Reliable Sensory Processing in Mouse Visual Cortex through Cooperative Interactions between Somatostatin and Parvalbumin Interneurons

Abstract

Intrinsic neuronal variability significantly limits information encoding in the primary visual cortex (V1). However, under certain conditions, neurons can respond reliably with highly precise responses to the same visual stimuli from trial to trial. This suggests that there exists intrinsic neural circuit mechanisms that dynamically modulate the intertrial variability of visual cortical neurons. Here, we sought to elucidate the role of different inhibitory interneurons (INs) in reliable coding in mouse V1. To study the interactions between somatostatin-expressing interneurons (SST-INs) and parvalbumin-expressing interneurons (PV-INs), we used a dual-color calcium imaging technique that allowed us to simultaneously monitor these two neural ensembles while awake mice, of both sexes, passively viewed natural movies. SST neurons were more active during epochs of reliable pyramidal neuron firing, whereas PV neurons were more active during epochs of unreliable firing. SST neuron activity lagged that of PV neurons, consistent with a feedback inhibitory SST→PV circuit. To dissect the role of this circuit in pyramidal neuron activity, we used temporally limited optogenetic activation and inactivation of SST and PV interneurons during periods of reliable and unreliable pyramidal cell firing. Transient firing of SST neurons increased pyramidal neuron reliability by actively suppressing PV neurons, a proposal that was supported by a rate-based model of V1 neurons. These results identify a cooperative functional role for the SST→PV circuit in modulating the reliability of pyramidal neuron activity.SIGNIFICANCE STATEMENT Cortical neurons often respond to identical sensory stimuli with large variability. However, under certain conditions, the same neurons can also respond highly reliably. The circuit mechanisms that contribute to this modulation remain unknown. Here, we used novel dual-wavelength calcium imaging and temporally selective optical perturbation to identify an inhibitory neural circuit in visual cortex that can modulate the reliability of pyramidal neurons to naturalistic visual stimuli. Our results, supported by computational models, suggest that somatostatin interneurons increase pyramidal neuron reliability by suppressing parvalbumin interneurons via the inhibitory SST→PV circuit. These findings reveal a novel role of the SST→PV circuit in modulating the fidelity of neural coding critical for visual perception.

Keywords: inhibitory neurons; optogenetics; reliable processing; two-photon microscopy; visual cortex.

Figure 1.

SST and PV-INs respond during distinct epochs of EXC neuron activity. A, Schematic showing experimental setup and method to record from EXC neurons. B, Raster plots (trials vs time) of two simultaneously recorded EXC neurons showing reliable and sparse responses to the same movie. Gray lines show trial-averaged responses, and shaded areas indicate SEM over trials. Shaded purple bar shows time period (epoch) when these EXC neurons are reliably activated. C, Scatter plot showing strong negative (positive) correlation between intertrial variance (mean) and response reliability. Each data point is the mean response reliability and the across-trial variance of each imaged population, error bars indicate SEM (19, each with 22–87 neurons; 10 mice, 6 female, 4 male). D, Scatter plot showing the correlation between reliability computed from either DF/F or inferred spike rates. E, Pie charts showing moviewise distribution of reliably responding EXC (top), PV (middle), and SST (bottom) neurons. Movies that recruit a greater fraction of reliably responding EXC neurons also recruit more reliably responding PV and SST-INs. F, Comparison between evoked (averaged from 5 different movies) and spontaneous (Spont., gray screen) activity for all the three cell types, expressed in number of inferred events per second. All cell types showed a significant increase in evoked response rate compared with spontaneous activity. Error bars indicate SEM. G, Histogram showing the fraction of active PV (left) and SST-INs (right) in 200 ms time bins aligned to peak EXC population reliability. Triangles above the histograms indicate mean time to peak activity. There was a significant difference between PV and EXC neuron activation times (p < 10⁻⁶) but no significant difference between activation times for SST and EXC neurons (p = 0.129). Data in G are from 10 mice (sex as mentioned above; 1101 EXC neurons, 120 SST-INs, 186 PV-INs). H, Bar plots comparing the median fraction of active PV and SST-INs during epoch of unreliable and reliable EXC neuron firing, respectively. Error bars indicate 95% CI. I, Method to image INs. J, Example raster plot of a PV and an SST-IN to the same movie. Format same as B, K, Histogram of PV and SST-IN reliability in relation to EXC neuron reliability (gray). Triangles above the histograms indicate mean reliability pooled over all neurons. Inset, Comparison of median reliability for all cell types. Each data point is the median reliability of each imaged population. Data from PV = 8 mice (690 neurons; 5 male, 3 female); SST = 8 mice (368 neurons); EXC = 10 mice (1101 neurons). All p values computed using grouped Bonferroni-corrected rank-sum test. N.S., non-significant.

