Abstract
We present a novel approach to modeling neural behavior using a “qualitative reasoning” algorithm. The Qualitative Reasoning Neuron (QRN) is capable of qualitatively reproducing single neuron behavior, but is computationally simple enough to use in large scale neural networks without loss of critical details. QRN simulations of a single Purkinje cell (∼1600 compartments) show significant speedup over a recent GENESIS model. A large scale model of the cerebellar cortex (256 neurons, ∼300,000 compartments) is used to simulate a saccadic eye movement task. The model reproduces in vivo Purkinje cell bursting patterns during saccades. We simulate rapid and gradual adaptation paradigms and show that error correction is possible when climbing fiber input is periodic and contains no error signal.
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Krichmar, J.L., Ascoli, G.A., Hunter, L., Olds, J.L. (1997). A model of cerebellar saccadic motor learning using qualitative reasoning. In: Mira, J., Moreno-Díaz, R., Cabestany, J. (eds) Biological and Artificial Computation: From Neuroscience to Technology. IWANN 1997. Lecture Notes in Computer Science, vol 1240. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0032471
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DOI: https://doi.org/10.1007/BFb0032471
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