English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We introduce and study a new model of interacting neural networks,incorporating the spatial dimension (e.g. position of neurons across thecortex) and some learning processes. The dynamic of each neural network isdescribed via the elapsed time model, that is, the neurons are described by theelapsed time since their last discharge and the chosen learning processes areessentially inspired from the Hebbian rule. We then obtain a system ofintegro-differential equations, from which we analyze the convergence tostationary states by the means of entropy method and Doeblin's theory in thecase of weak interconnections. We also consider the situation where neuralactivity is faster than the learning process and give conditions where one canapproximate the dynamics by a solution with a similar profile of a steadystate. For stronger interconnections, we present some numerical simulations toobserve how the parameters of the system can give different behaviors andpattern formations.

Dynamics of neural networks with elapsed time model and learning processes