In the present study, one only recorded at (some sites) at 300 micron distance. Yet, all sites connected with the neurons directly affected by the light might show "off-target" effects, even those in remote areas. It is impossible to 'cover' all these sites with electrophysiological recordings. Hence the solution offered by the authors is not workable. Whole brain imaging may be an alternative, yet it remains challenging to relate changes in imaging signals with alterations in neuronal activity.

The authors categorized the optogenetic induced neuronal responses at a distance in four classes, which is a nice finding. It is unclear, however, how the neurons were clustered. Unless I missed it, no clustering approach with objective criteria to determine the number of relevant clusters was used. Please elaborate on this.

The authors emphasize the degree of heterogeneity of the indirect network effects, but this may be highly related to the transduction efficiency and layer-specificity of transduction.

Figure S5 is the same as Figure 5.

Reviewer #3:

The manuscript investigates the important question how local optogenetic silencing of V1 neurons affects neural populations that are located nearby, and whether these effects can impact on behaviour. Based on contrast response functions, the authors identify 4 different effect types, which range from inhibition only through mixed effects that depend on stimulus contrast to facilitation only.

The diversity of effects could be replicated in a network model by varying stimulus sensitivity of network inputs.

Behavioural effects on contrast detection occurred only when off target effects were dominated by type I effects, i.e. those that show inhibition at all contrast levels.

The idea of studying the effects of optogenetic silencing on off target neurons is important, but there are problems with the identification of different effect types given the coarse sampling of stimulus contrasts. Also, the effect on behaviour would benefit from more detailed investigation, e.g. limiting the stimulus dimensions to off target locations, as well as obtaining a lager data base to study effects of other response types on behaviour more quantitatively.

1. In methods the injection procedures are described, but it remains unclear how 'the deepest point' of a column was determined? Was it a fixed depth that was targeted? Also, the 5 injections per column were probably evenly spaced. But the distance between sites should still be given.

2. Methods: "If either the transient or sustained response during optical stimulation was significantly different (P<0.05, Wilcoxon rank sum test)" -- was this corrected for multiple comparisons? After all 2 tests are done on the same activity.

3. The contrast fitting function (aka Nassi) is problematic due to the number of contrasts tested in a single experiment. It has 4 free parameters for 5 data points (when 4 +0% contrast was used). Also, the equation has no numbering.

4. Were the oriented gratings really flashed at 30Hz? i.e. 33ms on time per stimulus? This could induce strong masking effects.

5. In methods the authors state that the light guide was close to the recording electrode in 7 sessions. In figure 1E it states that n=48, while in the text it states that n=41 when the statistics are mentioned. Also, it is unclear whether this is neurons or contacts? Were there depth differences for these n=48(41).

6. Were the effects of light stimulation at different contrasts corrected for multiple comparison? If not, they should be.

7. Unless I am mistaken, equation 1 has 4 free parameters (r0, rmax, P,Q, with c50 and n fixed) that are fit to each neuron with optogenetic stimulation? If so, this is problematic for the reasons mentioned above. The authors only measured 5 data points, and the model is thus likely to overfit. Hence the variance accounted for is not very impressive. If my understanding of the fitting procedure is mistaken, then the authors should explain in detail how it was implemented.

8. The authors should describe in detail how the classification of the 4 response types was arrived at. How was the 'Type' category defined? Ideally this would be done, based on the terms P and Q yielding significant improvements to the fit?

9. The data shown in figure 3D-G are puzzling. The P values are mostly negative, i.e. they seem subtractive, rather than additive? That suggests the network does not provide excitation, unlike stated in the main text. Also, the c50 values of many neurons appear very high, and are in a range where sampling was basically absent. All examples shown in figure 2 have c50 values much lower.

10. While the claim that type 2 neurons are more sensitive to low contrast than other types, is correct, this cannot be inferred from the slope of the fitting function, but from the c50. If a neuron did not respond to any contrast including 20%, but strongly to 100% stimuli, it would have a c50>20%, but could have a very steep response function, which would be an artefact of the fitting in conjunction with the sampling.

Reviewer #4:

In this study the authors examine the effects of optogenetic inactivation directed onto a cortical site on neuronal activity of nearby lateral loci (off-target), and on behavioral performance.

