Transistor channel dendrites implementing HMM classifiers

Abstract

Recently the authors presented transistor channel models of biological channels and the resulting implementation towards building spiking nodes, synapses, and dendrites. The authors also discussed how to build reconfigurable dendrites using programmable analog techniques. With all of this technology components available, the authors begin to address the question of the computation model possible using a dendrite element, as well as a network of dendrite elements. The authors discuss the connection between a dendrite element and a hidden Markov model (HMM) classifier branch, as well as a network of dendrites and somas to create an HMM classifier typical of what is used in speech recognition systems. The authors present simulation and experimental results for the branch elements; the authors also present initial results for a small dendrite based classifier structure to show the similarities to the HMM paradigm.