title: Measuring linear and quadratic contributions to neuronal response.
abstract: 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.
authors.affiliation: Department of Mathematics, University of California, Los Angeles, CA 90095-1555, USA. nykamp@math.umn.edu
authors.forename: Duane Q
authors.initials: DQ
authors.lastname: Nykamp
mesh_terms: D000200:Action Potentials / Q000502:physiology; D000818:Animals; D003198:Computer Simulation; D016014:Linear Models; D008252:Macaca fascicularis; D008959:Models, Neurological; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*; D010775:Photic Stimulation; D011897:Random Allocation; D013997:Time Factors