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Optogenetics in Silicon: A Neural Processor for Predicting Optically Active Neural Networks

Lookup NU author(s): JunWen Luo, Na Dong, Professor Peter Andras, Professor Alex Yakovlev, Dr Patrick Degenaar

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Abstract

We present a reconfigurable neural processor for real-time simulation and prediction of opto-neural behaviour. We combined a detailed Hodgkin-Huxley CA3 neuron integrated with the four-state Channelrhodopsin (ChR2) model into a reconfigurable silicon hardware. Our architecture consists of a Field Programmable Gated Array (FPGA) with a custom-build computing data-path, a separate data management system and a memory approach based router. Advancements over previous work include the incorporation of short and long-term calcium and light-dependent ion channels in reconfigurable hardware . Also, the developed processor is computationally efficient, requiring only 0.03ms processing time per sub-frame for a single neuron and 9.7ms for a fully connected network of 500 neurons with a given FPGA frequency of 56.7MHz. It can therefore be utilized for exploration of closed loop processing and tuning of biologically realistic optogenetic circuitry.


Publication metadata

Author(s): Luo J, Nikolic K, Evans B, Dong N, Sun X, Andras P, Yakovlev A, Degenaar P

Publication type: Article

Journal: IEEE Transactions on Biomedical Circuits and Systems

Year: 2017

Volume: 11

Issue: 1

Pages: 15-27

Online publication date: 17/08/2016

Acceptance date: 27/04/2016

Print publication date: 01/02/2017

ISSN (print): 1932-4545

ISSN (electronic): 1940-9990

Publisher: IEEE

URL: http://dx.doi.org/10.1109/TBCAS.2016.2571339

DOI: 10.1109/TBCAS.2016.2571339

Data Source Location

http://dx.doi.org/10.17634/124074-1


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