Transistor Channel Dendrites implementing HMM classifiers

Paul E. Hasler, Scott Koziol, Ethan Farquhar, Arindam Basu

Abstract: Recently we have presented transistor channel models of biological channels and the resulting implementation towards building spiking nodes, synapses, and dendrites. We have also discussed how to build reconfigurable dendrites using programmable analog techniques. With all of this technology components available, we begin to address the question of the computation model possible using a dendrite element, as well as a network of dendrite elements. We will 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. We present simulation and experimental results for the branch elements; we also present initial results for a small dendrite based classifier structure to show the similarities to the HMM paradigm.