Mechanisms of self-sustained oscillatory states in hierarchical modular networks with mixtures of electrophysiological cell types
by Petar Tomov, Rodrigo F. O. Pena, Antonio C. Roque, Michael A. Zaks
Year:
2016
Publication:
Front. Comput. Neurosci., Volume 10
Abstract:
In a network with a mixture of different electrophysiological types of neurons linked by excitatory and inhibitory connections, temporal evolution leads through repeated epochs of intensive global activity separated by intervals with low activity level. This behavior mimics “up” and “down” states, experimentally observed in cortical tissues in absence of external stimuli. We interpret global dynamical features in terms of individual dynamics of the neurons.
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Brief introduction of the dida co-author(s) and relevance for dida's ML developments.
About the Co-Author
After his studies (LMU München) and PhD (HU Berlin) in theoretical physics, Petar worked for several years as an IT consultant with projects at different DAX companies. In the last years he developed his passion for machine learning and specialized in this field. Petar is supporting the machine learning team as a developer and project manager.