Robustness and versatility of a nonlinear interdependence method for directional coupling detection from spike trains
The detection of directional couplings between dynamics based onmeasured spike trains is a crucial problem in the understanding of many different systems. In particular, in neuroscience it is important to assess the connectivity between neurons.One of the approaches that can estimate directional cou...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2017 |
| País: | España |
| Institución: | Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
| Repositorio: | Recercat. Dipósit de la Recerca de Catalunya |
| OAI Identifier: | oai:recercat.cat:10230/32713 |
| Acceso en línea: | http://hdl.handle.net/10230/32713 http://dx.doi.org/10.1103/PhysRevE.96.022203 |
| Access Level: | acceso abierto |
| Palabra clave: | Coupled oscillators Synchronization Chaotic systems Dynamical systems Neuronal network models Time series analysis Interdisciplinary physics Networks Nonlinear dynamics |
| Sumario: | The detection of directional couplings between dynamics based onmeasured spike trains is a crucial problem in the understanding of many different systems. In particular, in neuroscience it is important to assess the connectivity between neurons.One of the approaches that can estimate directional coupling from the analysis of point processes is the nonlinear interdependence measure L. Although its efficacy has already been demonstrated, it still needs to be tested under more challenging and realistic conditions prior to an application to real data. Thus, in this paper we use the Hindmarsh-Rose model system to test the method in the presence of noise and for different spiking regimes.We also examine the influence of different parameters and spike train distances. Our results show that the measure L is versatile and robust to various types of noise, and thus suitable for application to experimental data. |
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