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research-article

Biologically compatible neural networks with reconfigurable hardware

Authors:

Juan Carlos Moctezuma,

Joseph P. McGeehan,

Jose Luis Nunez-YanezAuthors Info & Claims

Microprocessors & Microsystems, Volume 39, Issue 8

Pages 693 - 703

https://doi.org/10.1016/j.micpro.2015.09.003

Published: 01 November 2015 Publication History

Abstract

This paper presents a reconfigurable hardware neuro-simulator specifically designed to emulate biophysically accurate and biologically compatible neural networks. The platform is based on FPGA technology which is used to create real-time custom neuroprocessors with floating point accuracy and a novel hybrid time-event driven architecture for synaptic integration. Through a series of experiments the dynamics of the neuroprocessors are evaluated and compared with real neuron responses. The problem of interconnecting neurons with individual synapses is tackled with a novel synaptic architecture where all incoming synapses are merged efficiently in one single accumulative process without losing biological information. The case studies demonstrate the suitability of conductance-based models and FPGA platforms to simulate living organisms' behaviour in a biological compatible context.

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Cited By

Liu JHuang XHuang YLuo YYang S(2019)Multi-objective Spiking Neural Network Hardware Mapping Based on Immune Genetic AlgorithmArtificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation10.1007/978-3-030-30487-4_58(745-757)Online publication date: 17-Sep-2019
https://dl.acm.org/doi/10.1007/978-3-030-30487-4_58
Wan LLiu JHarkin JMcDaid LLuo Y(2018)Layered tile architecture for efficient hardware spiking neural networksMicroprocessors & Microsystems10.1016/j.micpro.2017.07.00553:C(21-32)Online publication date: 28-Dec-2018
https://dl.acm.org/doi/10.1016/j.micpro.2017.07.005

Biologically compatible neural networks with reconfigurable hardware
1. Computing methodologies
  1. Modeling and simulation
    1. Model development and analysis

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cover image Microprocessors & Microsystems

Microprocessors & Microsystems Volume 39, Issue 8

November 2015

718 pages

ISSN:0141-9331

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Copyright © Elsevier B.V.

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 November 2015

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Cited By

Liu JHuang XHuang YLuo YYang S(2019)Multi-objective Spiking Neural Network Hardware Mapping Based on Immune Genetic AlgorithmArtificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation10.1007/978-3-030-30487-4_58(745-757)Online publication date: 17-Sep-2019
https://dl.acm.org/doi/10.1007/978-3-030-30487-4_58
Wan LLiu JHarkin JMcDaid LLuo Y(2018)Layered tile architecture for efficient hardware spiking neural networksMicroprocessors & Microsystems10.1016/j.micpro.2017.07.00553:C(21-32)Online publication date: 28-Dec-2018
https://dl.acm.org/doi/10.1016/j.micpro.2017.07.005

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