They find that inactivation of the superficial layers, while reliably suppressing activity at the inactivation site, causes heterogeneous effects at off-target cortical loci, with some laminar-bias. The authors further determined that changes in behavioral performance were consistently observed only in sessions where suppressive effects among recorded cells dominated. The study is overall well executed, and well presented, and it is of interest to a broad audience. Some additional analysis would strengthen the claim that photostimulation at the inactivation site does not spread to nearby loci, where the recordings are made. Moreover, it is unclear whether the observed heterogeneous effects would become more homogenous at higher light intensities.

1) Could the effects seen at off-target sites depend on the specific temporal dynamics of the inactivating opsin used in this study? This point should be addressed in the discussion.

2) To strengthen the main claim of the paper, that off-target sites 300µm away from the inactivation site are not directly inactivated by light, the authors should determine the onset latency of suppressive effects at the on vs off-sites. The expectation would be that the latter are suppressed significantly later in time than the former if, indeed suppression is a network effect. While I do not expect that light could inactivate directly the deep layers at the off-site, it could easily spread 300µm away and directly affect the superficial layer cells. In support of this, the type I cells dominate in the superficial layers. Moreover, some of the off-site suppression, e.g. in the example cells in Figure 1F-G, seems to occur very early, possibly suggesting direct inactivation by light spreading to the off-site. Importantly, the latency analysis should be performed on a layer-by-layer basis because it is possible that only the superficial layer cells at the off-site are directly affected by light, while those in deep layers are a result of network effects. On p. 4 the authors state that:" The lack of suppression in the blank condition indicates these responses cannot be due to direct activation of GtACR2 at the distal site". First, the authors could strengthen this claim by showing this at the population level, rather than simply showing the few example cells in Figure 1F-I. On p. 6 the authors further make the point that the temporal profile of suppression seen at the inactivation site is rather different from that at the off-site, with long-lasting responses only found at the inactivation site near the light source. However, one could imagine that these different profiles may result from different light intensities at the inactivation site compared to the off-site; light scatter in tissue may result in reduced photostimulation intensity at the off-site. For the same reason, reduced light intensity at the off-site may also cause significant suppression only when the cell is driven by a visual stimulus, but not be apparent in the baseline response. For all these reasons, an analysis of onset latency of suppression (in the stimulus-driven condition) at the inactivated site vs the off-site could strengthen the authors' claim that the off-site is not directly affected by light.

3) I could not find the light intensity values used for inactivation anywhere in the manuscript. This should be added.

4) Figure 1E. rather than one example cell, it would be preferable to show the full laminar profile of suppression at the photoactivated site to demonstrate that light is, indeed, limited to the SG layers. This is shown in Figure S1B-C, but this figure is difficult to interpret correctly because the Y axis is not labeled, and the estimated top and bottom of cortex as well as L4C are not indicated on the laminar plot.

5) Figure 2E. Judging from the CSD analysis, here the top of the cortex would seem to be contact 2, rather than 0, and the thickness assigned to the G layer is too large. The latter in vivo typically spans about 3, not 4, contacts (if the penetration are vertical which this appears to be). Moreover, the earliest current sink, which is the criterion for identifying layer 4C, would seem such that the top of G should be moved down by at least one contact. The selection should be based on a latency analysis. I am raising this issue because with layers more properly assigned the laminar data could potentially clean up and appear less heterogeneous.

6) Model. Could the 4 different types of responses depend on intrinsic properties of cells (for example their contrast response function), rather than, or in addition to the lateral network connectivity?

7) Model. What determines how strongly the network is driven by the stimulus, in the model and possibly in the real brain? In other words, what determines network sensitivity? Is this the weights of the local connections?

8) There should be a discussion of whether these results could change depending on photostimulation intensity. Is it possible that at higher light intensities the off-site would be more homogeneously suppressed for ex.?

9) Please add all sample size to the figure legends. For ex, sample sizes are missing in panels C and F of Figure 5.

10) Isn't it odd that effects on behavioral performance in Figure S7E are only seen at 20% contrast given that type 2 cells are suppressed at contrasts {greater than or equal to} 10?

