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Rapid compensatory plasticity revealed by dynamic correlated activity in monkeys in vivo

Nature Neuroscience volume 26, pages 1960–1969 (2023)Cite this article

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Abstract

To produce adaptive behavior, neural networks must balance between plasticity and stability. Computational work has demonstrated that network stability requires plasticity mechanisms to be counterbalanced by rapid compensatory processes. However, such processes have yet to be experimentally observed. Here we demonstrate that repeated optogenetic activation of excitatory neurons in monkey visual cortex (area V1) induces a population-wide dynamic reduction in the strength of neuronal interactions over the timescale of minutes during the awake state, but not during rest. This new form of rapid plasticity was observed only in the correlation structure, with firing rates remaining stable across trials. A computational network model operating in the balanced regime confirmed experimental findings and revealed that inhibitory plasticity is responsible for the decrease in correlated activity in response to repeated light stimulation. These results provide the first experimental evidence for rapid homeostatic plasticity that primarily operates during wakefulness, which stabilizes neuronal interactions during strong network co-activation.

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Fig. 1: Strength of network connectivity progressively decreases with repeated optogenetic stimulation.
Fig. 2: Stimulation cycles, not frequency, lead to temporally decreasing correlations.
Fig. 3: Reversal in neuronal correlations depends on behavioral state.
Fig. 4: Model simulations with I–E plasticity replicate experimental findings.

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Data availability

All source data used to generate experimental figures are available at https://zenodo.org/record/7527435.

Code availability

Parameters used for model simulations are provided in the Supplementary Information (Table 1). Model code is available at https://github.com/alanakil/optogenetic_corr_modulation/.

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Acknowledgements

We are grateful to the excellent staff at the Center for Laboratory Animal Medicine and Care at UT Houston, in particular, C. Janssen, A. L. Trimble and M. Horscroft, for their invaluable veterinary insights. The study is funded by grants NSF/NIH CRCNS R01MH115557 and NIH RF1 MH130416 (to K.J.); NSF-DBI-1707400 (to K.J., A.E.A. and R.R.); NSF DMS-1654268 and USAF AFMC FA9550-21-1-0223 (to R.R.) and U01MH109146 and 1R01EY026156 (to V.D.).

Author information

Authors and Affiliations

  1. Department of Neurobiology and Anatomy, University of Texas, Houston, TX, USA

    Ariana R. Andrei, Natasha Kharas & Valentin Dragoi

  2. Departments of Mathematics, Biology and Biochemistry, University of Houston, Houston, TX, USA

    Alan E. Akil & Krešimir Josić

  3. Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, IN, USA

    Robert Rosenbaum

  4. Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA

    Valentin Dragoi

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  1. Ariana R. Andrei
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  5. Krešimir Josić
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Contributions

A.R.A. contributed to conceptualization, methodology, software, formal analysis, investigation, data curation, writing—original draft preparation, visualization a complete list of contributions to the paper; A.E.A. contributed to methodology, software, formal analysis, writing—review and editing, visualization; N.K. contributed to formal analysis, investigation; R.R. contributed to funding acquisition, supervision, writing—review and editing; K.J. contributed to conceptualization, funding acquisition, supervision, writing—review and editing; and V.D. contributed to conceptualization, funding acquisition, supervision, writing—review and editing.

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Correspondence to Ariana R. Andrei or Valentin Dragoi.

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Extended data

Extended Data Fig. 1 Eye position is stable between laser and control trials across early to late trials.

