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

Frontiers in Systems Neuroscience
Ksenia P SolovyevaWitali L Dunin-Barkowski

Abstract

In this work we reveal and explore a new class of attractor neural networks, based on inborn connections provided by model molecular markers, the molecular marker based attractor neural networks (MMBANN). Each set of markers has a metric, which is used to make connections between neurons containing the markers. We have explored conditions for the existence of attractor states, critical relations between their parameters and the spectrum of single neuron models, which can implement the MMBANN. Besides, we describe functional models (perceptron and SOM), which obtain significant advantages over the traditional implementation of these models, while using MMBANN. In particular, a perceptron, based on MMBANN, gets specificity gain in orders of error probabilities values, MMBANN SOM obtains real neurophysiological meaning, the number of possible grandma cells increases 1000-fold with MMBANN. MMBANN have sets of attractor states, which can serve as finite grids for representation of variables in computations. These grids may show dimensions of d = 0, 1, 2,…. We work with static and dynamic attractor neural networks of the dimensions d = 0 and 1. We also argue that the number of dimensions which can be represented by attractors of acti...Continue Reading

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Citations

Mar 24, 2016·Molecular Pharmaceutics·Polina MamoshinaAlex Zhavoronkov
Mar 28, 2019·Human Genetics·Hua ZhouKenneth Lange
Jan 25, 2019·PLoS Computational Biology·Dayanne M CastroRichard Bonneau
Jul 10, 2019·PLoS Computational Biology·Elyse H NortonMichael S Landy
Jul 16, 2020·Neuropsychopharmacology : Official Publication of the American College of Neuropsychopharmacology·Georgia KoppeDaniel Durstewitz
May 18, 2018·Proceedings of the VLDB Endowment·Alexander RatnerChristopher Ré
Mar 7, 2017·Frontiers in Behavioral Neuroscience·Kyobi S Kakaria, Benjamin L de Bivort

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Software Mentioned

SOM

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