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

Citation

Annau, Thomas Mark (1996) Models of visual feature detection and spike coding in the nervous system. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/CN6R-WE94. https://resolver.caltech.edu/CaltechETD:etd-09212006-152641

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.

Thesis Files