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.
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The author thanks Dario Ringach and an anonymous reviewer for insightful suggestions and comments. This work was supported by an NSF Mathematical Sciences Postdoctoral Research Fellowship.
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