Dynamical computational properties of local cortical networks for visual and motor processing: A Bayesian framework

A major unsolved question concerns the interaction between the coding of information in the cortex and the collective neural operations (such as perceptual grouping, mental rotation) that can be performed on this information. A key propel-ty of the local networks in the cerebral cortex is to combine thalamocortical or feedforward information with horizontal cortico-cortical connections. Among different types of neural networks compatible with the known functional and architectural properties of the cortex, we show that there exist interesting bayesian solutions resulting in an optimal collective decision made by the neuronal population. We suggest that thalamo-cortical and corticocortical synaptic plasticity can be differentially modulated to optimize this collective bayesian decision process. We take two examples of cortical dynamics, one for perceptual grouping in MT, and the other one for mental rotation in M1. We show that a neural implementation of the bayesian principle is both computationally efficient to perform these tasks and consistent with the experimental data on the related neuronal activities. A major implication is that a similar collective decision mechanism should exist in different cortical regions due to the similarity of the cortical functional architecture.