Volatile Network as a Simple Memory Model

Rainer Willi Schulze

doi:doi:10.11648/j.ajmcm.20230801.12

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Volatile Network as a Simple Memory Model

Rainer Willi Schulze

Published in American Journal of Mathematical and Computer Modelling (Volume 8, Issue 1)

Received: 10 February 2023 Accepted: 27 February 2023 Published: 22 May 2023

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Abstract

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.

Published in	American Journal of Mathematical and Computer Modelling (Volume 8, Issue 1)
DOI	10.11648/j.ajmcm.20230801.12
Page(s)	6-16
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

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

References

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[3]	Gerstner, W.; Kistler, W. et al.: Neuronal dynamics: from single neurons to networks and models of cognition. Cambridge: Cambridge University Press, 2014.
[4]	Nötzel, M.; Hermann, A. et al.: Measuring physical properties of living neurons: a novel Approach to study neurodegeneration. Klinik und Poliklinik für Neurologie Dresden, 2021.
[5]	Sperry R. W.: Cerebral organization and behavior. Science 133 (1961), S. 1749–1757.
[6]	Morbus Alzheimer – Mitochondrien in Zellen blockiert Nach einer Mitteilung der Albert-Ludwigs-Universität Freiburg Aus: Fortschritte Neurologie Psychiatrie 2014.
[7]	Ozansoy, M; Başak, A.: A Tauopathies: A Distinct Class of Neurodegenerative Diseases. Walter de Gruyter GmbH, 2007.
[8]	Eva-Maria und Eckhard Mandelkow: 2013 Khalid Iqbal Lifetime Achievement Award. Alzheimer's Association International Conference (AAIC 2013) Boston (USA).
[9]	Debnath, L; Bhatta, D: Integral transforms and their applications. Chapman & Hall/CRC, 2007 ISBN 1584885750; 9781584885757.
[10]	Jitsev, E.: On the self-organization of a hierarchical memory for compositional object representation in the visual cortex. Publication Server of Goethe University Frankfurt am Main, 2011.
[11]	Jockel, S.: Crossmodally Learning and Prediction of Autobiographical Episodic Experiences using a Sparse Distributed Memory. Staats- und Universitätsbibliothek Hamburg Carl von Ossietzky, 2009.
[12]	Hainmueller, T.; Bartos M.: The hippocampus converts dynamic entorhinal inputs into stable spatial maps Institute for Physiology I, University of Freiburg, Medical Faculty, Freiburg.
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[15]	Riley, K. F.; Hobson, M. P. et al.: Mathematical Methods for Physics and Engineering. Cambridge University Press ISBN 978-0-521-86153-3.
[16]	Haußmann, R.; Bauer, M. et al.: Prädikation von Neurodegenration in der multidimensionalen Demenzdiagnostik. Klinik und Poliklinik für Psychiatrie und Psychotherapie Dresden, Klinik und Poliklinik für Psychiatrie und Psychotherapie Dresden, 2020.

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Rainer Willi Schulze. (2023). Volatile Network as a Simple Memory Model. American Journal of Mathematical and Computer Modelling, 8(1), 6-16. https://doi.org/10.11648/j.ajmcm.20230801.12

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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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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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@article{10.11648/j.ajmcm.20230801.12,
  author = {Rainer Willi Schulze},
  title = {Volatile Network as a Simple Memory Model},
  journal = {American Journal of Mathematical and Computer Modelling},
  volume = {8},
  number = {1},
  pages = {6-16},
  doi = {10.11648/j.ajmcm.20230801.12},
  url = {https://doi.org/10.11648/j.ajmcm.20230801.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajmcm.20230801.12},
  abstract = {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.},
 year = {2023}
}

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TY  - JOUR
T1  - Volatile Network as a Simple Memory Model
AU  - Rainer Willi Schulze
Y1  - 2023/05/22
PY  - 2023
N1  - https://doi.org/10.11648/j.ajmcm.20230801.12
DO  - 10.11648/j.ajmcm.20230801.12
T2  - American Journal of Mathematical and Computer Modelling
JF  - American Journal of Mathematical and Computer Modelling
JO  - American Journal of Mathematical and Computer Modelling
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EP  - 16
PB  - Science Publishing Group
SN  - 2578-8280
UR  - https://doi.org/10.11648/j.ajmcm.20230801.12
AB  - 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.
VL  - 8
IS  - 1
ER  -

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Author Information

Rainer Willi Schulze

Faculty of Computer Science, Dresden University of Technology, Dresden, Germany

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Plain Text BibTeX RIS

APA Style

Rainer Willi Schulze. (2023). Volatile Network as a Simple Memory Model. American Journal of Mathematical and Computer Modelling, 8(1), 6-16. https://doi.org/10.11648/j.ajmcm.20230801.12

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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

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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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@article{10.11648/j.ajmcm.20230801.12,
  author = {Rainer Willi Schulze},
  title = {Volatile Network as a Simple Memory Model},
  journal = {American Journal of Mathematical and Computer Modelling},
  volume = {8},
  number = {1},
  pages = {6-16},
  doi = {10.11648/j.ajmcm.20230801.12},
  url = {https://doi.org/10.11648/j.ajmcm.20230801.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajmcm.20230801.12},
  abstract = {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.},
 year = {2023}
}

Copy | Download

TY  - JOUR
T1  - Volatile Network as a Simple Memory Model
AU  - Rainer Willi Schulze
Y1  - 2023/05/22
PY  - 2023
N1  - https://doi.org/10.11648/j.ajmcm.20230801.12
DO  - 10.11648/j.ajmcm.20230801.12
T2  - American Journal of Mathematical and Computer Modelling
JF  - American Journal of Mathematical and Computer Modelling
JO  - American Journal of Mathematical and Computer Modelling
SP  - 6
EP  - 16
PB  - Science Publishing Group
SN  - 2578-8280
UR  - https://doi.org/10.11648/j.ajmcm.20230801.12
AB  - 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.
VL  - 8
IS  - 1
ER  -

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