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

In this work we reveal and explore a new class of attractor neural networks,based on inborn connections provided by model molecular markers, the molecularmarker based attractor neural networks (MMBANN). We have explored conditionsfor the existence of attractor states, critical relations between theirparameters and the spectrum of single neuron models, which can implement theMMBANN. Besides, we describe functional models (perceptron and SOM) whichobtain significant advantages, while using MMBANN. In particular, theperceptron based on MMBANN, gets specificity gain in orders of errorprobabilities values, MMBANN SOM obtains real neurophysiological meaning, thenumber of possible grandma cells increases 1000- fold with MMBANN. Each set ofmarkers has a metric, which is used to make connections between neuronscontaining the markers. The resulting neural networks have sets of attractorstates, which can serve as finite grids for representation of variables incomputations. These grids may show dimensions of d = 0, 1, 2,... We work withstatic and dynamic attractor neural networks of dimensions d = 0 and d = 1. Wealso argue that the number of dimensions which can be represented by attractorsof activities of neural networks with the number of elements N=104 does notexceed 8.

Models of Innate Neural Attractors and Their Applications for Neural Information Processing