Next-generation advances in cognitive processing using spiking neural networks for biochemical sensing, radar and rapid HDL

Abstract

This invited plenary paper introduces a novel spiking neural network methodology, and applies it to an odorant learning, medical and radar detection applications. Rapid HDL is introduced as a 15 minute rapid prototyping approach, where real-time implementations will be demoed on FPGAs. The spike-time dependent plasticity can support coding schemes that are based on spatio-temporal spike patterns. Spiking (or pulsed) neural networks (SNNs) are models which explicitly take into account the timing of inputs. The network input and output are usually represented as series of spikes (delta function or more complex shapes). Plasticity SNNs have an advantage of being able to recurrently process information. Spike-time dependent plasticity can enhance signal transmission by selectively strengthening synaptic connections that transmit precisely timed spikes at the expense of those synapses that transmit poorly timed spikes.