Evaluating Encoding and Decoding Approaches for Spiking Neuromorphic Systems

Abstract

A challenge associated with effectively using spiking neuromorphic systems is how to communicate data to and from the neuromorphic implementation. Unless a neuromorphic or event-based sensing system is used, data has to be converted into spikes to be processed as input by the neuromorphic system. The output spikes produced by the neuromorphic system have to be turned back into a value or decision. There are a variety of commonly used input encoding approaches, such as rate coding, temporal coding, and population coding, as well as several commonly used output approaches, such as voting or first-to-spike. However, it is not clear which is the most appropriate approach to use or whether the choice of encoding or decoding approach has a significant impact on performance. In this work, we evaluate the performance of several encoding and decoding approaches on classification, regression, and control tasks. We show that the choice of encoding and decoding approaches significantly impact performance on these tasks, and we make recommendations on how to select the appropriate encoding and decoding approaches for real-world applications.