Title :
Dynamical digital silicon neurons
Author :
Cassidy, Andrew ; Andreou, Andreas G.
Author_Institution :
Dept. of Electr. & Comput. Eng., Johns Hopkins Univ., Baltimore, MD
Abstract :
We present an array of dynamical digital silicon neurons implementing the Izhikevich neuron model. The FPGA based array consists of 32 physical neurons, each time multiplexing the state of 8 virtual neurons, for a total of 256 independent neurons. The neural array operates at 5,000 times faster than real time, performing over 20.48 GOPS (giga operations per second). It is intended for neural simulation acceleration, neural prostheses, and neuromorphic systems.
Keywords :
field programmable gate arrays; neurophysiology; physiological models; silicon; FPGA; Izhikevich neuron model; Si; dynamical digital silicon neurons; multiplexing; neural prostheses; neural simulation acceleration; neuromorphic systems; Acceleration; Biological system modeling; Biology computing; Computational modeling; Computer architecture; Field programmable gate arrays; Neuromorphics; Neurons; Prosthetics; Silicon;
Conference_Titel :
Biomedical Circuits and Systems Conference, 2008. BioCAS 2008. IEEE
Conference_Location :
Baltimore, MD
Print_ISBN :
978-1-4244-2878-6
Electronic_ISBN :
978-1-4244-2879-3
DOI :
10.1109/BIOCAS.2008.4696931
