Plastic NNs for biochemical detection

Bio-inspired systems including neural networks (NNs) have proven to be a useful tool for biochemical electronic nose. Silicon embedded olfactory pattern recognition systems however, have been limited in scale due to inherent constraints of synapse routing and chip area. This paper presents a new plastic-based NN approach for a pseudo bloodhound nose for odorant learning and detection. The network uses spike driven plastic synapses, and 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 mm² using 0.16μm CMOS technology.