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.
Model Type: Connectionist Network
Cell Type(s): Abstract Izhikevich neuron; Abstract integrate-and-fire adaptive exponential (AdEx) neuron
Simulation Environment: MATLAB; Brian; Python
Implementer(s): Cohen, Michael X [mikexcohen at gmail.com]
References:
Cohen MX. (2014). Fluctuations in oscillation frequency control spike timing and coordinate neural networks. The Journal of neuroscience : the official journal of the Society for Neuroscience. 34 [PubMed]