A demonstration of using the model reference principle to develop the function-oriented adaptive pulse-coded neural network. (February 2020)

• Record Type: Journal Article
• Authors: Sharma, B Lungsi; Wells, Richard B
• Is Part Of: Simulation. Volume 96:Number 2(2020)
• Page Start: 207
• Page End: 219
• Publication Date: 2020-02
• Subjects: Model reference principle -- adaptive pulse-coded neural network -- scientific reduction -- model-order reduction -- computational neuroscience -- embedding field theory; Computer simulation -- Periodicals; 003.3
• DOI: 10.1177/0037549719860587
• Languages: English
• ISSNs: 0037-5497

Abstract

How can one design an adaptive pulsed neural network that is based on psycho-phenomenological foundations? In other words, how can one migrate the adaptive capability of a psychologically modeled neural network to a pulsed network? Neural networks that model psychological phenomena are at a larger scale than physiological models. There is a common presumption that pulse-coded neural network analogs to non-pulsing networks can be obtained by a simple mapping and scaling process of some sort. But the actual in vivo environment of pulse-coded neural network systems produces a much more diverse set of firing patterns. Thus, functional mapping from traditional neural network systems to pulse-coded neural network systems is much more challenging than has been presumed. This paper demonstrates that the employment of model reference adaptation as a method for applying scientific reduction is a powerful design tool for the development of a function-oriented adaptive pulse-coded neural network. The performance surface is empirically obtained by comparing the performance of the pulsed network to the non-pulsing network. Based on this surface, the adaptive algorithm is a combination of gain scheduling and steepest-descent method. Therefore, the adaptive property of the pulse-coded neural network is built upon a psycho-physiological foundation.