ABSTRACT
The statistical aspects of random intervals between the action potentials of biological neurons are normally decided by the irregularities due to neural conduction velocity/dynamics, axonal fiber type mixture, and synchronization/asynchronization effects percentage of polyphasic action potentials. The effect of intracellular disturbances when addressed to artificial neural networks refers to stochastical instability in solving optimization problems. Such noise-induced effects would render the problem suboptimal with increased computational time. The inevitable presence of noise in a neural assembly permits the neurons to change their internal states in a random manner. The state-transition in a neural complex represents a dichotomous process and the presence of noise would place the bistable potential at an unstable equilibrium point. The colored nature of the cellular noise also refers implicitly to the markovian nature of the temporal statistics of the action potentials which assume bistable values at random intervals.
