Spiking networks for biochemical detection

Artificial neural networks have proven to be a useful tool for olfactory pattern recognition; but most silicon-based implementations have been limited in scale due to inherent constraints on chip real estate and synapse routing. This paper presents a new spiking neural network approach to odorant learning and detection based on new learned information about the mammalian olfaction system. The network is designed to accept more than 1000 inputs and detect odors via an unlimited number of outputs. The basic theory is presented, including stochastic learning, and detection based on hamming distance. Simulation shows the network functions well, even in noisy environments where more than 10% of inputs are contaminated by background noise. Digital hardware implementation using VHDL shows that, a representative system with 128 inputs and 8 outputs fits on a single Xilinx Virtex v1000 chip and would occupy just 0.118 mm/sup 2/ using 0.16um CMOS technology.