Detection of directional interactions between neurons from spike trains
An important problem in neuroscience is the assessment of the connectivity between neurons from their spike trains. One recent approach developed for the detection of directional couplings between dynamics based on recorded point processes is the nonlinear interdependence measure L. In this thesis w...
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| Tipo de recurso: | tesis doctoral |
| Estado: | Versión publicada |
| Fecha de publicación: | 2019 |
| País: | España |
| Institución: | CBUC, CESCA |
| Repositorio: | TDR. Tesis Doctorales en Red |
| OAI Identifier: | oai:www.tdx.cat:10803/666226 |
| Acceso en línea: | http://hdl.handle.net/10803/666226 |
| Access Level: | acceso abierto |
| Palabra clave: | Spike trains Connectivity Nonlinear time series analysis Generalized synchronization Hindmarsh-Rose neurons Coupled oscillators Epilepsy Neuronal single-unit recordings Trens d’impulsos Connectivitat Anàlisi de sèries temporals no lineals Sincronització generalitzada Neurones Hindmarsh-Rose Oscilladors acoblats Epilèpsia Gravacions neuronals d’unitat única 62 |
| Sumario: | An important problem in neuroscience is the assessment of the connectivity between neurons from their spike trains. One recent approach developed for the detection of directional couplings between dynamics based on recorded point processes is the nonlinear interdependence measure L. In this thesis we first use the Hindmarsh-Rose model system to test L in the presence of noise and for different spiking regimes of the dynamics. We then compare the performance of L against the linear cross-correlogram and two spike train distances. Finally, we apply all measures to neuronal spiking data from an intracranial whole-night recording of a patient with epilepsy. When applied to simulated data, L proves to be versatile, robust and more sensitive than the linear measures. Instead, in the real data the linear measures find more connections than L, in particular for neurons in the same brain region and during slow wave sleep. |
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