A reservoir network model for sensory-guided probabilistic decision making
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KeywordsChoice Behavior Choice Response Tuning Curve Choice Probability Input Neuron
Our model successfully replicated the gradual choice behavior observed in our experiment. We further analyzed how neural dynamics in the reservoir network determines the choice probability for familiar and novel cues. We found that a familiar stimulus sequentially activates a relatively small portion of reservoir neurons, and reinforcement learning trained output connections from these neurons such that only an adequate output neuron is activated by the neural trajectory evoked in the reservoir. We further revealed that choice responses to novel cues become graded due to trial-by-trial overlaps between the familiar trajectories and novel-cue-evoked trajectories.
Interestingly, our model also exhibited similar variability in choice responses to that observed in individual rats. We found that if input neurons are highly sensitive to external stimuli, that is, if the width of their frequency tuning curves is broad, the model network likely shows gradual choice behavior for novel stimuli. In contrast, the model with more sensitive input neurons (with broader tuning curves) tends to generate near-random choice behavior, displaying a flat dependence of choice probability on novel stimuli. We compared choice behavior between the models and the rats by introducing quantitative measures and found that the behavioral tendency of our model is consistent with that of the rats (Fig. 1B).
These results may suggest that some individual differences in decision making behavior emerge from neural population dynamics rather than differences in higher-level behavioral strategies.
This work was partially supported by KAKEN-HI No. 255413, Grants-in-Aid for Scientific Research (no. 22115013) from MEXT
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