Neural oscillations : two characterization algorithms and a biophysical network model
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Neural data has become increasingly accessible due to advances in recording techniques and computational capabilities. This abundance of neural data presents both a significant challenge and an essential endeavor: the systematic analysis of this data and the application of reverse engineering principles to reveal the brain's concealed mechanisms. Computational neuroscience plays a key role in this effort, aided by the rapid progress in statistical techniques and machine learning. Within this context, neural oscillations are of great importance. They coordinate neuron ensembles, facilitating communication and cognitive processes. This dissertation focuses on the analysis of electrophysiological data and computational models at the cellular and network levels. The three core chapters aim to address the following objectives: In chapter 2, a pipeline was developed to improve characterization of oscillatory bursts and synthesize surrogate data that facilitates the evaluation of detection algorithms from individual recordings. In chapter 3, a scheme using simulations and machine learning was developed to infer the properties of single neurons from in vivo extracellular recordings, enhancing the construction of realistic network models. In chapter 4, a biophysical realistic computational model was used to investigate how microcircuits in the motor cortex interact to generate beta and gamma oscillations.
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Ph. D.