ABSTRACT
McCulloch and Pitts developed the first mathematical model of a neuron. Mathematically, the degree of influence that one neuron has on another is represented by a weight associated with the interconnection between them. Mathematical depiction of neural activities purport the analytical visualization of the function of real neurons. The logical neuron lends itself to analysis through boolean-space, and therefore an isomorphism between the bistable state of the neurons and the corresponding logic networks can be established via appropriate logical expressions or boolean functions as advocated by McCulloch and Pitts. Artificial neural networks, a modern trend in the art of computational science, are biologically inspired in that they perform in a manner similar to the basic functions of the biological neuron. Pertinent to the neural activity across the interconnected set of cells, the probabilistic attributes of neuronal spikes can be described by the considerations of random walk theory as proposed by Gerstein and Mandelbrot.