11) P. 25 Layer Identification. The granular layer in the CSD analysis should be defined as the location of the earliest current sink, not the "maximum sink" as sated.


Author response

First, thank you very much for your very useful comments and suggestions. In this revised manuscript we present the results of the analyses that you have recommended and describe the new control experiments we have performed. Our new findings offer additional insight into the response types revealed by cortical suppression and provide additional, strong support for our previous conclusions.

1. The most common question among reviewers was how the 4 response categories were identified, and whether a clustering method was used.

Reviewer 1: “How was the choice of four categories motivated, was this done through a clustering mechanism?”; Reviewer 2: “The authors categorized the optogenetic induced neuronal responses at a distance in four classes, which is a nice finding. It is unclear, however, how the neurons were clustered.”; Reviewer 3: “The authors should describe in detail how the classification of the 4 response types was arrived at. How was the 'Type' category defined?”

a. We took several steps to address this point (as described below).

b. We thank the reviewers and editors for suggesting the UMAP clustering algorithm. We have included the results of the UMAP clustering algorithm performed on the firing rates of the different response types, using the correctly ascribed labels (Figure 2 – supplementary figure 1D) and randomized labels (Figure 2 – supplementary figure 1E). UMAP produces 4 clear, non-overlapping clusters when firing rates across contrasts are correctly labeled with the response type, but yields only one cluster when labels are randomly assigned to the same firing rate data. This analysis demonstrates that the classification of response types based primarily on the normalization fits corresponds to differences in firing rates for individual cells and is not due to errors in fitting accuracy. This new figure has been included in the revised Figure 2 – supplementary figure 1 (page 32).

c. As suggested by the editors and reviewers, we also implemented a similar clustering analysis using the t-SNE (t-distribution stochastic neighbor embedding) algorithm, using the identical data as for the UMAP analysis above. The t-SNE algorithm produced similar results, producing better grouping of data with correct labels versus randomized labels (Author response image 1) . However, this method was not as effective compared to the UMAP method and given its redundancy we did not include this analysis in the revised manuscript.

Author response image 1
t-SNE results clustering firing rate differences (laser minus control) across all contrasts for all cells, labeled with ascribed labels (A), or with randomly ordered labels (B).

Error ellipses represent the covariance matrix and are centered on the median of tSNE results for each type.

d. Similar to the above clustering analysis above, in order to ensure that the response types categorized using the normalization fits corresponded to the underlying firing rate changes we implemented a classifier. The classifier was trained on the differences in firing rates (laser minus control) across all contrasts for each cell using either the correct labels (Figure 2 – supplementary figure 1F, left side black bar ‘Actual’) or randomly shuffled labels (Figure 2 – supplementary figure 1F, left side gray bar ‘Chance’). The classifier was validated using 25% holdout validation. This procedure was repeated 5 times and the mean ± s.e.m. classifier performance is shown in Figure 2 – supplementary figure 1F. The overall accuracy of the linear discriminant classifier to correctly classify firing rates changes into the ascribed classes was 74.3% ±2.6 (mean ± s.e.m.) compared to 33.2% ±1.9 on the randomized data. For individual response types (Figure 2 – supplementary figure 1F, right side), the area under the receiver operating characteristics curves (‘AUC’) are all significantly above chance (colored versus gray bars), and all AUC>0.8, indicating the classifier performed well for all types. This figure is included in Figure 2 – supplementary figure 1F, and is mentioned in the text (page 6, paragraph 2).

e. As suggested, we have now included a new paragraph to address the classification as a separate issue and describes the methods in detail (page 6, paragraph 2).

f. Taken together, the results of the clustering analysis and the classifier performance convincingly demonstrate that the response types can be well separated based on the measured light-evoked changes in firing rate across contrasts. Thus the identification of 4 response classes do not rely solely on the normalization fits, nor can they be attributable to chance.

