Neuromorphic Spike Timing Dependent Plasticity with adaptive OZ Spiking Neurons

Avi Hazan; Elishai Ezra Tsur

doi:10.1109/BioCAS49922.2021.9644944

Neuromorphic Spike Timing Dependent Plasticity with adaptive OZ Spiking Neurons

Avi Hazan, Elishai Ezra Tsur

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Spike Timing Dependent Plasticity (STDP) is a biologically plausible learning rule routinely used for real-time learning in brain-inspired (neuromorphic) systems. In this work, we utilized an analog design of a Neural Engineering Framework (NEF)-tailored spiking neuron, termed OZ, for STDP-driven learning. We propose analog circuit designs of STDP synapse and frequency adaptation and used them to demonstrate longterm potentiation and depression with adapted OZ neurons. Our design provides NEF-compiled energy-efficient STDP with analog circuitry.

Original language	English
Title of host publication	BioCAS 2021 - IEEE Biomedical Circuits and Systems Conference, Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728172040
DOIs	https://doi.org/10.1109/BioCAS49922.2021.9644944
State	Published - 2021
Event	2021 IEEE Biomedical Circuits and Systems Conference, BioCAS 2021 - Virtual, Online, Germany Duration: 6 Oct 2021 → 9 Oct 2021

Publication series

Name	BioCAS 2021 - IEEE Biomedical Circuits and Systems Conference, Proceedings

Conference

Conference	2021 IEEE Biomedical Circuits and Systems Conference, BioCAS 2021
Country/Territory	Germany
City	Virtual, Online
Period	6/10/21 → 9/10/21

Bibliographical note

Publisher Copyright:
© 2021 IEEE.

Keywords

Hebbian learning
long term depression
long-term potentiation
neural engineering framework
neuromorphic engineering
online learning

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10.1109/BioCAS49922.2021.9644944

Cite this

Hazan, A., & Tsur, E. E. (2021). Neuromorphic Spike Timing Dependent Plasticity with adaptive OZ Spiking Neurons. In BioCAS 2021 - IEEE Biomedical Circuits and Systems Conference, Proceedings (BioCAS 2021 - IEEE Biomedical Circuits and Systems Conference, Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BioCAS49922.2021.9644944

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title = "Neuromorphic Spike Timing Dependent Plasticity with adaptive OZ Spiking Neurons",

abstract = "Spike Timing Dependent Plasticity (STDP) is a biologically plausible learning rule routinely used for real-time learning in brain-inspired (neuromorphic) systems. In this work, we utilized an analog design of a Neural Engineering Framework (NEF)-tailored spiking neuron, termed OZ, for STDP-driven learning. We propose analog circuit designs of STDP synapse and frequency adaptation and used them to demonstrate longterm potentiation and depression with adapted OZ neurons. Our design provides NEF-compiled energy-efficient STDP with analog circuitry.",

keywords = "Hebbian learning, long term depression, long-term potentiation, neural engineering framework, neuromorphic engineering, online learning",

author = "Avi Hazan and Tsur, \{Elishai Ezra\}",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE Biomedical Circuits and Systems Conference, BioCAS 2021 ; Conference date: 06-10-2021 Through 09-10-2021",

year = "2021",

doi = "10.1109/BioCAS49922.2021.9644944",

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Hazan, A & Tsur, EE 2021, Neuromorphic Spike Timing Dependent Plasticity with adaptive OZ Spiking Neurons. in BioCAS 2021 - IEEE Biomedical Circuits and Systems Conference, Proceedings. BioCAS 2021 - IEEE Biomedical Circuits and Systems Conference, Proceedings, Institute of Electrical and Electronics Engineers Inc., 2021 IEEE Biomedical Circuits and Systems Conference, BioCAS 2021, Virtual, Online, Germany, 6/10/21. https://doi.org/10.1109/BioCAS49922.2021.9644944

Neuromorphic Spike Timing Dependent Plasticity with adaptive OZ Spiking Neurons. / Hazan, Avi; Tsur, Elishai Ezra.
BioCAS 2021 - IEEE Biomedical Circuits and Systems Conference, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2021. (BioCAS 2021 - IEEE Biomedical Circuits and Systems Conference, Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Tsur, Elishai Ezra

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N2 - Spike Timing Dependent Plasticity (STDP) is a biologically plausible learning rule routinely used for real-time learning in brain-inspired (neuromorphic) systems. In this work, we utilized an analog design of a Neural Engineering Framework (NEF)-tailored spiking neuron, termed OZ, for STDP-driven learning. We propose analog circuit designs of STDP synapse and frequency adaptation and used them to demonstrate longterm potentiation and depression with adapted OZ neurons. Our design provides NEF-compiled energy-efficient STDP with analog circuitry.

AB - Spike Timing Dependent Plasticity (STDP) is a biologically plausible learning rule routinely used for real-time learning in brain-inspired (neuromorphic) systems. In this work, we utilized an analog design of a Neural Engineering Framework (NEF)-tailored spiking neuron, termed OZ, for STDP-driven learning. We propose analog circuit designs of STDP synapse and frequency adaptation and used them to demonstrate longterm potentiation and depression with adapted OZ neurons. Our design provides NEF-compiled energy-efficient STDP with analog circuitry.

KW - Hebbian learning

KW - long term depression

KW - long-term potentiation

KW - neural engineering framework

KW - neuromorphic engineering

KW - online learning

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PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2021 IEEE Biomedical Circuits and Systems Conference, BioCAS 2021

Y2 - 6 October 2021 through 9 October 2021

ER -

Neuromorphic Spike Timing Dependent Plasticity with adaptive OZ Spiking Neurons

Abstract

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Keywords

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