Neuronal Classifier for both Rate and Timing-Based Spike Patterns

摘要

Spikes play an essential role in information transmission and neural computation, but how neurons learn them remains unclear. Most learning rules depend on either the rate- or timing-based code, but rare one is suitable for both. In this paper, we present an efficient multi-spike learning rule which is suitable to train neurons to classify both rate- and timing-based spike patterns. With our learning rule, neurons can be trained to fire different numbers of output spikes in response to their input patterns, and therefore single neurons axe capable for multi-category classification.