Sampling Spiking Neural Network electronic nose on a tiny-chip

Chemicals classification using a new Sampling Spiking Neural Network (SSNN) approach is presented in this paper with experimental measurements using the Cyranose 320 sensor array. The network is unique in its minimal yet powerful design which implements on chip learning and parallel monitoring to detect binary odor patterns with high noise environment. The SSNN architecture is further implemented on a 0.5 um CMOS technology tiny-chip designed to work in conjunction with a 256 K external SRAM memory. It handles the routing of spike signal among 32,000 synapses and 255 neurons. At the same time, it tracks and records learning statistics. The chip can be used in parallel with other SSNN co processors for very large systems. Experimental measurements of our SSNN E-Nose classifier, compared to other E-nose systems proved superior in capability, size, and correctness.