Spike-triggered characterization of excitatory and suppressive stimulus dimensions in monkey V1

Abstract

Neurons in primary visual cortex are commonly characterized using linear models, or simple extensions of linear models. Speciÿcally, V1 simple cell responses are often characterized using a rectiÿed linear receptive ÿeld, and complex cell responses are often described as the sum of squared responses of two linear subunits. We examined this class of model directly by applying spike-triggered covariance analysis to responses of monkey V1 neurons under binary white noise stimulation. The analysis extracts a low-dimensional subspace of the full stimulus space that is primarily responsible for generation of the neural response, including both excitatory and suppressive components. We found no fewer than two excitatory dimensions in simple cells, and as many as seven dimensions in complex cells. For all cells, we also found suppressive dimensions that were at least equal in number to the excitatory dimensions. These results suggest that extensions to standard models are required to fully describe the response properties of cells in V1.

Receptive Field Properties of Neurons in the Early Visual Cortex Revealed by Local Spectral Reverse Correlation

“…Recently, several groups have shown that neurons in the primary visual cortex can be described as a set of spatiotemporal linear filters, and these underlying filters can be estimated by conducting a spike-triggered covariance (STC) technique (Touryan et al, 2002;Rust et al, 2004Rust et al, , 2005. The STC and LSRC analyses seem to share several desirable features, especially in that both of these techniques attempt to reveal filtering profiles underlying the responses of neurons and that both of them use white-noise sequences.…”

Blue-Yellow Signals Are Enhanced by Spatiotemporal Luminance Contrast in Macaque V1

“…We define the "nonlinear RF" as the first principal component of the spike-triggered stimuli. This nonlinear RF has been used to describe the nonlinear properties of complex cells (see Rust et al 2004;Touryan et al 2002) (see also METHODS). Panels on the right of Fig.…”

“…Recently developed data-analysis tools have provided new ways to characterize neurons that combine inputs nonlinearly (de Ruyter van Steveninck and Bialek 1988;Paninski 2003;Rust et al 2004; Schwartz et al 2001;Sharpee et al 2004;Simoncelli et al 2004;Touryan et al 2002). Here we use one of these techniques to reveal a surprising nonlinear computation performed by blue-yellow neurons in V1.…”

“…While the linearity assumption is justified for many V1 neurons, it is clearly inappropriate for others (Conway 2001;Hanazawa et al 2000;Hubel and Wiesel 1968;Lennie et al 1990;Vautin and Dow 1985). For example, a V1 neuron that responds to S cone stimulation, but only when L-and M-cone excitations are appropriately balanced, does not integrate cone inputs linearly and thus cannot be characterized with coneisolating stimuli (Hanazawa et al 2000).Recently developed data-analysis tools have provided new ways to characterize neurons that combine inputs nonlinearly (de Ruyter van Steveninck and Bialek 1988;Paninski 2003;Rust et al 2004; Schwartz et al 2001;Sharpee et al 2004;Simoncelli et al 2004;Touryan et al 2002). Here we use one of these techniques to reveal a surprising nonlinear computation performed by blue-yellow neurons in V1.…”

Analysis and interpretation of quadratic models of receptive fields

“…Quadratic forms are used in experimental studies as quadratic approximations to the input-output function of neurons and can be derived from neural data as Volterra/Wiener approximations up to the second order [3][4][5][6][7][8][9][10][11][12][13] . In addition, several theoretical studies have defined quadratic models of neuronal RFs either explicitly 2,14,15 or implicitly as neural networks [16][17][18] .…”