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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

It is widely appreciated that well-balanced excitation and inhibition arenecessary for proper function in neural networks. However, in principle, suchbalance could be achieved by many possible configurations of excitatory andinhibitory strengths, and relative numbers of excitatory and inhibitoryneurons. For instance, a given level of excitation could be balanced by eithernumerous inhibitory neurons with weak synapses, or few inhibitory neurons withstrong synapses. Among the continuum of different but balanced configurations,why should any particular configuration be favored? Here we address thisquestion in the context of the entropy of network dynamics by studying ananalytically tractable network of binary neurons. We find that entropy ishighest at the boundary between excitation-dominant and inhibition-dominantregimes. Entropy also varies along this boundary with a trade-off between highand robust entropy: weak synapse strengths yield high network entropy which isfragile to parameter variations, while strong synapse strengths yield a lower,but more robust, network entropy. In the case where inhibitory and excitatorysynapses are constrained to have similar strength, we find that a small, butnon-zero fraction of inhibitory neurons, like that seen in mammalian cortex,results in robust and relatively high entropy.

Robust entropy requires strong and balanced excitatory and inhibitory synapses