Conjectures regarding the nonlinear geometry of visual neurons.

From the earliest stages of sensory processing, neurons show inherent non-linearities: the response to a complex stimulus is not a sum of the responses to a set of constituent basis stimuli. These non-linearities come in a number of forms and have been explained in terms of a number of functional goals. The family of spatial non-linearities have included interactions that occur both within and outside of the classical receptive field.

The berkeley wavelet transform: a biologically inspired orthogonal wavelet transform.

We describe the Berkeley wavelet transform (BWT), a two-dimensional triadic wavelet transform. The BWT comprises four pairs of mother wavelets at four orientations. Within each pair, one wavelet has odd symmetry, and the other has even symmetry.

Complete functional characterization of sensory neurons by system identification.

System identification is a growing approach to sensory neurophysiology that facilitates the development of quantitative functional models of sensory processing. This approach provides a clear set of guidelines for combining experimental data with other knowledge about sensory function to obtain a description that optimally predicts the way that neurons process sensory information. This prediction paradigm provides an objective method for evaluating and comparing computational models.

Nonlinear V1 responses to natural scenes revealed by neural network analysis.

A key goal in the study of visual processing is to obtain a comprehensive description of the relationship between visual stimuli and neuronal responses. One way to guide the search for models is to use a general nonparametric regression algorithm, such as a neural network. We have developed a multilayer feed-forward network algorithm that can be used to characterize nonlinear stimulus-response mapping functions of neurons in primary visual cortex (area V1) using natural image stimuli.