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Kinetic approach to neural systems: I

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Abstract

A kinetic model of neural systems is introduced and discussed with statistical mechanics techniques. It is assumed that, for a macroscopic description of the model, it suffices to consider only the distribution for the velocity and position of the impulses, and the distribution for the excitation and position of the neurons, at any timet. Making use of Boltzmann's method for the study of a dilute gas, coupled differential equations for the rate of change with time of the distributions have been constructed.

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Literature

  • Accardi, L. and A. Aiello. 1972. “Some Global Properties of Neural Networks”.Kybernetik,10, 115–119.

    Article  MATH  MathSciNet  Google Scholar 

  • Adrian, E. D. and B. H. C. Matthews. 1934. “The Interpretation of Potential Waves in the Cortex”.J. Physiol. London,81, 440–471.

    Google Scholar 

  • —. 1936. “The Spread of Activity in the Cerebral Cortex”.,88, 127–161.

    Google Scholar 

  • Caianiello, E. R. 1961. “Outline of a Theory of Thought Processes and Thinking Machines”.J. Theor. Biology,2, 204–235.

    Article  MathSciNet  Google Scholar 

  • —, A. de Luca and L. M. Ricciardi. 1967. “Reverberation and Control of Neural Networks”.Kybernetik.4, 10–18.

    Article  Google Scholar 

  • Chapman, S. and T. G. Cowling. 1970.The Mathematical Theory of Non-Uniform Gases. Cambridge: The University Press.

    Google Scholar 

  • Cohen, E. G. D. 1969. “The Kinetic Theory of Dilute Gases”. InTransport Phenomena in Fluids, Hanley, H. J. M., ed. New York: Marcel Dekker.

    Google Scholar 

  • Elul, R. 1968. “Brain Waves: Intracellular Recording and Statistical Analysis Help Clarify their Physiological Significance”. InData Acquisition and Processing in Biology and Medicine. Proceedings of the 1966 Rochester Conference. Oxford: Pergamon Press, Vol. 5.

    Google Scholar 

  • Griffith, J. S. 1963. “On the Stability of Brain-like Structures”.Biophysics J.,3, 299–308.

    MathSciNet  Google Scholar 

  • Rapoport, A. 1950. “Contribution to the Probabilistic Theory of Neural Nets: I. Randomization of Refractory Periods and of Stimulus Intervals”Bull. Math. Biophysics,12, 109–121.

    MathSciNet  Google Scholar 

  • Ricciardi, L. M. and H. Umezawa. 1967. “Brain and Physics of Many-Body Problems”.Kybernetik,4, 44–48.

    Article  Google Scholar 

  • — and F. Ventriglia. 1970. “Probabilistic Models for Determining the Input-Output Relationship in Formalized Neurons: I. A Theoretical Approach”.,7, 175–183.

    Article  Google Scholar 

  • Smith, D. R. and C. H. Davidson. 1961. “Maintained, Activity in Neural Nets”.J. Assoc. Comp. Mach.,9, 268–279.

    MathSciNet  Google Scholar 

  • Sugiyama, H., G. P. Moore and D. H. Perkel. 1970. “Solutions for a Stochastic Model of Neuronal Spike Production”.Math. Biosciences,8, 323–341.

    Article  MATH  Google Scholar 

  • Wilson, H. R. and J. D. Cowan. 1972. “Excitatory and Inhibitory Interactions in Localized Populations of Model Neurons”.Biophysics J.,12, 1–24.

    Article  Google Scholar 

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Ventriglia, F. Kinetic approach to neural systems: I. Bltn Mathcal Biology 36, 535–544 (1974). https://doi.org/10.1007/BF02463265

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  • DOI: https://doi.org/10.1007/BF02463265

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