Implementation of Hardware Model for Spiking Neural Network
نویسندگان
چکیده
The izhikevich neuron model is well known for mimicking almost all dynamics of the biological neurons like Hodgkin-Huxley neuron models with much less hardware resources. Despite its versatility and biological plausibility, izhikevich neuron model is still not suited for a large scale neural network simulation due to its complexity compared to the simpler neuron models like integrate-and-fire model. In this paper, we implement a Spiking Neural Network (SNN) of the silicon neurons based on the izhikevich neuron model in order to show that it is feasible to simulate a large scale SNN. As a demonstration, we construct our system to simulate a sparse network of 1000 spiking neurons on Xilinx FPGA. During the simulation period (1000ms), the network exhibits a rhythmic activity in delta frequency range around 4Hz. This means that the proposed network can simulate a large scale SNN based on izhikevich neuron model for human cortical system.
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