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

A spontaneously active neural system that is capable of continual learningshould also be capable of homeostasis of both firing rate and connectivity.Experimental evidence suggests that both types of homeostasis exist, and thatconnectivity is maintained at a state that is optimal for informationtransmission and storage. This state is referred to as the critical state. Wepresent a simple stochastic computational Hebbian learning model thatincorporates both firing rate and critical homeostasis, and we explore itsstability and connectivity properties. We also examine the behavior of ourmodel with a simulated seizure and with simulated acute deafferentation. Weargue that a neural system that is more highly connected than the criticalstate (i.e., one that is "supercritical") is epileptogenic. Based on oursimulations, we predict that the post-seizural and post-deafferentation statesshould be supercritical and epileptogenic. Furthermore, interventions thatboost spontaneous activity should be protective against epileptogenesis.

A simple spontaneously active Hebbian learning model: homeostasis of activity and connectivity, and consequences for learning and epileptogenesis