Neurosynaptic Computational Elements for Adaptive Transient Synchrony: Biophysical Accuracy versus Hardware Complexity

Amir Zjajo

doi:10.1109/IWCIA47330.2019.8955105

Neurosynaptic Computational Elements for Adaptive Transient Synchrony: Biophysical Accuracy versus Hardware Complexity

Amir Zjajo^*

^*Corresponding author for this work

Signal Processing Systems

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

Abstract

In this paper, we examine electro-chemically accurate, multi-compartment, neurosynaptic computational elements, and analyze their complexity, accuracy, and flexibility in signal processing of a time-varying task. We evaluate distributed patterns of simultaneously firing neurons in space and time, and we establish a transient synchrony and homeostatic regulation mechanism upon the underlying synaptic connectivity. With synchronic spiking, we form synchronous groups of neuronal subpopulations, which represent content forming a coherent entity. The neurosynaptic computational elements implemented on Xilinx Virtex 7 XC7VX550 FPGA board illustrate feasibility of the methodology.

Original language	English
Title of host publication	2019 IEEE 11th International Workshop on Computational Intelligence and Applications, IWCIA 2019 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	3-8
Number of pages	6
ISBN (Electronic)	9781728124292
DOIs	https://doi.org/10.1109/IWCIA47330.2019.8955105
Publication status	Published - 2019
Event	11th IEEE International Workshop on Computational Intelligence and Applications, IWCIA 2019 - Hiroshima, Japan Duration: 9 Nov 2019 → 10 Nov 2019

Publication series

Name	2019 IEEE 11th International Workshop on Computational Intelligence and Applications, IWCIA 2019 - Proceedings

Conference

Conference	11th IEEE International Workshop on Computational Intelligence and Applications, IWCIA 2019
Country/Territory	Japan
City	Hiroshima
Period	9/11/19 → 10/11/19

Keywords

biophysically-accurate models
FPGA
neuronomorphic computing
neurosynaptic computatioal elements

Access to Document

10.1109/IWCIA47330.2019.8955105

Cite this

Zjajo, A. (2019). Neurosynaptic Computational Elements for Adaptive Transient Synchrony: Biophysical Accuracy versus Hardware Complexity. In 2019 IEEE 11th International Workshop on Computational Intelligence and Applications, IWCIA 2019 - Proceedings (pp. 3-8). Article 8955105 (2019 IEEE 11th International Workshop on Computational Intelligence and Applications, IWCIA 2019 - Proceedings). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/IWCIA47330.2019.8955105

Zjajo, Amir. / Neurosynaptic Computational Elements for Adaptive Transient Synchrony : Biophysical Accuracy versus Hardware Complexity. 2019 IEEE 11th International Workshop on Computational Intelligence and Applications, IWCIA 2019 - Proceedings. Institute of Electrical and Electronics Engineers (IEEE), 2019. pp. 3-8 (2019 IEEE 11th International Workshop on Computational Intelligence and Applications, IWCIA 2019 - Proceedings).

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title = "Neurosynaptic Computational Elements for Adaptive Transient Synchrony: Biophysical Accuracy versus Hardware Complexity",

abstract = "In this paper, we examine electro-chemically accurate, multi-compartment, neurosynaptic computational elements, and analyze their complexity, accuracy, and flexibility in signal processing of a time-varying task. We evaluate distributed patterns of simultaneously firing neurons in space and time, and we establish a transient synchrony and homeostatic regulation mechanism upon the underlying synaptic connectivity. With synchronic spiking, we form synchronous groups of neuronal subpopulations, which represent content forming a coherent entity. The neurosynaptic computational elements implemented on Xilinx Virtex 7 XC7VX550 FPGA board illustrate feasibility of the methodology.",

keywords = "biophysically-accurate models, FPGA, neuronomorphic computing, neurosynaptic computatioal elements",

author = "Amir Zjajo",

year = "2019",

doi = "10.1109/IWCIA47330.2019.8955105",

language = "English",

series = "2019 IEEE 11th International Workshop on Computational Intelligence and Applications, IWCIA 2019 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

pages = "3--8",

booktitle = "2019 IEEE 11th International Workshop on Computational Intelligence and Applications, IWCIA 2019 - Proceedings",

address = "United States",

note = "11th IEEE International Workshop on Computational Intelligence and Applications, IWCIA 2019 ; Conference date: 09-11-2019 Through 10-11-2019",

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Zjajo, A 2019, Neurosynaptic Computational Elements for Adaptive Transient Synchrony: Biophysical Accuracy versus Hardware Complexity. in 2019 IEEE 11th International Workshop on Computational Intelligence and Applications, IWCIA 2019 - Proceedings., 8955105, 2019 IEEE 11th International Workshop on Computational Intelligence and Applications, IWCIA 2019 - Proceedings, Institute of Electrical and Electronics Engineers (IEEE), pp. 3-8, 11th IEEE International Workshop on Computational Intelligence and Applications, IWCIA 2019, Hiroshima, Japan, 9/11/19. https://doi.org/10.1109/IWCIA47330.2019.8955105

Neurosynaptic Computational Elements for Adaptive Transient Synchrony: Biophysical Accuracy versus Hardware Complexity. / Zjajo, Amir.
2019 IEEE 11th International Workshop on Computational Intelligence and Applications, IWCIA 2019 - Proceedings. Institute of Electrical and Electronics Engineers (IEEE), 2019. p. 3-8 8955105 (2019 IEEE 11th International Workshop on Computational Intelligence and Applications, IWCIA 2019 - Proceedings).

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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AB - In this paper, we examine electro-chemically accurate, multi-compartment, neurosynaptic computational elements, and analyze their complexity, accuracy, and flexibility in signal processing of a time-varying task. We evaluate distributed patterns of simultaneously firing neurons in space and time, and we establish a transient synchrony and homeostatic regulation mechanism upon the underlying synaptic connectivity. With synchronic spiking, we form synchronous groups of neuronal subpopulations, which represent content forming a coherent entity. The neurosynaptic computational elements implemented on Xilinx Virtex 7 XC7VX550 FPGA board illustrate feasibility of the methodology.

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Zjajo A. Neurosynaptic Computational Elements for Adaptive Transient Synchrony: Biophysical Accuracy versus Hardware Complexity. In 2019 IEEE 11th International Workshop on Computational Intelligence and Applications, IWCIA 2019 - Proceedings. Institute of Electrical and Electronics Engineers (IEEE). 2019. p. 3-8. 8955105. (2019 IEEE 11th International Workshop on Computational Intelligence and Applications, IWCIA 2019 - Proceedings). doi: 10.1109/IWCIA47330.2019.8955105

Neurosynaptic Computational Elements for Adaptive Transient Synchrony: Biophysical Accuracy versus Hardware Complexity

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