(a) Example eye position traces from Monkey 1. Cyan to magenta: trial order (early to late). (b) Superimposed laser and control traces from a. (c–d) Example session from Monkey 2. Same conventions as a-b. (e) Mean position difference between early-late trials (first, last 20 trials) for x and y coordinates across sessions from Monkey 1 (n = 7). No significant (n.s.) difference across any group. Horizontal red line: group median; edges of the box: 25th and 75th percentiles; whiskers: data extremes; red crosses: individual outliers. (f) Same as e, but showing sessions from Monkey 2 (n = 10). (g-i) Extensive microsaccade analysis: (g) Example eye velocity traces from one session. Green oval: 5 standard deviation threshold used to identify microsaccades (Hafed et al.48). (h) Eye position on trials with microsaccades from same session as in g. (i, left) Microsaccade direction circular histogram, combined for both monkeys on laser (orange) and control (gray) trials. Angle shows microsaccade direction relative to starting point; size of slice indicates the count across trials/sessions. Light gray shaded area denotes quadrant in which receptive field locations were located. No difference in mean microsaccade direction on laser versus control trials (P = 0.92, Watson-Williams test). (i, right) Separating cumulative microsaccade direction into early (first 20 trials) or late (last 20 trials) shows no difference between laser and control (P > 0.04 Watson-Williams test in all conditions; circular statistic). Bar graphs show mean ±s.e.m. Points show individual data points. (j) Two example psychometric curves from detection task. (k) Lower plot, false alarm raster showing trials on which false alarms were reported (first 60 trials). Upper plot shows cumulative false alarm distributions (P = 0.659, Wilcoxon ranked sum test). (l) Box-whisker plot of false alarms on early versus late trials (same conventions as in e-f). No difference between conditions (Kruskal-Wallis test P = 0.08; n = 29 sessions). (m) If monkeys were learning across sessions to ignore an optogenetically-evoked phosphene, a decreasing false alarm ratio between laser and control trials would be expected. Sessions are grouped chronologically per monkey, and separated when significant spans of time (months) separated sessions a monkey (red, blue). No temporal trends were significant for any group (Pearson correlation, P > 0.1 for all 3 session groups separately; P = 1, for all groups together).

Extended Data Fig. 2 Visual stimulus evoked responses were not different between early and late trial blocks.

(a–b) Population averaged evoked stimulus response for a subset of sessions that included visual stimuli of 50% contrast (n = 42 units) for control (a) and laser (b) trials on early (blue) and late (magenta) trial blocks. Early and late trial blocks consisted of the first and last 7 trials, respectively, with no overlap. Black horizontal line shows 300 ms visual stimulus duration. Dashed blue line in b shows time period of laser stimulation (35 Hz, 10 cycles, 10 ms per cycle). (c–d) Average evoked firing rate (average firing rate during first 200 ms minus the baseline firing rate, taken 200 ms prior to stimulus onset) on early versus late trial blocks for each unit on control (c) and laser (d) trials. Red asterisk shows median. P–value result from two-sided Wilcoxon signed rank test comparing firing rates on early versus late trials for each unit.

Extended Data Fig. 3 Correlation and potentiation index results are robust to changes in time windows and cell populations used for calculations.

(a) Recalculating noise correlations using a shorter 50 ms window, rather than a 200 ms window used in Fig. 1, showed a similar pattern of dynamic correlation reversal over trial blocks (indicated by the red arrow). Early versus late correlations on laser trials were significantly different from each other (** P = 0.001). Dashed vertical lines show time of laser onset and offset. (b) Correlation dynamics were similar if we separately considered pairs directly activated by the light (left-side panel), or indirectly via network connections (right-side panel). (c) We found similar effects when subselecting only significantly correlated pairs. To identify significantly correlated pairs, we shuffled control trials, and calculated correlations 1000 times. This created a distribution of chance level correlations. We next found pairs that had correlations on the first control trial block that were absolutely higher than the shuffled correlations > 95% of the chance correlations. The time- course of correlations on laser (blue) and control (gray) for these pairs is similar to the whole population of pairs (Fig. 1g). (d) Time course of correlation difference during rest looking only at the subset of light-responsive pairs on channels also identified as light-responsive during the awake condition (n = 264). (e) Recalculating the potentiation index using a larger 150 ms window around the CCG peak (rather than the 15 ms window used in Fig. 4) resulted in a similar significant increase (right-ward shift) in potentiation index distribution for narrow and broad cell pairs (purple, right side plot). This longer time window resulted in greater potentiation indices compared to the shorter time window (PI distribution means 1.68 versus 1.14, for long and short windows, respectively). Putative excitatory pairs (green, left side plot) did not exhibit a shift in the potentiation index using this longer time window (P = 0.056 Wilcoxon signed rank test). The distributions were significantly different from each other (P = 2.94E10-4, Wilcoxon ranked sum test, I-E vs. E-E, n = 1476 pairs). Arrowheads above distributions show the means. These results suggest that excitatory-excitatory and inhibitory-excitatory interactions can occur on different timescales.

