Modeling lateral geniculate nucleus response with contrast gain control. Part 1: Formulation - Cope, Davis (2013)
A class of models for lateral geniculate nucleus (LGN) ON-cell behavior is proposed. The models consist of a linear filter with divisive normalization by root mean square local contrast and include an intrinsic noise density parameter. The properties of these models are shown to match observed LGN behavior: (1) a linear response to low magnitude stimuli; (2) a linear response without saturation (luxotonic behavior) for zero contrast stimuli (homogeneous fields) with increasing magnitude; (3) response saturation for non-zero contrast stimuli with increasing magnitude. The models possess an intrinsic scale for signal-to-noise ratio (SNR). The models show under- and super-saturation, as well as saturation, for sinusoidal grating stimuli with increasing contrast and predict that different SNR regimes will cause a single neuron to show different contrast response curves. A companion paper (Cope, Blakeslee and McCourt, 2013) provides a detailed analysis of the full nonlinear response for sinusoidal grating stimuli and circular spot stimuli.