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

The mammalian brain could contain dense and sparse network connectivitystructures, including both excitatory and inhibitory neurons, but is withoutany clearly defined output layer. The neurons have time constants, which meanthat the integrated network structure has state memory. The network structurecontains complex mutual interactions between the neurons under differentconditions, which depend on the internal state of the network. The internalstate can be defined as the distribution of activity across all individualneurons across the network. Therefore, the state of a neuron/network becomes adefining factor for how information is represented within the network. Towardsthis study, we constructed a fully connected (with dense/sparse codingstrategies) recurrent network comprising of both excitatory and inhibitoryneurons, driven by pseudo-random inputs of varying frequencies. In this studywe assessed the impact of varying specific intrinsic parameters of the neuronsthat enriched network state dynamics, such as initial neuron activity, amountof inhibition in combination with thresholded neurons and conduction delays.The impact was assessed by quantifying the changes in mutual interactionsbetween the neurons within the network for each given input. We found sucheffects were more profound in sparsely connected networks than in denselyconnected networks. However, also densely connected networks could make use ofsuch dynamic changes in the mutual interactions between neurons, as a giveninput could induce multiple different network states.

Rich dynamics caused by known biological brain network features resulting in stateful networks