title: 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.
authors.affiliation: Department of German Studies and Linguistics, Humboldt-Universität zu Berlin Berlin, Germany ; Department of Physics, Humboldt-Universität zu Berlin Berlin, Germany.
authors.forename: Cordula
authors.initials: C
authors.lastname: Schwappach
authors.affiliation: Team Neurosys, Inria Villers-les-Nancy, France ; Team Neurosys, Centre National de la Recherche Scientifique, UMR nō 7503, Loria Villers-les-Nancy, France ; Team Neurosys, UMR nō 7503, Loria, Université de Lorraine Villers-les-Nancy, France.
authors.forename: Axel
authors.initials: A
authors.lastname: Hutt
authors.affiliation: Department of German Studies and Linguistics, Humboldt-Universität zu Berlin Berlin, Germany ; Bernstein Center for Computational Neuroscience, Humboldt-Universität zu Berlin Berlin, Germany.
authors.forename: Peter
authors.initials: P
authors.lastname: Beim Graben
keywords: distributed representations; heteroclinic orbits; kernel construction; metastability; neural fields; sparsity; sub-networks; trial-to-trial variability