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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Neuronal dynamics is driven by externally imposed or internally generatedrandom excitations/noise, and is often described by systems of random orstochastic ordinary differential equations. Such systems admit a distributionof solutions, which is (partially) characterized by the single-time jointprobability density function (PDF) of system states. It can be used tocalculate such information-theoretic quantities as the mutual informationbetween the stochastic stimulus and various internal states of the neuron(e.g., membrane potential), as well as various spiking statistics. When randomexcitations are modeled as Gaussian white noise, the joint PDF of neuron statessatisfies exactly a Fokker-Planck equation. However, most biologicallyplausible noise sources are correlated (colored). In this case, the resultingPDF equations require a closure approximation. We propose two methods forclosing such equations: a modified nonlocal large-eddy-diffusivity closure anda data-driven closure relying on sparse regression to learn relevant features.The closures are tested for the stochastic non-spiking leaky integrate-and-fireand FitzHugh-Nagumo (FHN) neurons driven by sine-Wiener noise. Mutualinformation and total correlation between the random stimulus and the internalstates of the neuron are calculated for the FHN neuron.

Autonomous learning of nonlocal stochastic neuron dynamics