Noise-induced firing patterns in generalized neuron model with subthreshold oscillations
| Autor: | Y. Trenikhina, Dmitry E. Postnov, R. Zhirin, Ludmila S. Ryazanova |
|---|---|
| Rok vydání: | 2007 |
| Předmět: |
Membrane potential
Quantitative Biology::Neurons and Cognition Mathematical model business.industry Noise induced Computer science Subthreshold conduction Biological neuron model Stimulus (physiology) Nonlinear system Bursting medicine.anatomical_structure medicine Artificial intelligence Neuron Subthreshold oscillations business Biological system |
| Zdroj: | Complex Dynamics and Fluctuations in Biomedical Photonics IV. |
| ISSN: | 0277-786X |
| DOI: | 10.1117/12.714241 |
| Popis: | Subthreshold oscillations can be found in different neural systems. Some mathematical models of bursting neurons also manifest slow oscillations that are more or less independent from fast spiking process and becomes subthreshold when spiking subsystem is set in excitable regime. Because neural activity is known to be heavily influenced by a variety of noisy processes, it is important to understand how the subthreshold oscillations can change the response of neural system on noisy stimulus. It is typically assumed that generation of spike does not affect slow subsystem. However, such one-way connection between slow and fast subsystems is not the case for many neural models where fast and slow ionic currents share the same equation for transmembrane potential (for example, well known Huber-Braun model). Definitely, the generation of fast action potential can affect the slow ionic currents. Thus, being excited by noise, such neural system could show different firing patterns depending on how slow subsystem is affected by the fast one. To address this problem we propose the generalized model consisting of two FitzHugh-Nagumo systems that are set in different operating regimes and thus play the role of fast excitable and slow self-sustained subsystems. With this model, we study how the noise-induced firing patterns depend on different variants of fast-to-slow coupling between subsystems. The corresponding changes in ISI distribution as well as underlying nonlinear mechanisms are discussed. |
| Databáze: | OpenAIRE |
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