ABSTRACT
Randomly interconnected neurons emulate a redundant system of parallel connections with almost unlimited routings of signal proliferation through an enormous number of von Neumann random switches. The pseudo specific heat refers to the average rate of change of pseudo-temperature with respect to the objective function. Adoption of thermodynamic annealing to neural activity refers to achieving the global energy minimum criterion. The cooperative process across the interconnections dictates a simple, but powerful massive parallelism and distribution of the state transitional progression and hence portrays a useful configuration model. Apart from the neural networks being modeled to represent the neurological fidelity, the adjunct consideration in the relevant modeling is the depiction of neural computation. Neural models represent general purpose learning systems that begin with no initial object-oriented knowledge. Annealing in the metallurgical sense refers to the physical process of heating a solid until it melts, followed by cooling it down until it crystallizes into a state with a perfect lauice structure.
