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Proceedings Paper

Large-scale photonic neural networks with biology-like processing elements: the role of electron-trapping materials
Author(s): Nabil H. Farhat; Zhimin Wen
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Paper Abstract

Neural networks employing pulsating biology-oriented integrate-and-fire (IF) model neurons, that can exhibit synchronicity (phase-locking), bifurcation, and chaos, have features that make them potentially useful for learning and recognition of spatio-temporal patterns, generation of complex motor control, emulating higher-level cortical functions like feature binding, separation of object from background, cognition and other higher-level functions; all of which are beyond the ready reach of nonpulsating sigmoidal neuron networks. The spiking nature of biology-oriented neural networks makes their study in digital hardware impractical. Prange and Klar convincingly argued that the best way of realizing such networks is through analog CMOS technology rather than digital hardware. They showed, however, that the number of neurons one can accommodate on a VLSI chip limited to a hundred or so, even when submicron CMOS technology is used, because of the relatively large size of the neuron/dendrite cell. One way of reducing the size of neuron/dendrite cell is to reduce the structural complexity of the cell by realizing some of the processes needed in the cell's operation externally to the chip and by coupling these processes to the cell optically. Two such processes are the relaxation mechanism of the IF neuron and dendritic-tree processing. We have shown, by examining the blue light impulse response of electron trapping materials (ETMs) used under simultaneous infrared and blue light bias, that these materials offer features that can be used in realizing both the optical relaxation and synapto-dendritic response mechanisms. Experimental results demonstrating the potential of this approach in realizing dense arrays of biology-oriented neuron/dendrite cells will be presented, focusing on the concept and design of ETM-based image intensifier as new enabling technology.

Paper Details

Date Published: 28 August 1995
PDF: 10 pages
Proc. SPIE 2565, Optical Implementation of Information Processing, (28 August 1995); doi: 10.1117/12.217643
Show Author Affiliations
Nabil H. Farhat, Univ. of Pennsylvania (United States)
Zhimin Wen, Hewlett-Packard Co. (United States)


Published in SPIE Proceedings Vol. 2565:
Optical Implementation of Information Processing
Bahram Javidi; Joseph L. Horner, Editor(s)

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