Abstract
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.
Highlights
Brain disorders are among the leading causes of disabilities worldwide
We proposed a Spiking Neural Network (SNN) based on these equations and adapted to digital hardware allowing the computation of cortical neurons connected by synapses including synaptic noise to incorporate spontaneous activities
The optimization of the Hodgkin-Huxley equations using hyperbolic function and power of two parameters allows the use of large scale with low resources of biomimetic neurons
Summary
Brain disorders are among the leading causes of disabilities worldwide. The increasing number of neurological diseases raised scientists to reconsider the way of studying the human cells and the process of healing brain afflictions. Reproducing neuro-mimetic activities and improving the connection between living cells and machines became mandatory for designing neuroprosthesis Such devices (Nicolelis and Lebedev, 2009; Hochberg et al, 2012; Bonifazi et al, 2013) have been focused on the interactions with neuronal cell assemblies especially on mimicking the spontaneous activities of the biological neural networks. Biomimetic SNN is one way to explore for designing new generation of neuroprosthesis and will facilitate the bio-hybrid experiments. Such components make one reconsider about the interaction between artificial devices and living cells
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