Refractory neural nets and vision

Thomas C. Fall

doi:10.1117/12.2040212

Abstract

Biological understandings have served as the basis for new computational approaches. A prime example is artificial neural nets which are based on the biological understanding of the trainability of neural synapses. In this paper, we will investigate features of the biological vision system to see if they can also be exploited. These features are 1) the neuron’s refractory period - the period of time after the neuron fires before it can fire again and 2) the ocular microtremor which moves the retinal neural array relative to the image. The short term memory due to the refractory period allows the before and after movement views to be compared. This paper will discuss the investigation of the implications of these two features.

Citation Download Citation

Thomas C. Fall, Thomas C. Fall, "Refractory neural nets and vision", Proc. SPIE 9019, Image Processing: Algorithms and Systems XII, 90190H (25 February 2014); doi: 10.1117/12.2040212; https://doi.org/10.1117/12.2040212

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