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Digital cellular implementation of Morris-Lecar neuron model

The detailed biological neuron models such as Morris-Lecar cannot be easily implemented using conventional digital or analog Euler-based methods due to the presence of nonlinear functions and complex operations in their equations. This study presents an efficient cellular-based digital architecture for implementing Morris-Lecar neuron model. Digital hardware post synthesis results show that this hardware model is able to reproduce various responses of the biological model. The proposed architecture is not dependent on the complexity of the equations, and applies no function approximation method to deliver implementable equations. This implies that all other detailed neuron models can also be implemented by this structure. High programmability of the proposed hardware model also enables it to be applied to embedding neuromorphic hardware and real-time applications.