Abstract
Recently it was demonstrated that the activity of frontal cortical neurons in the awake behaving monkey comprises excessive occurrences of highly accurate (~1–3 ms) spatio-temporal firing patterns. Moreover, these patterns can be related to the behavioral state of the animal [l, 10]. On the basis of the characteristic anatomy and physiology of the cortex, it was proposed that syn fire activity, propagating through the sparsely firing cortical neural network, presents a natural explanation for this phenomenon [2, 1]. In order to test this hypothesis, we investigated the dependence of reliable synfire propagation on the structural and the dynamical properties of a model cortical network, using the newly developed simulation tool SYNOD [6].
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Gewaltig, MO., Diesmann, M., Aertsen, A. (1995). Propagation of Synfire Activity in Cortical Networks: a Statistical Approach. In: Kappen, B., Gielen, S. (eds) Neural Networks: Artificial Intelligence and Industrial Applications. Springer, London. https://doi.org/10.1007/978-1-4471-3087-1_6
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DOI: https://doi.org/10.1007/978-1-4471-3087-1_6
Publisher Name: Springer, London
Print ISBN: 978-3-540-19992-2
Online ISBN: 978-1-4471-3087-1
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