Figure 2.

SST-INs are temporally delayed relative to PV-INs in reliably processed movies. A, Left, Experimental setup. Briefly, a 1020 nm laser and a 920 nm laser were combined using a half-wave plate (HWP) and a polarizing beam splitter (PBS) to optimally activate jRGECO1a and GCaMP6f in SST and PV-INs, respectively (see above, Materials and Methods). Middle, Example field of view showing colabeled PV and SST-INs. Image covers a cortical area of 150 µm × 150 µm. Right, Example calcium transients from simultaneously recorded interneurons. B, Top, Images of jRGECO1a-expressing SST-INs and GCaMP6f-expressing PV-INs taken at 920 nm and 1020 nm, respectively. Bleed through from the green to the red channel can be clearly seen at 920 nm (yellow arrowheads). In contrast, no green signal can be detected at 1020 nm. Bottom, No jRGECO1a activity can be detected at 920 nm compared with 1020 nm. In contrast, no GCaMP6f activity can be detected at 1020 nm. Each trace is matched to the same neuron and shows activity in response to a series of natural movies (800 s long, acquired at 20 Hz). C, Left, Trial-averaged responses from a pair of simultaneously recorded PV and SST-INs. Right, CCG of this pair. Orange line shows Gaussian fit to trial-averaged CCG. Shaded areas indicate SEM over trials. D, Difference in correlation for two different movies for the same PV and SST-IN pair. E–G, More reliable movies have a stronger SST peak activity compared with that of PV. E, Longer delays between SST and PV peak activity (F) and stronger PV-SST correlation at peak delay (G). Data are from 2292 pairs, 5 mice (3 female, 2 male). Data points denote median ± 95% CI for each movie; p values computed using F test to measure significance of the trend relative to a constant model.

Figure 3.

SST-INs strongly inhibit PV-INs via the SST→PV circuit. A, Inset, Cre-dependent ArchT was expressed in SST-INs, and Flp-dependent GCaMP6f was expressed in PV-INs in SXP mice. Representative trial-averaged firing rate from one PV-IN showing that suppressing SST-INs strongly increases the firing rate of PV-INs. Shaded area indicates SEM over trials. B, Left, Response rate change in one representative population of PV-INs (8 neurons) aligned to laser onset. All PV-INs increase firing rates following suppression of SST-INs. Right, Quantification of change in response rate of PV neurons following SST suppression relative to response rate on Laser-off trials. There is a significant increase in PV activity (p < 0.001, permutation test) regardless of when SST-INs are suppressed during a movie. Shaded area, 95% CI. Data from three mice (121 PV neurons). C, Inset, Cre-dependent ChR2 and jRGECO1a were expressed in SST-INs, whereas Flp-dependent GCaMP6f was expressed in PV-INs in SXP mice. Representative example trial-averaged firing rate from one simultaneously imaged PV-SST pair, showing a strong suppression of PV-INs following SST-IN activation. The peak suppression occurs almost at the same time as SST-INs reach peak activation. D, Left, Example cross-correlogram between all pairs of simultaneously recorded SST (n = 4) and PV-INs (n = 9) for an example population, showing the effect of SST activation on the time lag between PV and SST-INs. Gaussian fit is not shown. Data here are averaged over all stimulation epochs. Shaded area indicates SEM. Right, Box-whisker plot showing that activating SST-INs increases the time lag between PV and SST-INs (p = 0.235, Bonferroni-corrected rank-sum test). Data from three mice (84 PV neurons, 39 SST neurons), all males.

Figure 4.

