How Composite Prior and Noise Shape Multisensory Integration
Xiangyu Ma, He Wang, K. Y. Michael Wong, Hong Kong University of Science and Technology,, China; Wen-Hao Zhang, UT Southwestern Medical Center, United States
Posters 2 Poster
Pacific Ballroom H-O
Fri, 26 Aug, 19:30 - 21:30 Pacific Time (UTC -7)
Experimental data on multisensory integration revealed that modules in the brain processing different inputs are reciprocally connected, and can be utilized for multisensory information integration. We consider environments described by composite priors with both correlated and independent components, and reveal that the correlated and composite posterior probabilities can be encoded in the collective neural activities of separate neuron groups, referred to as congruent and integrated groups, with the latter receiving feedforward inputs from the former. Furthermore, in the framework of probabilistic population coding, we find that the accuracy of the Bayesian prediction can be remarkably improved if the noise in the neuronal dynamics is determined by the amplitude of the population vector, which originates from the collective nature of the neuronal responses themselves.