Input strength and time-varying oscillation peak frequency (Cohen MX 2014)

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The purpose of this paper is to argue that a single neural functional principle—temporal fluctuations in oscillation peak frequency (“frequency sliding”)—can be used as a common analysis approach to bridge multiple scales within neuroscience. The code provided here recreates the network models used to demonstrate changes in peak oscillation frequency as a function of static and time-varying input strength, and also shows how correlated frequency sliding can be used to identify functional connectivity between two networks.
1 . Cohen MX (2014) Fluctuations in oscillation frequency control spike timing and coordinate neural networks Journal of Neuroscience 34(27):8988-8998
Model Information (Click on a link to find other models with that property)
Model Type: Connectionist Network;
Brain Region(s)/Organism:
Cell Type(s): Abstract Izhikevich neuron; Abstract integrate-and-fire adaptive exponential (AdEx) neuron;
Gap Junctions:
Simulation Environment: MATLAB; Brian; Python;
Model Concept(s):
Implementer(s): Cohen, Michael X [mikexcohen at];
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