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Collicular circuits for flexible sensorimotor routing

Abstract

Context-based sensorimotor routing is a hallmark of executive control. Pharmacological inactivations in rats have implicated the midbrain superior colliculus (SC) in this process. But what specific role is this, and what circuit mechanisms support it? Here we report a subset of rat SC neurons that instantiate a specific link between the representations of context and motor choice. Moreover, these neurons encode animals’ choice far earlier than other neurons in the SC or in the frontal cortex, suggesting that their neural dynamics lead choice computation. Optogenetic inactivations revealed that SC activity during context encoding is necessary for choice behavior, even while that choice behavior is robust to inactivations during choice formation. Searches for SC circuit models matching our experimental results identified key circuit predictions while revealing some a priori expected features as unnecessary. Our results reveal circuit mechanisms within the SC that implement response inhibition and context-based vector inversion during executive control.

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Fig. 1: Individual SC neurons encode task and choice variables during flexible sensorimotor routing.
Fig. 2: SC population contains strong task context information and earlier choice information than prefrontal cortical populations.
Fig. 3: Distinct roles of SC subpopulations.
Fig. 4: A relationship between task context and choice encoding around stimulus onset, suggesting two groups of SC delay/choice neurons.
Fig. 5: SC delay activity is required for the Anti task.
Fig. 6: Dynamical SC model for flexible sensorimotor switching replicates optogenetic inactivation patterns.
Fig. 7: Common circuit mechanisms across heterogeneous model dynamics.

Data availability

Processed behavioral, electrophysiological, optogenetic and video data are publicly available on GitHub: https://github.com/Brody-Lab/Proanti. Raw data are archived at Princeton University and available from the corresponding author upon reasonable request. Modeling data are publicly available on GitHub: https://github.com/carlosbrody/superior_colliculus_mutual_inhibition.

Code availability

All software used for behavioral training is available on the Brody lab website at http://brodylab.org/code/proanti-code. All custom data analysis and modeling codes are freely available on the corresponding GitHub repositories: https://github.com/Brody-Lab/Proanti (analysis) and https://github.com/carlosbrody/superior_colliculus_mutual_inhibition (modeling).

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Acknowledgements

We thank K. Osorio and J. Teran for animal and laboratory support. This work was funded by the Howard Hughes Medical Institute. C.A.D. was supported by a Howard Hughes Medical Institute predoctoral fellowship. C.A.D. and M.P. are supported by a Simons Collaboration on the Global Brain postdoctoral fellowship.

Author information

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Authors

Contributions

C.A.D. collected electrophysiological and optogenetics data. M.P. and C.A.D. analyzed electrophysiological data. C.A.D. analyzed the optogenetics data. M.P., A.T.P., C.D.B. and A.J.R. generated and analyzed modeling results. A.A. and C.D.K. carried out the acute optogenetics experiments. J.C.E. and C.D.K. played an advisory role on electrophysiological and optogenetics experiments, respectively. C.A.D., J.C.E. and C.D.B. conceived the project. C.A.D., M.P., A.T.P. and C.D.B. wrote the paper with comments from J.C.E. C.D.B. was involved in all aspects of experimental design and data analysis.

Corresponding authors

Correspondence to Chunyu A. Duan or Carlos D. Brody.

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The authors declare no competing interests.

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Peer review information Nature Neuroscience thanks Gidon Felsen and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Post-surgery performance for implanted rats.

Asymmetries between Pro and Anti response time (RT), accuracy, and task switch cost in implanted rats. a, Normalized RT distributions of an example rat. Histograms of correct Pro (n = 3894 trials) and correct Anti (n = 3323) RTs are shown on top and error Pro (n = 1239 trials) and error Anti (n = 1161 trials) RTs are shown in the bottom. Each curve is normalized to have a total area of 1. Median RTs for Pro and Anti hits and errors are indicated by vertical bars; 95% confidence intervals across trials for each trial type are indicated by horizontal bars. b, RT summary of 16 individual rats (7 for SC and PFC neural recordings and 9 for optogenetic inactivation experiments). Left: median RTs for Anti hits and Pro hits for all rats (n = 16). ***P = 4 × 10−4, two-sided bootstrap test. Right: RT difference between Pro and Anti, hits and errors, averaged across all rats (n = 16). For each rat, the difference between median RTs of paired conditions was calculated. White bar shows the mean and s.e.m. across rats for Anti hit RTs minus Pro hit RTs, P = 4 × 10−4, two-sided bootstrap test. Green bar shows Pro hit RTs minus Pro error RTs, P = 4 × 10−4, two-sided bootstrap test. Orange bar shows Anti hit RTs minus Anti error RTs, P = 4 × 10−4, two-sided bootstrap test. c, Pro and Anti performance for individual rats (n = 16). Mean and s.e.m. of Pro and Anti performance are computed over sessions for each rat and plotted against each other. Average Pro (green) and Anti (orange) performance across rats was plotted in the upper left corner (n = 16). Pro versus Anti, P = 0.003, two-sided bootstrap test. d, Switch cost asymmetry. Left: percent correct as a function of trial number relative to a task block switch for one example rat. Each data point is the mean and s.e.m. across trials for Pro and Anti accuracy on three trials before and after the switch. Right: average accuracy switch cost for Pro trials (P = 4 × 10−4) and Anti trials (P = 4 × 10−4) across rats (n = 16). The cost of switching to Pro was larger than the cost of switching to Anti (P = 0.002), two-sided bootstrap tests.

