Inference, Attention, and Decision in a Bayesian Neural Architecture

Part of Advances in Neural Information Processing Systems 17 (NIPS 2004)

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Angela J. Yu, Peter Dayan


We study the synthesis of neural coding, selective attention and percep- tual decision making. A hierarchical neural architecture is proposed, which implements Bayesian integration of noisy sensory input and top- down attentional priors, leading to sound perceptual discrimination. The model offers an explicit explanation for the experimentally observed modulation that prior information in one stimulus feature (location) can have on an independent feature (orientation). The network's intermediate levels of representation instantiate known physiological properties of vi- sual cortical neurons. The model also illustrates a possible reconciliation of cortical and neuromodulatory representations of uncertainty.