Inference, attention, and decision in a bayesian neural architecture
Journal
Advances in Neural Information Processing Systems
Date Issued
2005
Author(s)
Dayan P.
Abstract
We study the synthesis of neural coding, selective attention and perceptual decision making. A hierarchical neural architecture is proposed, which implements Bayesian integration of noisy sensory input and topdown 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 visual cortical neurons. The model also illustrates a possible reconciliation of cortical and neuromodulatory representations of uncertainty.
Other Subjects
Neural networks; Bayesian integration; Intermediate level; Neural architectures; Perceptual decision making; Physiological properties; Prior information; Selective attention; Visual cortical neurons; Network architecture
Type
conference paper