Imperial College London
Alternate

DrKonstantinNikolic

Faculty of Engineering, Department of Electrical and Electronic Engineering

Visiting Professor
 
 
 
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Contact

 

k.nikolic

 
 
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Location

 

Bessemer 420CBessemer BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Luo:2016:10.1109/TBCAS.2016.2571339,
author = {Luo, J and Nikolic, K and Evans, BD and Dong, N and Sun, X and Andras, P and Yakovlev, A and Degenaar, P},
doi = {10.1109/TBCAS.2016.2571339},
journal = {IEEE Transactions on Biomedical Circuits and Systems},
pages = {15--27},
title = {Optogenetics in silicon: a neural processor for predicting optically active neural networks},
url = {http://dx.doi.org/10.1109/TBCAS.2016.2571339},
volume = {11},
year = {2016}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - Luo,J
AU  - Nikolic,K
AU  - Evans,BD
AU  - Dong,N
AU  - Sun,X
AU  - Andras,P
AU  - Yakovlev,A
AU  - Degenaar,P
DO  - 10.1109/TBCAS.2016.2571339
EP  - 27
PY  - 2016///
SN  - 1940-9990
SP  - 15
TI  - Optogenetics in silicon: a neural processor for predicting optically active neural networks
T2  - IEEE Transactions on Biomedical Circuits and Systems
UR  - http://dx.doi.org/10.1109/TBCAS.2016.2571339
UR  - http://hdl.handle.net/10044/1/39654
VL  - 11
ER  -
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