Extended Data Fig. 2 Detailed timing of behavioral events in recording and optogenetic sessions.

a, A light in the center port indicates that rats should nose poke there to initiate a trial and keep their noses there until the center light offset (‘‘fixation’’ period). During the first 1 s of the fixation period, a Pro or Anti sound is played to indicate the current task, followed by a 500-ms silent delay. The center light is then turned off, indicating that the animal is now free to withdraw from the center port, and the moment it withdraws, a left or right light is turned on to indicate the target location. The temporal gap between fixation offset (that is, end of the delay period) and target stimulus onset was controlled by animals and was thus variable on each trial (mean = 127 ms after fixation offset). Reaction Time (RT) is defined as the time from target onset until side poke. The 3 vertical lines correspond to the vertical lines in Fig. 13. b, Similar to a, for optogenetic sessions. To ensure that all sub-trial optogenetic inactivation conditions have the same laser duration (750 ms, green shade), rats were trained on a modified version of the behavior where the task cue period and the delay period both lasted 750 ms. Choice period inactivation started at the onset of visual target and lasted 750 ms, covering the time it took animals to form and execute the orienting choice into the side poke (690.8 ± 39.1 ms, mean ± s.e.m. across animals’ median RT in optogenetic inactivation sessions).

Extended Data Fig. 3 Individual PFC neurons encode task and choice variables during flexible sensorimotor routing.

a-c, Same as in Fig. 1c-e, for the PFC population (291 out of 331 total neurons).

Extended Data Fig. 4 Breakdown of electrophysiology results by rat.

Similar to Fig. 1c,e, separated by recordings from individual rats. Mean performance on Pro and Anti trials during each rat’s recording sessions are shown above each panel. Rat ‘J205’, ‘A117’, and ‘Z014’ had implants both in SC and in PFC. Rats with fewer than 20 neurons were excluded from this analysis.

Extended Data Fig. 5 Relationship between task context (Pro/Anti) and choice (Contra/Ipsi) d’ across the SC population.

a, For each SC neuron, the signed Pro/Anti d’ computed at the time of peak Pro/Anti selectivity was plotted against the signed Choice d’ computed at the time of peak Choice selectivity. No correlation is observed (Pearson’s correlation coefficient r = 0.06, n = 193, P = 0.3821, t-test). b, Correlation between Pro/Anti d’ and Choice d’ for the whole SC population computed at all time points. The two black dashed lines in the color bar indicate the correlation values that are not significantly different from 0 (P > 0.05). The correlation is significantly different than 0 only at times shortly after the appearance of the target stimulus. Positive correlation corresponds to either a Pro (d’ > 0) and Contra (d’ > 0) preference or an Anti (d’ < 0) and Ipsi (d’ < 0) preference. Bin size = 250 ms, centered (that is, it includes spikes from ± 125 ms relative to the plotted timepoint.

Extended Data Fig. 6 Information regarding the side light stimulus in SC neurons.

a, SC population decoding performance (mean ± s.d.) for linear classification of correct Pro versus Anti trials (task, red line), Go-Left versus Go-Right trials (choice, blue line), and Left-Light versus Right-Light trials (light stimulus, black line). Compared to the early and strong choice information in the SC population, linearly decodable information related to the light stimulus appeared later and weaker, suggesting that information being received by deep SC layer neurons about which side the Light is on is combined nonlinearly and very rapidly with context information, to produce early, linearly decodable information about choice. b, Matrix of light stimulus (left side light/ right side light) selectivity for the SC neural population, similar to Fig. 1e.

