Analog hardware implementation of neocognitron networks

Rafael M. Inigo; Allen Bonde Jr.; Bradford Holcombe

doi:10.1117/12.21176

1 August 1990 Analog hardware implementation of neocognitron networks

Rafael M. Inigo, Allen Bonde Jr., Bradford Holcombe

Proceedings Volume 1294, Applications of Artificial Neural Networks; (1990) https://doi.org/10.1117/12.21176
Event: 1990 Technical Symposium on Optics, Electro-Optics, and Sensors, 1990, Orlando, FL, United States

Abstract

This paper deals with the analog implementation of neocognitron based neural networks. All of Fukushima''s and related work on the neocognitron is based on digital computer simulations. To fully take advantage of the power of this network paradigm an analog electronic approach is proposed. We first implemented a 6-by-6 sensor network with discrete analog components and fixed weights. The network was given weight values to recognize the characters U L and F. These characters are recognized regardless of their location on the sensor and with various levels of distortion and noise. The network performance has also shown an excellent correlation with software simulation results. Next we implemented a variable weight network which can be trained to recognize simple patterns by means of self-organization. The adaptable weights were implemented with PETs configured as voltage-controlled resistors. To implement a variable weight there must be some type of " memory" to store the weight value and hold it while the value is reinforced or incremented. Two methods were evaluated: an analog sample-hold circuit and a digital storage scheme using binary counters. The latter is preferable for VLSI implementation because it uses standard components and does not require the use of capacitors. The analog design and implementation of these small-scale networks demonstrates the feasibility of implementing more complicated ANNs in electronic hardware. The circuits developed can also be designed for VLSI implementation. 1.

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Rafael M. Inigo, Allen Bonde Jr., and Bradford Holcombe "Analog hardware implementation of neocognitron networks", Proc. SPIE 1294, Applications of Artificial Neural Networks, (1 August 1990); https://doi.org/10.1117/12.21176

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KEYWORDS

Analog electronics

Artificial neural networks

Image segmentation

Binary data

Very large scale integration

Computer simulations

Field effect transistors

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