Luo, J, Nikolic, K, Evans, B, Dong, N, Sun, X, Andras, PE ORCID: https://orcid.org/0000-0002-9321-3296, Yakovlev, A and Degenaar, P (2016) Optogenetics in Silicon: A Neural Processor for Predicting Optically Active Neural Networks. IEEE Transactions on Biomedical Circuits and Systems, 11 (1). pp. 15-27.

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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 a four-state Channelrhodopsin-2 (ChR2) model into reconfigurable silicon hardware. Our architecture consists of a Field Programmable Gated Array (FPGA) with a custom-built 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 0.03 ms processing time per sub-frame for a single neuron and 9.7 ms for a fully connected network of 500 neurons with a given FPGA frequency of 56.7 MHz. It can therefore be utilized for exploration of closed loop processing and tuning of biologically realistic optogenetic circuitry.

Item Type: Article
Subjects: Q Science > QA Mathematics
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Divisions: Faculty of Natural Sciences > School of Physical and Geographical Sciences
Depositing User: Symplectic
Date Deposited: 08 Jun 2016 13:57
Last Modified: 09 Apr 2019 11:56
URI: http://eprints.keele.ac.uk/id/eprint/1780

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