Extended Data Fig. 4 Timing of trial events during dynamic reversal transition period.

(a) Number of laser stimulation trials (all conditions) preceding trial block 8 (orange) and trial block 20 (blue) across all awake sessions (n = 29). Each laser trial consisted of 10 pulses (10 ms per pulse) at 35 Hz. Vertical lines and arrow heads show medians of each distribution. (b) Absolute time, in minutes, measured from the start of the first trial in a session to reach trial block 8 (orange) and trial block 20 (blue). Vertical lines and arrow heads show medians. (c) Distributions of inter-trial intervals for each session, measured as the end of one completed trial to the start of the next complete trial. Blue circles show session median ±s.e.m. Red horizontal line show median across all sessions. (d–f) Same as a-c, but for rest sessions (n = 10).

Extended Data Fig. 5 Dynamic reversal of correlations is distributed across pairs of neurons.

(a) Population correlation difference on laser versus control trials. Solid line shows mean, envelope shows s.e.m. Analysis presented in this figure compares the correlation difference on early (vertical cyan) versus late (vertical magenta) trial blocks, averaged over the first/last 5 trial blocks. (b) Population distributions of spike count correlations during control (top row) and laser (bottom row) conditions on early (left column) and late (right column) trial blocks. Early versus late distributions were significantly different on laser but not control trials (P = 7.279−10, Kruskal–Wallis test, d.f. = 3, post–hoc Tukey test). Early laser distribution was significantly different from all other distributions, while the late laser distribution was significantly different from the late control distribution. (c) Example session data showing differences in correlations for individual pairs (n = 120). Red asterisks shows median of pairs in this session. Lower right quadrant shows pairs that exhibit both the early increase and the late decrease in correlations on laser versus control trials. Percentages denote the percent of total pairs found in each quadrant for the example session pairs (all pairs across sessions). (d) Distribution of early versus late noise correlation changes for the example session shown in panel c. Same conventions as panel b. (e–f) Same as panels c-d, but showing distributions across pairs from the model for one example simulation. Vertical lines and titles show means of early (cyan) and late (magenta) distributions.

Extended Data Fig. 6 Stimulation frequencies less than 35 Hz produce a reversal of correlation dynamics over time.

(a) Correlations on laser and control trials during the first block of stimulation using only data from sessions stimulated with less than 35 Hz (unlike Fig. 2f, which also includes 35 Hz sessions; n = 243 pairs). Correlations are calculated in 200 ms windows, with a 50 ms slide. (b) Same as panel a, but for the first 20 trials of the second frequency block (n = 950 pairs). (c-d) Firing rates associated with laser (color, solid line) and control (gray, dashed line) trials on Block 1 and Block 2 respectively. All plots show mean and s.e.m. Vertical lines denote laser onset and offset times.

Extended Data Fig. 7 Dynamic reversal of correlations is independent of laser stimulation frequency in network model.

(a) Drop in mean correlations at center time bin of laser period for networks with varying laser stimulation frequencies. (Error shows 95% confidence intervals; n = 10 neural network simulations. Same for panels d-f). (b) Change in mean spike count correlations in a sample simulation of the numerical experiment in the awake condition with laser frequency 10 Hz. Correlations over the laser period drop below control due to changes in synaptic weights. (c) Firing rates of ChR2–expressing neurons averaged over the laser period. Higher laser frequencies evoke stronger responses. (d) Change in synaptic weights compared to baseline (first block) and computed over a window of 200 ms and 20 trials. (e) Evolution of difference in synaptic weights in laser vs control trials for different frequencies, f. (f) Mean inhibitory input to ChR2–expressing neurons changes little as changes in synaptic strengths are heterogeneous, and the network remains in balance. Inhibitory input is normalized by the first trial block, and computed over a time window of 200 ms and 20 trials.

Extended Data Fig. 8 Dynamic reversal of correlations only occurred when i-e connections were plastic in the model network.

