Invited Session III: Neural network models of the visual system: Robust information representation in hierarchical networks of the visual cortex.

Abstract

The mammalian visual system has an exquisite ability to encode relevant information from noisy sensory inputs in natural scenes. Such robust yet adaptable computation is believed to emerge from the uniquely complex connectivity structure of biological neural networks. In this talk, I will focus on the hierarchical organization of the cortical network, and discuss how this unique network structure shapes robust representations of noisy visual inputs via predictive coding. Using a predictive coding model of hierarchically-related visual cortical areas, we link feedback connections to top-down predictions of the lower cortical neural activity. Our model shows that hierarchical predictive coding explains the mechanisms of robust recognition of noisy objects in intermediate cortical areas. Secondly, we study how context-specific information may be represented and modified across learning in populations of neurons in a recurrent neural network model of hierarchically-related cortical areas. Motivated by an experimental study investigating neural activities in response to expected and unexpected natural images in mouse visual cortex, we test how the divergence of feedforward and feedback connections underlie differential representations of expected and surprising sensory information across cortical areas and layers. In sum, our study provides insights into how robust and adaptable visual encoding arise from biologically-motivated connectivity across the cortical hierarchy.