Stochastic modeling of neuronal signaling

stochastic Modeling of Neuronal Dynamics

Special iSSue i Modeling of brain processes has emerged as a promising research topic, which can help in the understanding of the human cognition. Investigations in this field have delivered successful implementations of learning and memory along with lines inspired by neural architectures. These findings have promoted optimism that a sufficiently complex artificial neural network would reprodu...

Neuronal modeling with intracellular calcium signaling.

Cytosolic Ca(2+) ions play an important role in the regulation of numerous aspects of cellular activity in virtually all cell types. There is a complex interaction between the neuronal electrical signals on plasma membrane and the chemical signals of intracellular calcium. Each neuron can be considered as a binary membrane system with plasma membrane and endoplasmic reticulum membrane, and the ...

Modeling Neuronal Signal Transduction Using Itô Stochastic Differential Equations and the Gillespie Stochastic Simulation Algorithm

Several discrete, as well as continuous, stochastic approaches have been developed for the time-series simulation of biochemical systems. Stochastic approaches, in general, are needed because chemical reactions involve discrete, random collisions between individual chemical species. One of the well-known discrete stochastic approaches is the computationally demanding Gillespie stochastic simula...

Stochastic models of neuronal dynamics.

Cortical activity is the product of interactions among neuronal populations. Macroscopic electrophysiological phenomena are generated by these interactions. In principle, the mechanisms of these interactions afford constraints on biologically plausible models of electrophysiological responses. In other words, the macroscopic features of cortical activity can be modelled in terms of the microsco...

Stochastic modelling of neuronal data