Recognition and tracking of impulse patterns with delay adaptation in biology-inspired pulse processing neural net (BPN) hardware

Abstract

The application of an electronic real time emulator for biology-inspired pulse processing neural networks (BPN) to recognition and temporal tracking of discrete impulse patterns via delay adaptation is demonstrated. The electronic emulation includes biologically plausible features, such as asynchronous impulses, membrane potentials and adaptive weights, as well as a mechanism to modify signal delays. The rule for the adaptation of impulse propagation delays is as follows: ‘error neurons” detect temporal differences between single impulses of other neurons and adjust corresponding signal delay parameters. In the application presented BPN adapts its time delays in order to form a finely tuned match with a given sequence of three discrete impulses. After learning, BPN is capable not only of highly selective recognition of the learned impulse pattern but also of tracking a gradually changing impulse pattern. Tracking is achieved by continuously re-adjusting the delay profile. Delay adaptation (rather than weight adaptation) appears to be the more effective mechanism for such applications.