Year: 2013
Type: other
Abstract: One of the most notorious characteristics of neuronal electrical activity is its \nvariability, whose origin is not just instrumentation noise, but mainly the intrinsically \nstochastic nature of neural computations. Neuronal models based \non deterministic differential equations cannot account for such variability, \nbut they can be extended to do so by incorporating random components. \nHowever, the computational cost of this strategy and the storage requirements \ngrow exponentially with the number of stochastic parameters, quickly \nexceeding the capacities of current supercomputers. This issue is critical in \nNeurodynamics, where mechanistic interpretation of large, complex, nonlinear \nsystems is essential. In this paper we present accurate and computationally \nefficient methods to introduce and analyse variability in neurodynamic \nmodels depending on multiple uncertain parameters. Their use is illustrated \nwith relevant examples
Authors: Aldemar Torres Valderrama, Joke Blom
Language: English
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2590502366