Input arrival-time-dependent decoding scheme for a spiking neural network

Spiking neurons model a type of biological neural system where information is encoded with spike times. In this paper, a new method for decoding input spikes according to their absolute arrival times is proposed. The output times, which are responses to different input patterns, can differentiate these input patterns uniquely. Features of Spiking Neural Networks (SNN) such as actual spike input time and synaptic weights are utilized. Only a limited number of neurons are needed to implement the decoding scheme.

• Type: Conference
• Year: 2004
• Where: ESANN
• Authors: Hesham H. Amin, Robert H. Fujii
• Keywords: Actual Spike Input, ESANN 2004, ESANN 2007, Input, Input Patterns