Parallelism and Redundancy in Neural Networks

Abstract

Recent interest in neural networks is largely triggered by the idea that knowledge from this field may be advantageously applied to problems arising in massively parallel computing. Besides increasing processing velocity, parallelism provides possibilities for improved fault tolerance and graceful degradation. In technical devices, this has been achieved by back-up hardware added in parallel to the main system. Biological systems take advantage of parallelism in many other respects including natural implementations, minimization of the number of computation steps, exploitation of signal redundancy, and a balanced distribution of processing tasks between all subsystems. As a result, reliability and accuracy of computation become exchangable. We present examples for these principles of biological information processing and discuss how parallelism is used for their implementation.