OS9-3 Real-time Digital Implementation of HH neural network on FPGA: cortical neuron simulation

Title:	OS9-3 Real-time Digital Implementation of HH neural network on FPGA: cortical neuron simulation
Publication:	ICAROB2018
Volume:	23
Pages:	469-472
ISSN:	2188-7829
DOI:	10.5954/ICAROB.2018.OS9-3
Author(s):	Farad Khoyratee, Sylvain Saïghi, Timothee Levi
Publication Date:	February 2, 2018
Keywords:	Silicon neuron, Hodgkin-Huxley, FPGA, neurological diseases, CORDIC
Abstract:	Research on neurological disorder led to an alternative treatment using biomimetic and real-time hardware as neuroprosthesis. These devices must follow several requirements such as the connection between cells and machine or the neurons activities, the bio-hybrids experiments. Furthermore, timing and shape of the action potential (AP) must reproduce the same dynamics of a real nerve impulse. The system must be real time, biomimetic and tunable. Thus, a Field Programmable Gate Array (FPGA) has been chosen to carry the implementation of cortical neurons. The Hodgkin-Huxley (HH) model is the most plausible and realistic one. A model including Fast Spiking (FS), Regular Spiking (RS) and Low-Threshold Spiking (LTS) neurons has been implemented in a Field Programmable Gate Array (FPGA) as a neural network. Some digital methods were used like the CORDIC algorithm or Euler method to solve exponential and differential equations. Here a pipelined implementation of a cortical neural network which contains 150 neurons is presented. This should allow future studies of neurological effect on cells and neurons replacement.
PDF File:	https://alife-robotics.co.jp/members2018/icarob/data/html/data/OS_pdf/OS9/OS9-3.pdf
Copyright:	© The authors. This article is distributed under the terms of the Creative Commons Attribution License 4.0, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited. See for details: https://creativecommons.org/licenses/by-nc/4.0/

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