Digital Implementation Of Biologically Inspired Wilson Model, Population Behavior, And Learning

Spiking neurons, as a computational unit, are the main part in biological information processing systems. This paper presents a digital hardware implementation of a biological neuron on a field-programmable gate array due to its high accuracy and high speed, especially for large-scale simulations which is a key objective in the neuromorphic research field. Although this is a computationally expensive task, the use of more biological realistic system results in higher accuracy in mimicking biological behaviors of neural networks. Given that, the Wilson model is one of the most important biological neuron models that can be used in the architecture of spiking neural networks. To be closer to biological systems, a method is proposed to test the possibility of implementation of the Wilson neuron model on digital platforms. The results of the hardware implementation of the Wilson neuron and a spiking network on a field-programmable gate array, capable of character recognition with supervised learning algorithm, are presented in this paper; moreover, population behavior of this model is simulated. In large-scale implementation of 2000 Wilson neuron model, population capability, feasibility, and costs are investigated. This paper presents a method to the implementation of Wilson neurons on digital platforms, suggesting that the available system is an attainable platform for the implementation of large-scale biologically plausible neural networks on field-programmable gate array devices. Hardware synthesis, physical implementation on field-programmable gate array, and theoretical analysis confirm that the proposed model has hardware so that makes it an appropriate model for the large-scale digital implementation.