Annau, Thomas Mark (1996) Models of visual feature detection and spike coding in the nervous system. Dissertation (Ph.D.), California Institute of Technology. http://resolver.caltech.edu/CaltechETD:etd-09212006-152641
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.
|Item Type:||Thesis (Dissertation (Ph.D.))|
|Degree Grantor:||California Institute of Technology|
|Major Option:||Computation and Neural Systems|
|Thesis Availability:||Public (worldwide access)|
|Defense Date:||20 May 1996|
|Non-Caltech Author Email:||tom (AT) tomannau.com|
|Default Usage Policy:||No commercial reproduction, distribution, display or performance rights in this work are provided.|
|Deposited By:||Imported from ETD-db|
|Deposited On:||22 Sep 2006|
|Last Modified:||26 Dec 2012 03:02|
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