An accurate and efficient numerical method for neural field models with transmission delays

2022 ◽  
Vol 135 ◽  
pp. 206-216
Author(s):  
W. Zhao ◽  
Y.C. Hon
2015 ◽  
Vol 297 ◽  
pp. 88-101 ◽  
Author(s):  
K. Dijkstra ◽  
S.A. van Gils ◽  
S.G. Janssens ◽  
Yu.A. Kuznetsov ◽  
S. Visser

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Len Spek ◽  
Yuri A. Kuznetsov ◽  
Stephan A. van Gils

AbstractA neural field models the large scale behaviour of large groups of neurons. We extend previous results for these models by including a diffusion term into the neural field, which models direct, electrical connections. We extend known and prove new sun-star calculus results for delay equations to be able to include diffusion and explicitly characterise the essential spectrum. For a certain class of connectivity functions in the neural field model, we are able to compute its spectral properties and the first Lyapunov coefficient of a Hopf bifurcation. By examining a numerical example, we find that the addition of diffusion suppresses non-synchronised steady-states while favouring synchronised oscillatory modes.


2012 ◽  
Vol 66 (4-5) ◽  
pp. 837-887 ◽  
Author(s):  
S. A. van Gils ◽  
S. G. Janssens ◽  
Yu. A. Kuznetsov ◽  
S. Visser

2002 ◽  
Vol 14 (8) ◽  
pp. 1801-1825 ◽  
Author(s):  
Thomas Wennekers

This article presents an approximation method to reduce the spatiotemporal behavior of localized activation peaks (also called “bumps”) in nonlinear neural field equations to a set of coupled ordinary differential equations (ODEs) for only the amplitudes and tuning widths of these peaks. This enables a simplified analysis of steady-state receptive fields and their stability, as well as spatiotemporal point spread functions and dynamic tuning properties. A lowest-order approximation for peak amplitudes alone shows that much of the well-studied behavior of small neural systems (e.g., the Wilson-Cowan oscillator) should carry over to localized solutions in neural fields. Full spatiotemporal response profiles can further be reconstructed from this low-dimensional approximation. The method is applied to two standard neural field models: a one-layer model with difference-of-gaussians connectivity kernel and a two-layer excitatory-inhibitory network. Similar models have been previously employed in numerical studies addressing orientation tuning of cortical simple cells. Explicit formulas for tuning properties, instabilities, and oscillation frequencies are given, and exemplary spatiotemporal response functions, reconstructed from the low-dimensional approximation, are compared with full network simulations.


2019 ◽  
Vol 15 (11) ◽  
pp. e1007442
Author(s):  
Michael E. Rule ◽  
David Schnoerr ◽  
Matthias H. Hennig ◽  
Guido Sanguinetti

Author(s):  
Dimitris A. Pinotsis ◽  
Marco Leite ◽  
Karl J. Friston

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