Neural field equations with neuron‐dependent Heaviside‐type activation function and spatial‐dependent delay

2020 ◽  
Author(s):  
Evgenii Burlakov ◽  
Evgeny Zhukovskiy ◽  
Vitaly Verkhlyutov
Keyword(s):  
Activation Function ◽  
Neural Field ◽  
Field Equations ◽  
Neural Field Equations

On ring solutions of neural field equations

2021 ◽  
pp. 363-371
Author(s):  
Rachid Atmania ◽  
Evgenii O. Burlakov ◽  
Ivan N. Malkov
Keyword(s):  
Activation Function ◽  
Stationary Solutions ◽  
Neural Field ◽  
Neuronal Activation ◽  
Field Equations ◽  
Broad Ring ◽  
Activation Threshold ◽  
Electrical Potentials ◽  
Radially Symmetric Solutions ◽  
Neural Field Equations

The article is devoted to investigation of integro-differential equation with the Hammerstein integral operator of the following form: ∂_t u(t,x)=-τu(t,x,x_f )+∫_(R^2)▒〖ω(x-y)f(u(t,y) )dy, t≥0, x∈R^2 〗. The equation describes the dynamics of electrical potentials u(t,x) in a planar neural medium and has the name of neural field equation.We study ring solutions that are represented by stationary radially symmetric solutions corresponding to the active state of the neural medium in between two concentric circles and the rest state elsewhere in the neural field. We suggest conditions of existence of ring solutions as well as a method of their numerical approximation. The approach used relies on the replacement of the probabilistic neuronal activation function f that has sigmoidal shape by a Heaviside-type function. The theory is accompanied by an example illustrating the procedure of investigation of ring solutions of a neural field equation containing a typically used in the neuroscience community neuronal connectivity function that allows taking into account both excitatory and inhibitory interneuronal interactions. Similar to the case of bump solutions (i. e. stationary solutions of neural field equations, which correspond to the activated area in the neural field represented by the interior of some circle) at a high values of the neuronal activation threshold there coexist a broad ring and a narrow ring solutions that merge together at the critical value of the activation threshold, above which there are no ring solutions.

On connection between continuous and discontinuous neural field models with microstructure: II. Radially symmetric stationary solutions in 2D (“bumps”)

2020 ◽  
pp. 6-17
Author(s):  
Evgenii O. Burlakov ◽  
Ivan N. Malkov
Keyword(s):  
Activation Function ◽  
Stationary Solutions ◽  
Neural Field ◽  
Neural Fields ◽  
Activation Functions ◽  
Field Equations ◽  
Discontinuous Activation ◽  
Present Part ◽  
Discontinuous Activation Functions ◽  
Neural Field Equations

We suggest a method allowing to investigate existence and the measure of proximity between the stationary solutions to continuous and discontinuous neural fields with microstructure. The present part involves results on proximity of the stationary solutions to specific homogenized neural field equations with continuous and discontinuous activation functions. The results of numerical investigation of radially symmetric stationary solutions (bumps) to the neural field with a discontinuous activation function and a given microstructure are presented.

scholarly journals Control of Nonlinear Wave Solutions to Neural Field Equations

2019 ◽  
Vol 18 (2) ◽  
pp. 1015-1036 ◽  
Author(s):  
Alexander Ziepke ◽  
Steffen Martens ◽  
Harald Engel
Keyword(s):  
Nonlinear Wave ◽  
Neural Field ◽  
Field Equations ◽  
Wave Solutions ◽  
Neural Field Equations ◽  
Nonlinear Wave Solutions

scholarly journals Spatiotemporal canards in neural field equations

2017 ◽  
Vol 95 (4) ◽  
Author(s):  
D. Avitabile ◽  
M. Desroches ◽  
E. Knobloch
Keyword(s):  
Neural Field ◽  
Field Equations ◽  
Neural Field Equations

IMAGE PROCESSING AND SELF-ORGANIZING CNN

2005 ◽  
Vol 15 (09) ◽  
pp. 2939-2958 ◽  
Author(s):  
MAKOTO ITOH ◽  
LEON O. CHUA
Keyword(s):  
Image Processing ◽  
Pattern Formation ◽  
Vector Fields ◽  
Reaction Diffusion ◽  
Neural Field ◽  
Reaction Diffusion Equations ◽  
Diffusion Equations ◽  
Self Organization ◽  
Field Equations ◽  
Neural Field Equations

CNN templates for image processing and pattern formation are derived from neural field equations, advection equations and reaction–diffusion equations by discretizing spatial integrals and derivatives. Many useful CNN templates are derived by this approach. Furthermore, self-organization is investigated from the viewpoint of divergence of vector fields.

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