Abstract
With the current need for the miniaturization of electronic circuits and transistors, there is a critical problem of fluctuations that create signal interference for the outputs of those circuits. One solution is to use those fluctuations and stochastic manipulation to the benefit of computing architectures. Such a solution is defined as Brownian computing, and it is possible to apply it with Brownian circuits. At those physical limits, nonlinear dynamics dominate, and synaptic modeling is critical. Synapses are the target of most, if not all, brain disorders. Because of the enormous number of synapses in our brain (~ 100,000,000,000), it is often difficult to comprehend how defects in one type of synapse yield the overall loss in brain performance characteristic of all brain disorders. Computational modeling of synapses has been a slow process that is constrained by advances in the interdisciplinary fusion of physics, both for measuring and data analysis, and advances in molecular biology or biophysics. These constraints are also computational: some models have detailed sets of coupled equations with experimental parameters that, although accurate, are impossible to solve because of the enormous number of synapses. This limitation impedes understanding of brain information processing, both in health and in disease states.