Increasing PV-IN activity reduces EXC neuron reliability. A, Diagram describing random stimulation strategy. A brief laser pulse (stimulation event) was applied at 22 equally spaced time points during a 4 s movie (light blue lines). At each movie repetition, stimulation event time is drawn from this distribution at random (dark blue line). The bottom plots show the timing of each stimulation event in relation to the reliability of an example EXC neuron (black line). Following this, post hoc analysis was used to identify stimulation events that occurred within periods of reliable firing and unreliable firing (shaded purple and green, respectively). B, Diagram of experimental setup. C, Left, Representative example of an EXC neuron that is suppressed following PV activation. Blue line indicates the time of the stimulation event. Right, Change in firing rate for each PV stimulation event. To facilitate comparisons between movies and mice, all neurons were aligned to have a maximum reliability at 1 s. All shaded areas are 95% CI of the median. Yellow circles represent nonsignificant change (relative to 0) and were computed using a permutation test; p values (Bonferroni-corrected rank-sum test) compare changes in firing rate between epochs of reliable versus unreliable responses (shaded bars). D, Change in EXC reliability for each stimulation event. E, Left, Representative raster plot of an EXC neuron showing a reduction in reliability following PV activation during the reliable firing epoch. Right, Box-whisker plots summarizing the effect of PV activation on EXC neuron reliability. Each dot is pooled data from one population; p value computed using Bonferroni-corrected Wilcoxon rank-sum test. F, Same as E but shows no change in reliability when PV-INs are activated during epochs of unreliable firing. G, H, Scatter plots quantifying the relationship between ΔReliability and a change in rate (ΔRate, left) or a change in between-trial variability (ΔVariance, right). Error bars indicate 95% CI of the median; p values computed from multivariate linear regression analysis. I, Diagram describing method to study the effect of PV activation on SST-INs. J, Left, Representative SST-IN that shows no change following PV activation. Right, No change in SST rate for all PV activation events. K, No change in SST reliability for all PV activation events.

Figure 5.

Deconvolution and analysis window length does not affect reliability; change in rate and reliability is not because of stimulation laser artifacts. A, Change in reliability measured using DF/F without deconvolution for PV neurons. B, Change in reliability measured using DF/F without deconvolution for SST neurons. Data same as Figures 4 and 6. C, Percent change in EXC neuron response following laser activation of PV-INs. All data analysis was limited to a 600 ms window indicated by the gray box. During this period, the laser maximally suppresses EXC neurons. D, Plot of change in EXC reliability following PV activation at stimulus onset over different analysis window lengths. We found that changing the window length within 50 (1 frame) to 600 ms (12 frames) following laser offset did not significantly affect the reduction in EXC reliability caused by PV activation. The effect, however, was significantly diminished when the entire 4 s stimulus-on period (open circle) was included in the analysis. This is mainly because of the fact that PV-INs stop exerting an inhibitory effect on EXC neurons after 650 ms as shown in C. E, F, Same as C and D but for SST-IN activation instead. Again, changing the duration of the analysis window does not affect the increase in EXC reliability caused by SST activation. Data in C–F indicate mean ± SEM for stimulation at stimulus onset. Analysis of other stimulation epochs yielded qualitatively similar results. All shaded areas are 95% CI of median. G–I, No significant change in response rate in tdTomato-expressing mice following stimulation with either blue (G, H) or green laser (I; see above, Material and Methods). J–L, No significant change in reliability in the same tdTomato-expressing mice. Data pooled from PV-tdTomato (blue laser) = three mice (120 neurons), PV-tdTomato (green laser) = three mice (102 neurons), SST-tdTomato = three mice (98 neurons). All p values are nonsignificant (permutation test). Shaded areas indicate 95% CI of median. N.S., non-significant.

Figure 6.

Increasing SST-IN activity increases EXC neuron reliability. A, Diagram of experimental setup. B, Left, Representative EXC neuron that shows a modest decrease in firing rate following SST activation (blue bar). Right, Change in firing rate for each SST stimulation event shown in relation to EXC neuron reliability on light-off trials. C, Change in EXC neuron reliability for each SST stimulation event. D, Representative raster plot of an EXC neuron and box-whisker plot showing no change in reliability following SST activation during epoch of most reliable firing. E, Same as D but showing that SST activation during epoch of least reliable firing can increase EXC neuron reliability. F, G, Scatter plots quantifying the relationship between ΔReliability and a change in rate (ΔRate, left) or a change in between-trial variability (ΔVariance, right). Error bars are 95% CI of the median; p values computed from multivariate linear regression analysis. Data in B–G are from 8 SST-ChR2 mice (622 neurons, 19 populations). H, Diagram describing method to study the SST activation on PV-INs. I, Left, Representative PV-IN that is suppressed following SST activation. Right, PV-IN firing rate is significantly suppressed for all SST activation events. J, SST activation reduces the reliability of PV-INs. Data in same format as Figure 4 and are from 4 SXP mice (372 PV neurons). K, L, Diagram summarizing photoactivation results. N.S., non-significant.

