Title: A Novel Circuit Model of Contextual Modulation and Normalization in Primary Visual Cortex
Description: The response of a neuron encoding information about a sensory stimulus is influenced by the context in which that information is presented. In the primary visual cortex (area V1), neurons respond selectively to stimuli presented to a relatively constrained region of visual space known as the classical receptive field (CRF). These responses are influenced by stimuli in a much larger region of visual space known as the extra-classical receptive field (eCRF). In that they cannot directly evoke a response from the neuron, surround stimuli in the eCRF provide the context for the input to the CRF. Though the past few decades of research have revealed many details of the complex and nuanced interactions between the CRF and eCRF, the circuit mechanisms underlying these interactions are still unknown. In this thesis, we present a simple, novel cortical circuit model that can account for a surprisingly diverse array of eCRF properties. This model relies on extensive recurrent interactions between excitatory and inhibitory neurons, connectivity that is strongest between neurons with similar stimu- lus preferences, and an expansive input-output neuronal nonlinearity.

There is substantial evidence for all of these features in V1. Through analytical and computational modeling techniques, we demonstrate how and why this circuit is able to account for such a comprehensive array of contextual modulations. In a linear network model, we demonstrate how surround suppression of both excitatory and inhibitory neurons is achieved through the selective amplification of spatially-periodic pat- terns of activity. This amplification relies on the network operating as an inhibition-stabilized network, a dynamic regime previously shown to account for the paradoxical decrease in in- hibition during surround suppression (Ozeki et al., 2009). With the addition of nonlinearity, effective connectivity strength scales with firing rate, and the network can transition be- tween different dynamic regimes as a function of input strength. By moving into and out of the inhibition-stabilized state, the model can reproduce a number of contrast-dependent changes in the eCRF without requiring any asymmetry in the intrinsic contrast-response properties of the cells.

This same model also provides a biologically plausible mechanism for cortical normalization, an operation that has been shown to be ubiquitous in V1. Through a winner-take-all population response, we demonstrate how this network undergoes a strong reduction in trial-to-trial variability at stimulus onset. We also propose a novel mechanism for attentional modulation in visual cortex. We then go on to test several of the critical pre- dictions of the model using single unit electrophysiology. From these experiments, we find ample evidence for the spatially-periodic patterns of activity predicted by the model. Lastly, we show how this same circuit motif may underlie behavior in a higher cortical region, the lateral intraparietal area.