Evidence for a probabilistic, brain-distributed, recursive mechanism for decision-making

Decision formation recruits many brain regions, but the procedure they jointly execute is unknown. To characterize it, we introduce a recursive Bayesian algorithm that makes decisions based on spike trains. Using it to simulate the random-dot-motion task, based on area MT activity, we demonstrate it quantitatively replicates the choice behaviour of monkeys, whilst predicting losses of usable sensory information. Its architecture maps to the recurrent cortico-basal-ganglia-thalamo-cortical loops, whose components are all implicated in decision-making. We show that the dynamics of its mapped computations match those of neural activity in the sensory-motor cortex and striatum during decisions, and forecast those of basal ganglia output and thalamus. This also predicts which aspects of neural dynamics are and are not part of inference. Our single-equation algorithm is probabilistic, distributed, recursive and parallel. Its success at capturing anatomy, behaviour and electrophysiology suggests that the mechanism implemented by the brain has these same characteristics.

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