A learning-enabled neuron array IC based upon transistor channel models of biological phenomena

Stephen Brink; Stephen Nease; Paul Hasler; Shubha Ramakrishnan; Richard Wunderlich; Arindam Basu; Brian Degnan

doi:10.1109/TBCAS.2012.2197858

A learning-enabled neuron array IC based upon transistor channel models of biological phenomena

Stephen Brink
, Stephen Nease
, Paul Hasler
, Shubha Ramakrishnan
, Richard Wunderlich
, Arindam Basu
, Brian Degnan

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

100 Citations (Scopus)

Abstract

We present a single-chip array of 100 biologically-based electronic neuron models interconnected to each other and the outside environment through 30,000 synapses. The chip was fabricated in a standard 350 nm CMOS IC process. Our approach used dense circuit models of synaptic behavior, including biological computation and learning, as well as transistor channel models. We use Address-Event Representation (AER) spike communication for inputs and outputs to this IC. We present the IC architecture and infrastructure, including IC chip, configuration tools, and testing platform. We present measurement of small network of neurons, measurement of STDP neuron dynamics, and measurement from a compiled spiking neuron WTA topology, all compiled into this IC. © 2012 IEEE.

Original language	English
Article number	6218734
Pages (from-to)	71-81
Journal	IEEE Transactions on Biomedical Circuits and Systems
Volume	7
Issue number	1
DOIs	https://doi.org/10.1109/TBCAS.2012.2197858
Publication status	Published - 2013
Externally published	Yes

Bibliographical note

Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

Research Keywords

Electrical implementation of neurobiology
neuromorphic engineering

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abstract = "We present a single-chip array of 100 biologically-based electronic neuron models interconnected to each other and the outside environment through 30,000 synapses. The chip was fabricated in a standard 350 nm CMOS IC process. Our approach used dense circuit models of synaptic behavior, including biological computation and learning, as well as transistor channel models. We use Address-Event Representation (AER) spike communication for inputs and outputs to this IC. We present the IC architecture and infrastructure, including IC chip, configuration tools, and testing platform. We present measurement of small network of neurons, measurement of STDP neuron dynamics, and measurement from a compiled spiking neuron WTA topology, all compiled into this IC. {\textcopyright} 2012 IEEE.",

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AU - Nease, Stephen

AU - Hasler, Paul

AU - Ramakrishnan, Shubha

AU - Wunderlich, Richard

AU - Basu, Arindam

AU - Degnan, Brian

N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

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AB - We present a single-chip array of 100 biologically-based electronic neuron models interconnected to each other and the outside environment through 30,000 synapses. The chip was fabricated in a standard 350 nm CMOS IC process. Our approach used dense circuit models of synaptic behavior, including biological computation and learning, as well as transistor channel models. We use Address-Event Representation (AER) spike communication for inputs and outputs to this IC. We present the IC architecture and infrastructure, including IC chip, configuration tools, and testing platform. We present measurement of small network of neurons, measurement of STDP neuron dynamics, and measurement from a compiled spiking neuron WTA topology, all compiled into this IC. © 2012 IEEE.

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KW - neuromorphic engineering

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ER -

A learning-enabled neuron array IC based upon transistor channel models of biological phenomena

Abstract

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Research Keywords

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