Figure 7.

Computational model accurately captures temporal dynamics and variability to natural movies. A, Diagram illustrating connectivity among the three major units simulated in this model. Round connections indicate excitatory synapses, whereas blunt connections indicate inhibitory synapses. B, See above, Material and Methods for details of the LNP model. C, Example input spike trains produced by the LNP model along with estimated firing rates (blue lines, normalized to maximum) to two different natural movies. Note that the model captures the different temporal properties of each movie, and as a result produces different inputs for each movie. For each movie, these spike trains are summed and used as an input to either EXC, PV, and SST units. D, The LNP model is able to recapitulate the moviewise trend in EXC neuron reliability observed in the experimental data (black dots). The gray dots are the average reliability of EXC units in the model (from 500 simulations; see above, Materials and Methods). Error bars indicate SEM.

Figure 8.

Computational model predicts that SST-INs increase reliability by suppressing PV-Ins. A, Representative simulation showing the response of PV and SST units to a natural movie. Inset, Zoomed view of the onset dynamics to highlight the temporal lag between PV and SST units. B, The delay between PV and SST units is reduced when the SST→PV connection is removed. Box-whisker plots quantify the change in time lag between PV and SST units caused by parametrically reducing the strength of the SST→PV connection from normal weight to zero (no connection). Data are pooled from 100 simulations each with randomly drawn connection weights; p value computed using Kruskal–Wallis one-way ANOVA relative to the model with normal SST→PV connection. C, Left, Significant correlation between PV-SST delay duration and EXC unit reliability for the normal model, which is lost when the SST→PV connection is cut. Right, Removing the SST→PV circuit increases PV unit firing rate while suppressing SST and EXC units. Error bar indicates SEM over simulations. D, Model predicts that activating SST activation during epoch of unreliable firing will increase the reliability of EXC units. Inset, Representative raster plot of an EXC unit (orange arrow). E, Large changes in EXC unit reliability are associated with a large decrease in PV unit firing rate. F, Scatter plot showing no significant relationship between the changes in EXC unit reliability and firing rate following SST unit activation. Each data point is an independent model simulation (see above, Materials and Methods). G, Model predicts that PV activation will reduce reliability. H, SST-induced suppression is reduced when the SST→PV connection is cut. Box-whisker plot showing the change in PV firing rate as the SST→PV connection strength is varied; p value computed using Kruskal–Wallis one-way ANOVA relative to the model with normal SST→PV connection. I, Left, Box-whisker plot showing that the change in EXC unit reliability induced by SST activation varies as the connection strength is changed. Right, The effect of PV activation on EXC reliability is independent of the SST→PV connection strength. J, Suppressing PV units will result in an increase in variability. All data points are an independent simulation in which a natural movie is repeated 30 times. To test robustness, we repeated each simulation 100 times, each with randomly drawn connection weights; p values in the scatter plots are computed from linear regression (see above, Materials and Methods).

Figure 9.

Suppressing PV-INs increases EXC neuron reliability. A, Experimental setup. B, Arch activation transiently suppresses PV-INs with short latency following laser onset. C, Left, Suppressing PV-INs transiently increases the response rate of EXC neurons. Right, Change in firing rate for all PV suppression event. D, Change in EXC neuron reliability, aligned to reliability on Laser-off trials. E, Left, Representative raster plot of an EXC neuron showing no change in reliability following PV suppression during epoch of most reliable firing. Right, Box-whisker plot summarizing the effect of PV suppression. Each dot represents the median reliability from each imaged population; p value computed using Bonferroni-corrected rank-sum test. F, Same as E, but showing an increase in reliability following PV suppression during epoch of least reliable firing. G, Changes in reliability that occur when PV-INs are suppressed during epoch of most reliable firing are weak because of ΔRate (left) but not ΔVariance (right). Each data point in I and J shows median change for each imaged population. Error bars indicate 95% CI of the median; p values computed from multivariate linear regression analysis. H, Same as G but shows that the increase in reliability is strongly associated with a reduction in variance but not a change in rate. Data in this figure are from 8 mice (634 neurons, 22 populations).