@ARTICLE{10.21494/ISTE.OP.2018.0270, 

TITLE={Digital implementation of stochastic biomimetic}, 
  
AUTHOR={Filippo Grassia, Takashi Kohno, Timothée Levi, }, 
	
JOURNAL={Cognitive Engineering}, 
	
VOLUME={1}, 

NUMBER={Issue 1}, 
	
YEAR={2017}, 

URL={https://openscience.fr/Digital-implementation-of-stochastic-biomimetic}, 
	
DOI={10.21494/ISTE.OP.2018.0270}, 
	
ISSN={2517-6978}, 
	
ABSTRACT={Millions of people around the world are affected by neurological disorders that impair good communication between the brain and the body. The development of neuroprostheses will have a social impact on the quality of life of patients. These neuroprostheses are designed on the basis of neuronal cell interactions, starting from the spontaneous intrinsic activities of neural networks until the stimulation of the neural networks in order to obtain a specific behavior. The long-term objective of replacing damaged neuron networks by artificial systems requires the development of neuron
models whose activity is similar to the biological electrophysiological activity of living biological networks: the biomimetic Spiking Neural Networks (SNN). On account of their parallel and distributed structures, spiking neuronal networks can simulate neuronal activities, potentially realizing an extremely large-scale network comparable to that of the human brain in future. This study explores the feasibility of simulation stochastic neurons in digital systems (FPGA), which realizes an implementation of a simple two-dimensional neuron model. The stochasticity is added by a source of current noise mimicking biological synaptic noise in the silicon neuron. The experimental results confirmed the validity of the developed stochastic FPGA implementation, which makes the implementation of the silicon neuron more biologically plausible.}}