2. Reviewers 1 and 3 ask about statistical testing to ensure that the responses are unlikely to be due to chance.

a. To address whether the heterogeneity could arise by chance, we re-examined the MATLAB code where the laser and stimulus responsiveness is statistically tested for each unit and found a typographical error in the methods describing this process. The laser responsiveness of each cell was based on an α value for the Wilcoxon ranked sum test of P<0.005 (0.05 divided by 10 comparisons), not 0.05 as previously stated. To assess responsiveness to the visual stimulus we used an α value of P<0.05, since only one comparison was made for each cell (highest contrast versus 0% contrast). This has now been corrected in the revised methods section (page 27, paragraph 2). Since this method includes a stringent Bonferroni correction for multiple comparisons to identify laser responsive units, this should provide a high degree of confidence that the laser responses are not due to chance.

b. In addition to showing example cells for each response type in Figure 2A, we have also included the population firing rates for each response type in Figure 2B (page 8), showing statistics with multiple comparisons corrections. This figure was previously provided in Supplementary Figure S2.

3. Reviewers 2 and 4 raised the important point of distinguishing direct activity suppression from indirect, network-based suppression. Reviewer 4 suggested that we perform a latency analysis to differentiate direct light suppressive effects from indirect ‘Type 1’ suppressive effects.

As suggested by Reviewer #4 – to test whether the Type 1 (all suppressive) responses at the distal/indirect site could actually be due direct activation of GtACR2 by a small amount of light scattering, we performed a suppression latency analysis. That is, we compared the time required to reach the maximum suppression following the onset of both the visual stimulus and the light for cells at the direct site and for Type 1 cells found at the distal site (Figure 1 – supplementary figure 1L). We also separated the indirect Type 1 cells across layers (Figure 1 – supplementary figure 1M). As expected, we found that the suppression latency at the direct sites was significantly shorter than that at the indirect sites (31.2 ± 1.15ms versus 92.9 ±1.10ms, respectively; P=0.00074 Wilcoxon ranked sum test). Suppression at direct site was also significantly different compared to Type 1 cells across all layers (P=0.0022 ANOVA test, d.f.=3), but no differences were found across layers. This analysis supports our previous interpretation that Type1 cells at the indirect site are suppressed due to their synaptic connections with the directly suppressed cells. This is now addressed in the manuscript (page 4, paragraph 3).

4. Reviewer 3 and the editors raised concerns about the number of contrasts used to fit the contrast response functions and suggested that we perform additional experiments using 7-8 stimulus contrasts.

a. We performed additional experiments using 9 stimulus contrasts to evaluate the accuracy of our previous fitting procedure (Figure 2 – supplementary figure 2). The responses of 54 units were either fit (i) using the mean firing rates from all 9 contrasts (0, 3.5, 5, 10, 15, 25, 50, 75, and 100%), or (ii) using a subset of 5 contrasts (0, 5, 10, 25, and 100%), with a similar range as in the original experiments. However, when we compared the fit parameters obtained from the 9-point and 5-point fits, we found that they were highly similar (assessed by a Pearson correlation coefficient). In particular, the more critical parameters of c50 (Figure 2 – supplementary figure 2C) and slope (Figure 2 – supplementary figure 2D, left) both had a Pearson correlation coefficient of 0.89 (Pearson P<0.001). This is now addressed in the manuscript (page 6, paragraph 3).

b. Interestingly, the above analysis also led us to notice that a portion of cells (~30%) do not have saturating responses at high contrasts. For these cells, the Naka-Rushton fitting parameter associated with c50 no longer corresponds with the contrast that elicits 50% of the maximal response, but rather this parameter approaches 100% contrast (Figure 2 – supplementary figure 2E-F). In our previous manuscript we reported the overall mean value of Naka-Rushton c50 parameter without accounting for these cells, which led to an over-estimation of the actual c50 of the population. To correct this, in the revised manuscript we have measured the actual c50 from the fits (new Figure 3G) and moved the report of the Naka-Rushton fit parameter c50 to Figure 2 – supplementary figure 2G. We now also report the percentage of cells within each of the 4 types that exhibit this non-saturating behavior (Figure 2 – supplementary figure 2H), and show the distributions of the Naka-Rushton c50 parameters (Figure 2 – supplementary figure 2I). Importantly, please note that for the majority of cells, both the empirically-measured c50 (Figure 2 – supplementary figure 2C, above; 12.6% ± 0.98 contrast, mean ± s.e.m., n=214) and the Naka-Rushton c50 parameter was contrast (Figure 2 – supplementary figure 2I) was less than 20% – which is within the range of contrasts sampled in the original manuscript. This is addressed in the manuscript (page 11, paragraph 4).