(a) Mean correlations at the center time bin during the laser period decreased over trial blocks when inhibitory synapses (ie or both ie and ii) were plastic. However, we observed a reversal in mean correlations, as seen in experimental data, only when ie connections were plastic, and not when both ie and ii synapses were plastic. Mean correlations remained nearly constant in the absence of STDP or when excitatory connections (ee or ei) were plastic. (Error shows 95% confidence intervals; n = 7 neural network simulations. Same for panels b-c). (b) Change in mean synaptic weights compared to baseline (first block) and computed over a window of 200 ms and over 20 trials. For every pair of neuronal populations where weights underwent STDP, the mean weight potentiated as a consequence of repeated stimulation and the nature of the plasticity rules. Thus, only changes in ie weights resulted in a reversal of mean correlations (panel (a)). (c) Evolution of the difference in synaptic weights in laser vs control trial blocks for different STDP rules. Under all rules, mean weights tended towards a steady state where they were approximately equal in laser and control trial blocks. However, mean weights were larger in early laser trial blocks than in early control trial blocks only under inhibitory plasticity. Thus, even though mean weights were potentiated under each STDP rule (panel (b)), mean correlations decreased (panel (a)) only when the difference between mean weights in laser vs control trials also decreased over trial blocks.

Extended Data Fig. 9 Laminar distributions of potentiated and depressed pairs.

(a) Noise correlation difference (laser minus control) for pairs of units with broad waveforms with potentiation index (‘PI’) > 1 (potentiated, light green) or < 1 (depressed, dark green). Asterisks indicate trial blocks in which the change in correlations was significantly different from zero (Wilcoxon signed rank test, two-sided, false discovery rate corrected, P ≤ 0.0059). Vertical dotted line shows trial block 4, where the laser induced increase in correlated variability begins to decline between broad pairs. (b) Same as panel A, but for pairs of narrow and broad wave- form pairs. Asterisks indicate significantly different from zero trial blocks (Wilcoxon signed rank test, two-sided, false discovery rate corrected, P ≤ 0.0068). Vertical dotted line shows trial block 4, where correlated variability is first significantly increased between broad-narrow cell pairs with a PI > 1. The common inflection point between panels A and B (dotted vertical line at trial block 4) suggests that broad cells become more coupled with narrow cells and simultaneously less coupled with other broad cells, consistent with the idea that I–E connections are potentiated leading to the overall decrease in correlated variability across trials. (c) Pairs were assigned to layers (supragranular ‘SG’, granular ‘G’, and infragranular ‘IG’) using the current source density method (21, 23). Distributions show potentiation indices across broad wave- form pairs in each layer combination (dark green indicates PI < 1, light green indicates PI > 1). Distributions are only shown for layer combinations in which the median PI was significantly different from unity (two-sided Wilcoxon signed rank test, P values as shown). (d) Same as panel c, but for narrow–broad waveform pairs. (e) Summary of results in panels c–d. Broad–narrow cell pairs were potentiated across SG–G and SG–IG, while broad pairs were potentiated within SG but depressed between SG–G and within G.

Extended Data Fig. 10 Transition of network model from awake to rest condition.

(a) Drop in mean correlations at center time bin of laser period for networks with varying synaptic weight scaling factors. (Error shows 95% confidence intervals; n = 10 neural network simulations. Same for panels d-f). (b) Peri–stimulus time histogram (PSTH) of stimulated cells in awake condition. (c) PSTH of stimulated cells in rest condition. (d) Change in synaptic weights compared to baseline (first block) and computed over a window of 200 ms and 20 trials. (e) Evolution of difference in synaptic weights in laser vs control trials for different values of scaling factor f. (f) Mean inhibitory input to ChR2–expressing neurons changes little as changes in synaptic strengths are heterogeneous, and the network remains in balance. Inhibitory input is normalized by the first trial block, and computed over a time window of 200 ms and 20 trials. (g) Change in mean spike count correlations in a sample simulation of the numerical experiment in the awake condition. Correlations over the laser period drop below control due to changes in synaptic weights. (h–i) Same as (g), but for spike count covariances and variances. Changes in correlations are due to changes in covariability, not in variance. (j–l) Same as (g–i) but for 15 the rest condition (f = 0.4). Correlations, covariance, and variance remain high throughout the laser period.

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Andrei, A.R., Akil, A.E., Kharas, N. et al. Rapid compensatory plasticity revealed by dynamic correlated activity in monkeys in vivo. Nat Neurosci 26, 1960–1969 (2023). https://doi.org/10.1038/s41593-023-01446-w

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