Transistor channel dendrites implementing HMM classifiers

Paul Hasler, Scott Kozoil, Ethan Farquhar, Arindam Basu

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

24 Citations (Scopus)

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. © 2007 IEEE.
Original languageEnglish
Article number4253399
Pages (from-to)3359-3362
JournalProceedings - IEEE International Symposium on Circuits and Systems
DOIs
Publication statusPublished - 2007
Externally publishedYes
Event2007 IEEE International Symposium on Circuits and Systems, ISCAS 2007 - New Orleans, LA, United States
Duration: 27 May 200730 May 2007

Bibliographical note

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