c. As a side note, the above analysis made us wonder whether these non-saturating cells maybe do actually saturate, but only at very high contrasts that our 9-point fits did not sample. To satisfy curiosity, we recorded an additional population of 28 cells and densely sampled at high contrasts (Figure 2 – supplementary figure 2J-L, above). We found that 21% of cells indeed do not seem to saturate (Figure 2 – supplementary figure 2J). This proportion was similar to that observed in the original data (compare Figure 2 – supplementary figure 2L with Figure 2 – supplementary figure 2I, above), leading us to conclude that the cells in the original data with high (>80%) Naka-Rushton c50 parameters represent a real type of cell behavior, and is not due to under sampling of high contrasts. This is addressed in the legend to Figure 2 – supplementary figure 2 (page 33).

d. The above experiments demonstrate that the range and number of contrasts used in the original experiments are sufficient to capture the contrast response parameters almost as well as it could have been obtained using 9 contrasts. Importantly, this analysis shows that the c50 parameter for the majority of cells falls within the range of contrasts tested in the original experiments. This is discussed in manuscript (page 6, paragraph 3).

5. Reviewer 4: “… and the thickness assigned to the G layer is too large. The latter in vivo typically spans about 3, not 4, contacts (if the penetration are vertical which this appears to be).”

a. In our laminar analysis we employed the same laminar definitions as many other physiology studies (Cox et al., 2019; Dougherty et al., 2019; Van Kerkoerle et al., 2017; Westerberg et al., 2019), with a granular layer depth of ~500 microns, as supported by anatomical studies (Lund, 1973; O’Kusky and Colonnier, 1982; Vanni et al., 2020).

b. However, since there is considerable variability in the thickness of the granular layer across anatomical studies (ranging between 360 – 580 microns from our literature survey), we have redefined granular as spanning 300 microns and replotted the laminar distribution of response types for comparison (Figure 2F, Figure 2—figure supplement 3C). Our previous analysis used a convention of 400 µm. Shifting this boundary by 100 µm (1 contact difference) resulted in layer assignment changes (G to SG) for 16 cells (n=7 Type 1, n=5 Type 2, n=4 Type 3 and n=0 Type 4).

6. Reviewer 1: “The nature of the model is not entirely clear: Which variables represent the local site and which variables represent the distant site? Do both have E/I neurons? What are the connections between those?”

The model output is the firing rate of one neuron at the distant site. The activity of this neuron is driven by stimulus-related activity, as well as by excitatory and inhibitory currents representing the local network. This has been clarified in the manuscript (page 12, paragraph 2), and the model schematic has been modified to show that both the local network and the output neuron receive feedforward input (Figure 4A left, page 13)

7. Reviewer 1: “It would be useful to explain better what standard normalization models would predict. There is some discussion on this, but it is not clear why one would expect suppression of a distal site rather than activation at these retinotopic distances. In this context it would also be useful to discuss Mexican-hat profiles of activation/suppression in relationship to the present findings.”

a. We have included the following paragraphs in the discussion (starting page 17, paragraph 3):

“Heterogeneity of normalization has not been well studied, nor is it well-defined for populations of neurons. The denominator of the normalization equation represents the cumulative activity of the local network (or ‘normalization pool’). […] It is quite possible that some of the effects recorded at the 300 µm distance, could be due to activity changes that occurred at longer distance (>500 µm) primarily mediated by long-range inhibitory projections.”