Extended Data Fig. 7 Effect of bilateral SC inactivation and YFP control.

a, Effect of full-trial and sub-trial inactivations of bilateral SC on Pro (green) and Anti (orange) error rate (mean and s.e.m.) compared to YFP controls (gray). Full-trial: n = 662, 615 for Pro and Anti inactivation trials; n = 362, 322 for Pro and Anti control trials. ***P = 4 × 10−4, two-sided permutation test. Task cue: n = 413, 401 for Pro and Anti inactivation trials; n = 290, 271 for Pro and Anti control trials. Delay: n = 562, 527 for Pro and Anti inactivation trials; n = 315, 260 for Pro and Anti control trials. ***P = 4 × 10−4; **P = 0.0012, two-sided permutation tests. Choice: n = 547, 506 for Pro and Anti inactivation trials; n = 319, 261 for Pro and Anti control trials. All paired statistics shown here are computed using a two-sided permutation test, shuffled 5000 times. b, Effect of full-trial and sub-trial inactivations of bilateral SC on response time (RT). For each behavioral session, a median RT on non-stimulated control trials is calculated and subtracted from the RTs on inactivation trials, and these normalized RT changes due to inactivation are plotted here. Each curve is normalized to have a total area of 1. Vertical bars show the median RT changes for correct Pro and Anti trials; s.e.m. across trials for each trial type are indicated by horizontal bars. A shift to the right indicates slowing due to inactivation and a shift to the left indicates speeding.

Extended Data Fig. 8 Variability across model solutions in dynamics and parameters, and common functional properties.

a, Distribution of choice preference (d’) for Pro and Anti model units from 373 individual model solutions during the choice period (Methods). Note that although most Anti units (red shading) were Ipsi-preferring, we also observed Anti/Contra-preferring units (red shaded counts to the right of zero), similar to the SC neural data (Fig. 4). In contrast, all Pro units (gray shading) were Contra-preferring. b, The dimensionality of parameters across model solutions, and of dynamics across model solutions (n = 373 solutions). Eight SVD dimensions are required to explain 90% of the variance in dynamics across model solutions. Ten PCA dimensions are required to explain 90% of the variance in parameters across model solutions. c, Variance explained by each dimension of PCA performed on each model solution’s dynamics. Full trial: PCA computed on all time points. Delay period only: PCA computed only during the delay period. Target period only: PCA computed only during the target period. Mean ± s.d across 373 model solutions. d, The connectivity matrix of each model solution was analyzed via the Schur Decomposition (Methods). All solutions (n = 373) contained one of each of the following functional modes: All, Side of brain, Task, and Diagonal. The percentage of solutions with positive eigenvalues for each mode is reported.

Extended Data Fig. 9 A 6-node SC model replicates results from the 4-node SC model.

a, Schematic of the 6-node SC model, in which each hemisphere contains two Anti pools and one Pro pool. b, Format and results similar to Fig. 7e. Histogram of horizontal weights between the two Pro units (as illustrated by the insert cartoon) for all 36 six-node model solutions. Red arrow marks average value across solutions. Solutions do not require inhibitory weights between the two Pro/Contra pools. c, Format and results similar to Fig. 7c. Scatter plot of diagonal weights (from Anti units to the Pro unit on the opposite hemisphere) against vertical weights (from Anti units to the Pro unit on the same hemisphere), for all model solutions. Each dot represents the average weights from the two Anti units in a solution. Red line marks unity. d, Format and results similar to Extended Data Fig. 8a. Histogram of choice d’ for Pro and Anti nodes during the choice period (n = 36 model solutions). We observed both Anti/Ipsi and Anti-Contra-preferring units, with a majority of Anti/Ipsi units, as in the experimental data. e, Similar to Extended Data Fig. 8d. Percentage of model solutions with positive eigenvalues for each Schur mode type, based on Schur Decomposition analysis of the connectivity matrix. The solution networks (n = 36 solutions, red, mean ± 95% CI) are compared against 10,000 random networks (black) with the same symmetry and parameter value constraints. Dashed line indicates results from 10,000 random networks with the same symmetry, but not the parameter value constraints.

Extended Data Fig. 10 Distributions of parameters across all model solutions.

The distribution of parameter values across all solutions (n = 373) is plotted for each of the 16 free parameters (Methods). Vertical dashed line marks zero for reference. Red arrow marks average parameter value across solutions. The weight parameters determined the connectivity matrix between units. The noise parameter was the variance of white noise added to each unit on each time step. The Pro and Anti rule input weights determined the strength of the task context inputs to either the Pro or Anti units. The stimulus input determined the weight of the stimulus to either the Left or Right units. The Pro bias term was a constant input to only the Pro units. The target period input was a constant input to all nodes, only during the target period. The constant input was a bias term during all time points for all units. The opto strength was the fraction of each node’s output that was transmitted to the other nodes during inactivations; a strength of 1 is no inactivation, a strength of 0 is complete inactivation.

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Duan, C.A., Pagan, M., Piet, A.T. et al. Collicular circuits for flexible sensorimotor routing. Nat Neurosci (2021). https://doi.org/10.1038/s41593-021-00865-x

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