PMID- 31068781
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20200930
IS  - 1662-4548 (Print)
IS  - 1662-453X (Electronic)
IS  - 1662-453X (Linking)
VI  - 13
DP  - 2019
TI  - Optimized Real-Time Biomimetic Neural Network on FPGA for Bio-hybridization.
PG  - 377
LID - 10.3389/fnins.2019.00377 [doi]
LID - 377
AB  - Neurological diseases can be studied by performing bio-hybrid experiments using a 
      real-time biomimetic Spiking Neural Network (SNN) platform. The Hodgkin-Huxley 
      model offers a set of equations including biophysical parameters which can serve 
      as a base to represent different classes of neurons and affected cells. Also, 
      connecting the artificial neurons to the biological cells would allow us to 
      understand the effect of the SNN stimulation using different parameters on nerve 
      cells. Thus, designing a real-time SNN could useful for the study of simulations 
      of some part of the brain. Here, we present a different approach to optimize the 
      Hodgkin-Huxley equations adapted for Field Programmable Gate Array (FPGA) 
      implementation. The equations of the conductance have been unified to allow the 
      use of same functions with different parameters for all ionic channels. The low 
      resources and high-speed implementation also include features, such as synaptic 
      noise using the Ornstein-Uhlenbeck process and different synapse receptors 
      including AMPA, GABAa, GABAb, and NMDA receptors. The platform allows real-time 
      modification of the neuron parameters and can output different cortical neuron 
      families like Fast Spiking (FS), Regular Spiking (RS), Intrinsically Bursting 
      (IB), and Low Threshold Spiking (LTS) neurons using a Digital to Analog Converter 
      (DAC). Gaussian distribution of the synaptic noise highlights similarities with 
      the biological noise. Also, cross-correlation between the implementation and the 
      model shows strong correlations, and bifurcation analysis reproduces similar 
      behavior compared to the original Hodgkin-Huxley model. The implementation of one 
      core of calculation uses 3% of resources of the FPGA and computes in real-time 
      500 neurons with 25,000 synapses and synaptic noise which can be scaled up to 
      15,000 using all resources. This is the first step toward neuromorphic system 
      which can be used for the simulation of bio-hybridization and for the study of 
      neurological disorders or the advanced research on neuroprosthesis to regain lost 
      function.
FAU - Khoyratee, Farad
AU  - Khoyratee F
AD  - Laboratoire de l'Integration du Materiau au Systeme, Bordeaux INP, CNRS UMR 5218, 
      University of Bordeaux, Talence, France.
FAU - Grassia, Filippo
AU  - Grassia F
AD  - LTI Laboratory, EA 3899, University of Picardie Jules Verne, Amiens, France.
FAU - Saighi, Sylvain
AU  - Saighi S
AD  - Laboratoire de l'Integration du Materiau au Systeme, Bordeaux INP, CNRS UMR 5218, 
      University of Bordeaux, Talence, France.
FAU - Levi, Timothee
AU  - Levi T
AD  - Laboratoire de l'Integration du Materiau au Systeme, Bordeaux INP, CNRS UMR 5218, 
      University of Bordeaux, Talence, France.
AD  - Institute of Industrial Science, The University of Tokyo, Tokyo, Japan.
LA  - eng
PT  - Journal Article
DEP - 20190424
PL  - Switzerland
TA  - Front Neurosci
JT  - Frontiers in neuroscience
JID - 101478481
PMC - PMC6491680
OTO - NOTNLM
OT  - Hodgkin-Huxley
OT  - bio-hybrid
OT  - biomimetic
OT  - neural diseases
OT  - neuromorphic
OT  - spiking neural network
EDAT- 2019/05/10 06:00
MHDA- 2019/05/10 06:01
PMCR- 2019/01/01
CRDT- 2019/05/10 06:00
PHST- 2018/12/03 00:00 [received]
PHST- 2019/04/02 00:00 [accepted]
PHST- 2019/05/10 06:00 [entrez]
PHST- 2019/05/10 06:00 [pubmed]
PHST- 2019/05/10 06:01 [medline]
PHST- 2019/01/01 00:00 [pmc-release]
AID - 10.3389/fnins.2019.00377 [doi]
PST - epublish
SO  - Front Neurosci. 2019 Apr 24;13:377. doi: 10.3389/fnins.2019.00377. eCollection 
      2019.