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 activities of neural networks with the number of elements N = 104 does not exceed 8.
Highlights
The idea of neural systems working as unification of many similar units (“hyper-columns”) exists in neuroscience for years (Horton and Adams, 2005)
In this paper we extend our work on attractor based neural networks
We have demonstrated that robustly functioning neural networks of N neurons can have M = k · N attractor states, where k = 1 ÷ 1000
Summary
The idea of neural systems working as unification of many similar units (“hyper-columns”) exists in neuroscience for years (Horton and Adams, 2005). The attractor for the neural network in which neural interconnections are made with molecular markers consists of M states, Sm, (m = 1, M) of activity of the network of N neurons.
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