Abstract
A model of noise-driven biological neocortical networks which is based directly on rat neocortical in vitro recordings is introduced. Assuming only binary interaction, the model shows how complex spiking behavior is generated through the noise and gives a full description of the emerging spiking patterns. Mathematical universality properties predict that these patterns are independent of the individual neurons. These properties furthermore imply the observability of these patterns in in vivo neocortical networks, in the form of a complex background activity on top of which the processing of “real” data is performed. Interspike interval histograms are derived from this model and compared with the corresponding experimental histograms. Considerable agreement is found, which is notably expressed by the fact that both distributions are long-tailed.
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© 1999 Springer-Verlag
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Stoop, R. (1999). Complex spiking behavior from noise-driven neuron interaction. In: Reguera, D., Vilar, J., Rubí, J. (eds) Statistical Mechanics of Biocomplexity. Lecture Notes in Physics, vol 527. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0105018
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DOI: https://doi.org/10.1007/BFb0105018
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