Techniques: Computer networking capacity in robotic neural systems

Authors:
Carol Niznik;Robert Newcomb
Affiliations:
Center for Brain Research, Univeristy of Rochester, School of Medicine, 601 Elmwood Avenue, Rochester, NY 14642, USA;Microsystems Laboratory, Electrical Engineering Department, University of Maryland, College Park, MD 20742, USA
Venue:
Computer Communications
Year:
1984

Citing 3
Cited 0

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Abstract

This paper illustrates the mathematical and philosophical link between capacity measures developed in the mathematical theory of capacity, computer networking and robotic neural systems. The work of classical authors is founded upon concepts associated with physical circuit capacitors and Fourier series singularity points, and is developed through mathematical works originating with Gauss and Wiener. Three such mathematical measures of capacity are used here as a theoretical base: Newtonian capacity, Hausdorff measure capacity and analytic capacity. The linking of circuit and computer networking capacity measures, which equates to the capacity of robotic neural systems, is discussed by this means in two contexts, one in terms of the frequency of action potentials, and the other in terms of the frequency of bursts of action potentials in neural circuits. An all-or-none path model is also illustrated to indicate the position of coding in the robotic neural system.