Summary
Some extensions of the theory of adapting coincidence scaling are presented in the context of neural theory and modelling.
Previously the theory of adapting coincidence scaling has been successfully applied to quite a number of specific problems mainly drawn from psychophysical theories of vision: van de Grind et al. (1970a, b); Koenderink et al. (1970a, b). Here emphasis is on neurophysiological problems and after a brief discussion of the “coding” and “component” problems of neural network modelling and a survey of basic coincidence scaling mechanisms a paradigm for neural encoding is treated in some detail. This paradigm (Fig. 6A) is similar to the neuromimes developed and studied by Harmon (1959, 1961) and Küpfmüller and Jenik (1961) for deterministic input signals. On the basis of the introductory discussion of the coding problem it is assumed that the neural code in the peripheral part of the nervous system that we choose as our hunting ground, viz. the retina, is an average event rate code with a Poisson point process as a carrier. Thus the paradigm for neural encoding is studied for such a stochastic input point process. It is then among other things shown that such a simple encoder can generate a wide variety of multimodal interval distributions for certain choices of its parameters. Next we turn to a classic coincidence model of vision and give extremely accurate simulation results to substitute for the lacking analytic solution of the underlying K-fold coincidence problem.
A shortcoming of this model is analysed in terms of elementary neural operations and it is shown that the problem of specifying a generalized version of the model ties in with the problem of developing models to explain the quantal signals (bumps) observed on the generator potential during intracellular recordings from the eccentric cell of Limulus. A cybernetic principle for “bump” size adaptation is formulated on the basis of the apparent and possibly significant similarity of this adaptation process with the event rate reduction principle embodied in the so called V R-machine (van de Grind et al., 1970a) which is one of our set of adapting coincidence scalers.
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van de Grind, W.A., Koenderink, J.J., van der Heyde, G.L. et al. Adapting coincidence scalers and neural modelling studies of vision. Kybernetik 8, 85–105 (1971). https://doi.org/10.1007/BF00272290
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DOI: https://doi.org/10.1007/BF00272290