Nonlinear Time Series Analysis of Spike Data of Izhikevich Neuron Model
- 21 October 2020
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 203-204
Abstract
It is well known that burst patterns of neuronal networks may play an important role in information processing in the brain. We consider that it is advantageous to construct a model using mathematical neuronal models producing burst patterns, because it is such models are easier to study and more accessible as compared to real biological neuronal data. In this study, we use the Izhikevich neuron model to produce burst patterns and apply a recurrence plot density entropy to the Izhikevich neuron data.Keywords
This publication has 5 references indexed in Scilit:
- Nonlinear time-series analysis revisitedChaos: An Interdisciplinary Journal of Nonlinear Science, 2015
- Revealing neuronal function through microelectrode array recordingsFrontiers in Neuroscience, 2015
- Parameters for burst detectionFrontiers in Computational Neuroscience, 2014
- Fast monitoring of epileptic seizures using recurrence time statistics of electroencephalographyFrontiers in Computational Neuroscience, 2013
- Exploiting Nonlinear Recurrence and Fractal Scaling Properties for Voice Disorder DetectionBioMedical Engineering OnLine, 2007