Optogenetics in Silicon: A Neural Processor for Predicting Optically Active Neural Networks
datasetposted on 01.01.2016 by J Luo, K Nikolic, B Evans, N Dong, X Sun, P Andras, A Yakovlev, P Degenaar
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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. 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. The corresponding simulation results are shown in the attached data.