8. Reviewer 2: “This study yielded a number of interesting findings, but contrary to the framing of the authors, ("unexpected" "unpredicted" "most surprising" "unpredictable", "off-target effects have never been investigated…"etc.), a variety of immediate downstream "off-target" effects after optogenetic activation and inactivation have been amply described in primates -already starting with the first optogenetic study in monkeys (Han et al., 2009). The main 'selling' point of the study is unsurprising.”

a. We have noted that previously reported paradoxical effects (i.e. Nassi et al. 2015) were noted for cells recorded within the area where light was applied (page 17, paragraph 3). The novelty of our study is that our heterogeneous responses are away from the light source, in the local network where recordings are seldom made.

b. We have added to the discussion a useful counterpoint from Li et al. (Li et al., 2019), that measures the effects of optical suppression at various distances from the light source, in the mouse (page 19, paragraph 3). This study shows that suppression, in the absence of a sensory stimulus, is localized to an area about the size of the fiber optic. Li et al. (2019) conclude that optical suppression is highly localized to the area of the light, with virtually no lateral effects. The novelty of our study is that we show that once the network is driven by a stimulus, focal suppression produces unpredictable activity ripples in the local network, which has a behavioral impact. Our study is particularly useful for future implementations of optogenetics in NHPs aiming to drive/modulate behavior, which has proven notoriously difficult.

9. Reviewer 3 “The data shown in figure 3D-G are puzzling. The P values are mostly negative, i.e. they seem subtractive, rather than additive? That suggests the network does not provide excitation, unlike stated in the main text? Also, the c50 values of many neurons appear very high, and are in a range where sampling was basically absent. All examples shown in figure 2 have c50 values much lower.”

a. Negative P parameter values are not surprising since in this experiment the local network is suppressed, and the reported P parameters are from laser trials. The actual effect of the network under normal conditions can be inferred to be the opposite of this. We have emphasized that the P and Q parameters reported here were from laser trials (page 11, paragraph 4).

b. The c50 values measured from the fits (rather than the Naka-Rushton c50 parameter) are now shown in Figure 3G. See also point 4C above.

10. Reviewer 4: “Figure 1E. rather than one example cell, it would be preferable to show the full laminar profile of suppression at the photoactivated site to demonstrate that light is, indeed, limited to the SG layers. This is shown in Figure S1B-C, but this figure is difficult to interpret correctly because the Y axis is not labeled, and the estimated top and bottom of cortex as well as L4C are not indicated on the laminar plot.”

Figure 1E is actually a population average of all directly suppressed neurons. Unfortunately we could not obtain clear laminar information for the session in Figure 1-supplementary figure 1 (formerly Figure S1), possibly owing to the presence of 2 probes. However, we did measure the vertical distance between cells that were directly suppressed by light based on the distance between recording contacts (spaced every 100 µm). We found that these directly suppressed cells were clustered within 457.1 ± 104.3 µm (mean ± s.e.m., n=31 cells with maximum suppression within 30 ms of light onset). We have included this information in the revised manuscript (page 26, paragraph 3).

11. Reviewer 4: “Isn't it odd that effects on behavioral performance in Figure S7E are only seen at 20% contrast given that type 2 cells are suppressed at contrasts {greater than or equal to} 10?”

Overall, the Type 2 cells show greater suppression for higher contrasts. This is most obvious in Figure 2A-B above, where the suppression for the 100% contrast is stronger compared to control than the degree of suppression at 10% contrast. It is likely that the 20% contrast was high enough to sufficiently induce suppression in Type 2 cells, while being low enough to observe behavioral changes.


Ben-Yishai R, Lev Bar-Or R, Sompolinsky H. 1995. Theory of orientation tuning in visual cortex. Proc Natl Acad Sci U S A 92:3844–3848. doi:10.1073/pnas.92.9.3844

Cox MA, Dougherty K, Adams GK, Reavis EA, Westerberg JA, Moore BS, Leopold DA, Maier A. 2019. Spiking Suppression Precedes Cued Attentional Enhancement of Neural Responses in Primary Visual Cortex. Cereb Cortex 29:77–90. doi:10.1093/cercor/bhx305

Dougherty K, Cox MA, Westerberg JA, Maier A. 2019. Binocular Modulation of Monocular V1 Neurons. Curr Biol 29:381-391.e4. doi:10.1016/j.cub.2018.12.004

Lund JS. 1973. Organization of neurons in the visual cortex, area 17, of the monkey (Macaca mulatta). J Comp Neurol 147:455–495. doi:10.1002/cne.901470404

O’Kusky J, Colonnier M. 1982. A laminar analysis of the number of neurons, glia, and synapses in the visual cortex (area 17) of adult macaque monkeys. J Comp Neurol 210:278–290. doi:10.1002/cne.902100307

