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/

Zendy Plus

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

Zendy Tools

Zendy Open

Variability in neural responses is an ubiquitous phenomenon in neurons,usually modeled with stochastic differential equations. In particular,stochastic integrate-and-fire models are widely used to simplify theoreticalstudies. The statistical properties of the generated spikes depend on thestimulating input current. Given that real sensory neurons are driven bytime-dependent signals, here we study how the inter-spike interval distributionof integrate-and-fire neurons depends on the evolution of the stimulus, in aquasi-static limit. We obtain a closed-form expression for this distribution,and we compare it to the one obtained with numerical simulations for severaltime-dependent currents. For slow inputs, the quasi-static distributionprovides a very good description of the data. The results obtained for theintegrate-and-fire model can be extended to other non-autonomous stochasticsystems where the first passage time problem has an explicit solution.

Quasi-static approximation of the interspike interval distribution of neurons driven by time-dependent inputs