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Volatile Network as a Simple Memory Model

Technical information systems, from PCs to supercomputers, are characterized over time by ever-increasing storage capacities, while biological systems are permanently characterized by their trainable memory abilities. Although both systems are not comparable with each other, because they are based on different phenomena, the existing efficiency of biological systems offers a constant borrowing for the further development of technical systems. For this purpose, it is necessary to develop technical equivalence models. The following considerations aim to reproduce the factually limitless abilities of biological systems to store memory content as a result of the plasticity of neuronal populations. The difference between technical and biological systems becomes particularly clear under this aspect: while the development of technical systems aims to permanently increase the existing storage capacity, biological systems are based on independently separating relevant from irrelevant information and, moreover, permanently reorienting existing memory structures, called plasticity. Accordingly, the transmitter flow between the neurons constantly changes in direction and intensity. A network with a transient topology that is marginally able to model a memory-capable neuronal population characterized by a permanent loss of neuronal contact points is proposed for discussion. Such a loss permanently changes the direction and intensity of the transmitter flow between the neurons. Another focus of the topic is the question of how different stimuli, meaning optical, acoustic, tactile, etc., can become one and the same memory description of a neuron population. Here it is assumed that a pre-processing takes place in the biological system in the form of a functional transformation, the result of which is a neutral basis for representing the information. Although such an assumption seems to be highly speculative, a discussion of it would contribute to answering the question of which physiological mechanisms have to be taken into account to explain memory phenomena, reproduced in a model.

Engrams, Memory Structure, Observation Space, Memory Stimulator, Degeneration, Fokker-Planck Equation, Jacobian Matrix

APA Style

Rainer Willi Schulze. (2023). Volatile Network as a Simple Memory Model. American Journal of Mathematical and Computer Modelling, 8(1), 6-16.

ACS Style

Rainer Willi Schulze. Volatile Network as a Simple Memory Model. Am. J. Math. Comput. Model. 2023, 8(1), 6-16. doi: 10.11648/j.ajmcm.20230801.12

AMA Style

Rainer Willi Schulze. Volatile Network as a Simple Memory Model. Am J Math Comput Model. 2023;8(1):6-16. doi: 10.11648/j.ajmcm.20230801.12

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This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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