Proposed cellular automaton model for a neuronal patch with a thresholded linear activation function
Hodgkin-Huxley, and most of our current neuronal models consist of differential equations to explain the behavior of a single neuron with an electrical synapse, a chemical synapse, or both. In order to model a network of neurons, we need to couple these differential equations, and consequently, increasing the number of equations to solve. This will be difficult to do manually, and even, subjected to the limits of computing power of today’s computers, when done numerically. In this paper, we propose a model for a network of neurons (N = 10 000) placed in a 100 x 100 lattice, with a linear activation function as the rule of the cellular automaton.