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

  1. Lookup NU author(s)
  2. JunWen Luo
  3. Na Dong
  4. Professor Peter Andras
  5. Professor Alex Yakovlev
  6. Dr Patrick Degenaar
Author(s)Luo J, Nikolic K, Evans B, Dong N, Sun X, Andras P, Yakovlev A, Degenaar P
Publication type Article
JournalIEEE Transactions on Biomedical Circuits and Systems
Year2017
Volume11
Issue1
Pages15-27
ISSN (print)1932-4545
ISSN (electronic)1940-9990
Full text is available for this publication:
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.
PublisherIEEE
URLhttp://dx.doi.org/10.1109/TBCAS.2016.2571339
DOI10.1109/TBCAS.2016.2571339
Data Source Locationhttp://dx.doi.org/10.17634/124074-1
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