Models of visual feature detection and spike coding in the nervous system

Author: Annau, Thomas Mark

Year: 1996

Degree: Dissertation (Ph.D.)

Advisor: Hopfield, John J.

Committee Members: Hopfield, John J.; Mead, Carver; Laurent, Gilles J.; Abu-Mostafa, Yaser S.

Option: Computation and Neural Systems

DOI: 10.7907/CN6R-WE94

Abstract

We propose mathematical models to analyze two nervous system phenomena. The first is a model of the development and function of simple cell receptive fields in mammalian primary visual cortex. The model assumes that images are composed of combinations of a limited set of specific visual features and that the goal of simple cells is to detect the presence or absence of these features. Based on a presumed statistical character of images and their visual features, the model uses a constrained Hebbian learning rule to discover the structure of the features, and thus the appropriate response properties of simple cells, by training on a database of photographs. The response properties of the model simple cells agree qualitatively with neurophysiological observation.

The second is a model of the coding of information in the nervous system by the rate of axonal voltage spikes. Assuming an integrate-and-fire mechanism for spike generation, we develop a quantization-based model of rate coding and use it to derive the mathematical relationship between the amplitude and temporal resolution of a rate encoded signal. We elaborate the model to include integrator leak in the spike generation mechanism and show that it compactly combines coding and the computation of a threshold function.

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