Dataset Information

Metastable dynamics in heterogeneous neural fields.

ABSTRACT: We present numerical simulations of metastable states in heterogeneous neural fields that are connected along heteroclinic orbits. Such trajectories are possible representations of transient neural activity as observed, for example, in the electroencephalogram. Based on previous theoretical findings on learning algorithms for neural fields, we directly construct synaptic weight kernels from Lotka-Volterra neural population dynamics without supervised training approaches. We deliver a MATLAB neural field toolbox validated by two examples of one- and two-dimensional neural fields. We demonstrate trial-to-trial variability and distributed representations in our simulations which might therefore be regarded as a proof-of-concept for more advanced neural field models of metastable dynamics in neurophysiological data.

SUBMITTER: Schwappach C

PROVIDER: S-EPMC4485166 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Metastable dynamics in heterogeneous neural fields.

Schwappach Cordula C Hutt Axel A Beim Graben Peter P

Frontiers in systems neuroscience 20150630

We present numerical simulations of metastable states in heterogeneous neural fields that are connected along heteroclinic orbits. Such trajectories are possible representations of transient neural activity as observed, for example, in the electroencephalogram. Based on previous theoretical findings on learning algorithms for neural fields, we directly construct synaptic weight kernels from Lotka-Volterra neural population dynamics without supervised training approaches. We deliver a MATLAB neur ...[more]

PMID: 26175671

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Dataset Information

Metastable dynamics in heterogeneous neural fields.

Publications

Metastable dynamics in heterogeneous neural fields.

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