Self-consistent formulations for stochastic nonlinear neuronal dynamics

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...

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 Multiconfigurational Self-Consistent Field Theory.

The multiconfigurational self-consistent field theory is considered the standard starting point for almost all multireference approaches required for strongly correlated molecular problems. The limitation of the approach is generally given by the number of strongly correlated orbitals in the molecule, since its cost will grow exponentially with this number. We present a new multiconfigurational...

Consistent nonlinear dynamics: identifying model inadequacy

Empirical modelling often aims for the simplest model consistent with the data. A new technique is presented which quantifies the consistency of the model dynamics as a function of location in state space. As is well-known, traditional statistics of nonlinear models like root-mean-square (RMS) forecast error can prove misleading. Testing consistency is shown to overcome some of the deficiencies...

Dynamics of Nonlinear Stochastic Systems