ABSTRACT
The interconnected biological neurons and the network of their artificial counterparts have been modeled in physioanatomical perspectives, largely via cognitive considerations and in terms of physical reasonings based on statistical mechanics of interacting units. The subject of stochastical attributions to neuronal sample-space has been researched historically in some perspectives, namely, characterizing the response of a single neuron and analyzing the behavior of a set of interconnected neurons. Modern information processing systems are neuromimetic and becoming more and more sophisticated as their functional capabilities are directed to emulate the diversified activities of complex neural systems. The strength of physical modeling of a neural complex lies in a coherent approach that accounts for both stochastical considerations pertinent to interacting cells and self-regulatory features of neurocybernetics. The mediating process common to both considerations is the entropy or the informational entity associated with the memory, computational, and self-controlling efforts in the neural architecture.
