Download "Synaptic computation underlying probabilistic inference"

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.