A biologically realizable bayesian computation in a cortical neural network

Abstract

Bayesian estimation theory has been expected to explain how brain deals with uncertainty such as feature extraction against noisy observations. It has been implied that the neural networks that model cortical network could implement the Bayesian estimation algorithm by several previous studies. However, it is still unclear whether it is possible to implement the required computational procedures of the algorithm under physiological and anatomical constraints of the neural systems. We here propose the neural network that implements the algorithm in a biologically realizable manner, incorporating the discrete choice theory into the previously proposed model. Our model successfully demonstrated an orientation discrimination task with significantly noisy visual images. © 2012 Springer-Verlag.