Spiridon M, Gerstner W. 2001. Effect of lateral connections on the accuracy of the population code for a network of spiking neurons. Netw Comput Neural Syst 12:409–421. doi:10.1080/net.12.4.409.421

Van Kerkoerle T, Self MW, Roelfsema PR. 2017. Layer-specificity in the effects of attention and working memory on activity in primary visual cortex. Nat Commun 8:13804. doi:10.1038/ncomms13804

Vanni S, Hokkanen H, Werner F, Angelucci A, Helsinki B. 2020. Anatomy and Physiology of Macaque Visual Cortical Areas V1, V2, and V5/MT: Bases for Biologically Realistic Models. Cereb Cortex 30:3483–3517. doi:10.1093/cercor/bhz322

Westerberg JA, Cox MA, Dougherty K, Maier A. 2019. V1 microcircuit dynamics: Altered signal propagation suggests intracortical origins for adaptation in response to visual repetition. J Neurophysiol 121:1938–1952. doi:10.1152/jn.00113.2019


Article and author information

Author details

  1. Ariana R Andrei

    Department of Neurobiology and Anatomy, McGovern Medical School, University of Texas, Houston, United States
    Data curation, Formal analysis, Investigation, Software, Validation, Visualization, Writing - original draft
    Competing interests
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2152-2580
  2. Samantha Debes

    Department of Neurobiology and Anatomy, McGovern Medical School, University of Texas, Houston, United States
    Investigation, Methodology
    Competing interests
  3. Mircea Chelaru

    Department of Neurobiology and Anatomy, McGovern Medical School, University of Texas, Houston, United States
    Methodology, Software
    Competing interests
  4. Xiaoqin Liu

    Department of Neurobiology and Anatomy, McGovern Medical School, University of Texas, Houston, United States
    Investigation, Methodology
    Competing interests
  5. Elsa Rodarte

    Department of Neurology, McGovern Medical School, University of Texas, Houston, United States
    Investigation, Methodology
    Competing interests
  6. John L Spudich

    Center for Membrane Biology, Department of Biochemistry and Molecular Biology, McGovern Medical School, University of Texas, Houston, United States
    Methodology, Validation
    Competing interests
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4167-8590
  7. Roger Janz

    Department of Neurobiology and Anatomy, McGovern Medical School, University of Texas, Houston, United States
    Data curation, Methodology
    Competing interests
  8. Valentin Dragoi

    Department of Neurobiology and Anatomy, McGovern Medical School, University of Texas, Houston, United States
    Conceptualization, Funding acquisition, Investigation, Project administration, Resources, Supervision, Writing - review and editing
    For correspondence
    Competing interests
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9526-0926


National Institutes of Health (5U01MH109146)

  • Valentin Dragoi

National Institutes of Health (R01GM027750)

  • John L Spudich

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


The authors wish to thank Neal Waxham for kindly providing the monoclonal mouse CamKIIα antibody. This work was funded by NIH grants U01MH109146 (VD) and R01GM027750 (JLS).


This study was performed in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. All of the animals were handled according to approved institutional animal care and use committee (IACUC) protocols of the University of Texas, Houston. The protocol was approved by the Committee on the Ethics of Animal Experiments of the University of Houston (Protocol number: AWC-20-0075). All surgery was performed under general anesthesia, and every effort was made to minimize suffering.

Senior Editor

  1. Joshua I Gold, University of Pennsylvania, United States

Reviewing Editor

  1. Martin Vinck, Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Germany


  1. Martin Vinck, Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Germany
  2. Alessandra Angelucci

Version history

  1. Received: January 9, 2021
  2. Accepted: September 9, 2021
  3. Accepted Manuscript published: September 10, 2021 (version 1)
  4. Version of Record published: September 22, 2021 (version 2)


© 2021, Andrei 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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  1. Ariana R Andrei
  2. Samantha Debes
  3. Mircea Chelaru
  4. Xiaoqin Liu
  5. Elsa Rodarte
  6. John L Spudich
  7. Roger Janz
  8. Valentin Dragoi
Heterogeneous side effects of cortical inactivation in behaving animals
eLife 10:e66400.

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