Power-law input-output transfer functions explain the contrast-response and tuning properties of neurons in visual cortex.

ABSTRACT: We develop a unified model accounting simultaneously for the contrast invariance of the width of the orientation tuning curves (OT) and for the sigmoidal shape of the contrast response function (CRF) of neurons in the primary visual cortex (V1). We determine analytically the conditions for the structure of the afferent LGN and recurrent V1 inputs that lead to these properties for a hypercolumn composed of rate based neurons with a power-law transfer function. We investigate what are the relative contributions of single neuron and network properties in shaping the OT and the CRF. We test these results with numerical simulations of a network of conductance-based model (CBM) neurons and we demonstrate that they are valid and more robust here than in the rate model. The results indicate that b

SUBMITTER: Persi E 

PROVIDER: S-EPMC3044767 | biostudies-literature | 2011 Feb

REPOSITORIES: biostudies-literature