The Dynamics of Balanced Spiking Neuronal Networks Under Poisson Drive Is Not Chaotic
| Autor: | Gregor Kovačič, David Cai, Qinglong L. Gu, Zhong Qi K. Tian, Douglas Zhou |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
0301 basic medicine
chaotic dynamics Current (mathematics) irregular activity Neuroscience (miscellaneous) Chaotic Lyapunov exponent Type (model theory) Poisson distribution Topology lcsh:RC321-571 delta-pulse coupling 03 medical and health sciences Cellular and Molecular Neuroscience symbols.namesake 0302 clinical medicine largest Lyapunov exponent lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry Original Research Physics Quantitative Biology::Neurons and Cognition State (functional analysis) balanced state Coupling (probability) Range (mathematics) 030104 developmental biology symbols 030217 neurology & neurosurgery Neuroscience |
| Zdroj: | Frontiers in Computational Neuroscience Frontiers in Computational Neuroscience, Vol 12 (2018) |
| ISSN: | 1662-5188 |
| Popis: | Some previous studies have shown that chaotic dynamics in the balanced state, \emph{i.e.}, one with balanced excitatory and inhibitory inputs into cortical neurons, is the underlying mechanism for the irregularity of neural activity. In this work, we focus on networks of current-based integrate-and-fire neurons with delta-pulse coupling. While we show that the balanced state robustly persists in this system within a broad range of parameters, we mathematically prove that the largest Lyapunov exponent of this type of neuronal networks is negative. Therefore, the irregular firing activity can exist in the system without the chaotic dynamics. That is the irregularity of balanced neuronal networks need not arise from chaos. |
| Databáze: | OpenAIRE |
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