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Synaptic computation underlying probabilistic inference

Abstract

We propose that synapses may be the workhorse of the neuronal computations that underlie probabilistic reasoning. We built a neural circuit model for probabilistic inference in which information provided by different sensory cues must be integrated and the predictive powers of individual cues about an outcome are deduced through experience. We found that bounded synapses naturally compute, through reward-dependent plasticity, the posterior probability that a choice alternative is correct given that a cue is presented. Furthermore, a decision circuit endowed with such synapses makes choices on the basis of the summed log posterior odds and performs near-optimal cue combination. The model was validated by reproducing salient observations of, and provides insights into, a monkey experiment using a categorization task. Our model thus suggests a biophysical instantiation of the Bayesian decision rule, while predicting important deviations from it similar to the 'base-rate neglect' observed in human studies when alternatives have unequal prior probabilities.

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Figure 1: Schematic of the model and posterior computation by plastic synapses when a single cue is presented on each trial.
Figure 2: Posterior computation by plastic synapses when multiple cues are presented on each trial.
Figure 3: Choice behavior of the model and the subjective weight of evidence in the weather prediction task.
Figure 4: Model neural population activity during the weather prediction task.
Figure 5: Neural population activity is parametrically correlated with the log LR.
Figure 6: Effect of prior probability on the choice behavior and neural activity.

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Acknowledgements

This work was supported by US National Institutes of Health grants 2-R01-MH062349 and MH073246. We are thankful to D. Andrieux, S. Ardid, A. Bernacchia and R. Wilson for comments on the manuscript.

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Authors and Affiliations

Authors

Contributions

A.S. and X.-J.W. conceived the problem and designed the model. A.S. performed model simulations and analyzed the data. A.S. and X.-J.W. wrote the paper.

Corresponding authors

Correspondence to Alireza Soltani or Xiao-Jing Wang.

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Supplementary Figures 1–11, Supplementary Notes 1–5 and Supplementary Methods (PDF 1204 kb)

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Soltani, A., Wang, XJ. Synaptic computation underlying probabilistic inference. Nat Neurosci 13, 112–119 (2010). https://doi.org/10.1038/nn.2450

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