Abstract
We study the proposed statistical kinetic model for describing the pre- and consciousness structures based on the cognitive neural networks. The method of statistics of the growth graph systems and a possible transition to symmetric structures (a kind of phase transition) is applied. With the complication of a random Erdőos-Rényi (ER) graph during the percolation transition from the tree structure to the large cluster structures is obtained. In the evolutionary model two classes of algorithms have been developed. The differences between the cycle parameters in the obtained neural network models can reach thousands or more times. This is due to the tree-like architecture of the neural graph, which mimics the columnar structures of the neocortex. These cluster and cyclic structures can be interpreted as the primary elements of consciousness and as a necessary condition for the effect of consciousness itself. The comparison with other known theoretical mainly statistical models of consciousness is discussed. The presented results are promising in neurocomputer interfaces, man-machine systems and artificial intelligence systems.
Highlights
IntroductionThere are opinions, expressed e.g., in [1], about the difficulties of the reduction theory in treating consciousness and the possibility to use the phenomenological approach
The purpose of this work is to describe the initial model of neural systems based on kinetic-statistical methods
The question arises: how should the principles of plasticity be organized for the transition from a chaotic state of neurons to states of a given function and to more symmetrical structures? The authors provide a partial answer to this question in this paper, taking into account an analogy with the phase transition of the second type
Summary
There are opinions, expressed e.g., in [1], about the difficulties of the reduction theory in treating consciousness and the possibility to use the phenomenological approach. In contrast to these statements, we intend to develop a reductive constructive approach, believing that consciousness can manifest itself as a result of the transition of the level of complexity of the system through a certain threshold. Learning should be local and based on the neuron and nearest neighbors. In this regard, the question arises: how should the principles of plasticity be organized for the transition from a chaotic state of neurons to states of a given function and to more symmetrical structures? The question arises: how should the principles of plasticity be organized for the transition from a chaotic state of neurons to states of a given function and to more symmetrical structures? The authors provide a partial answer to this question in this paper, taking into account an analogy with the phase transition of the second type (as for the transition from paramagnetic to ferromagnetic)
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