Measuring linear and quadratic contributions to neuronal response

We present a method to dissociate the sign-dependent (linear or odd-order) response from the sign-independent (quadratic or even-order) response of a neuron to sequences of random orthonormal stimulus elements. The method is based on a modification of the classical linear–nonlinear model of neural response. The analysis produces estimates of the stimulus features to which the neuron responds in a sign-dependent manner, the stimulus features to which the neuron responds in a sign-independent manner and the relative weight of the sign-independent response. We propose that this method could be used to characterize simple and complex cells in the primary visual cortex.

[1]  Bartlett W. Mel,et al.  A model for intradendritic computation of binocular disparity , 2000, Nature Neuroscience.

[2]  D. Snodderly,et al.  Spatial organization of receptive fields of V1 neurons of alert monkeys: comparison with responses to gratings. , 2002, Journal of neurophysiology.

[3]  R. Shapley,et al.  The use of m-sequences in the analysis of visual neurons: Linear receptive field properties , 1997, Visual Neuroscience.

[4]  I. Ohzawa,et al.  Spatiotemporal organization of simple-cell receptive fields in the cat's striate cortex. I. General characteristics and postnatal development. , 1993, Journal of neurophysiology.

[5]  J. P. Jones,et al.  An evaluation of the two-dimensional Gabor filter model of simple receptive fields in cat striate cortex. , 1987, Journal of neurophysiology.

[6]  A. B. Bonds,et al.  Classifying simple and complex cells on the basis of response modulation , 1991, Vision Research.

[7]  P. Schiller,et al.  Quantitative studies of single-cell properties in monkey striate cortex. I. Spatiotemporal organization of receptive fields. , 1976, Journal of neurophysiology.

[8]  Yoshimichi Yoshimichi,et al.  For Further Development of Flow Visualization: Preface , 1999 .

[9]  Guillermo Sapiro,et al.  A subspace reverse-correlation technique for the study of visual neurons , 1997, Vision Research.

[10]  Duane Q Nykamp,et al.  Full identification of a linear-nonlinear system via cross-correlation analysis. , 2002, Journal of vision.

[11]  L. Maffei,et al.  The visual cortex as a spatial frequency analyser. , 1973, Vision research.

[12]  Robert Shapley,et al.  Receptive field structure of neurons in monkey primary visual cortex revealed by stimulation with natural image sequences. , 2002, Journal of vision.

[13]  D. G. Albrecht,et al.  Spatial frequency selectivity of cells in macaque visual cortex , 1982, Vision Research.

[14]  D. Ringach,et al.  On the classification of simple and complex cells , 2002, Vision Research.

[15]  BsnNr C. Srorn,et al.  CLASSIFYING SIMPLE AND COMPLEX CELLS ON THE BASIS OF RESPONSE MODULATION , 2002 .

[16]  J. Movshon,et al.  Spatial summation in the receptive fields of simple cells in the cat's striate cortex. , 1978, The Journal of physiology.

[17]  E J Chichilnisky,et al.  A simple white noise analysis of neuronal light responses , 2001, Network.

[18]  Michael J. Korenberg,et al.  Identification of Intensive Nonlinearities in Cascade Models of Visual Cortex and its Relation to Cell Classification , 1989 .

[19]  I. Ohzawa,et al.  Spatiotemporal organization of simple-cell receptive fields in the cat's striate cortex. II. Linearity of temporal and spatial summation. , 1993, Journal of neurophysiology.

[20]  RussLL L. Ds Vnlos,et al.  SPATIAL FREQUENCY SELECTIVITY OF CELLS IN MACAQUE VISUAL CORTEX , 2022 .

[21]  R. Shapley,et al.  Suppression of neural responses to nonoptimal stimuli correlates with tuning selectivity in macaque V1. , 2002, Journal of neurophysiology.

[22]  D. Hubel,et al.  Receptive fields, binocular interaction and functional architecture in the cat's visual cortex , 1962, The Journal of physiology.

[23]  I. Ohzawa,et al.  Neural mechanisms for processing binocular information II. Complex cells. , 1999, Journal of neurophysiology.