Generation of synthetic spike trains with defined pairwise correlations.
Recent technological advances as well as progress in theoretical understanding of neural systems have created a need for synthetic spike trains with controlled mean rate and pairwise cross-correlation. This report introduces and analyzes a novel algorithm for the generation of discretized spike trains with arbitrary mean rates and controlled cross correlation. Pairs of spike trains with any pairwise correlation can be generated, and higher-order correlations are compatible with common synaptic input. Relations between allowable mean rates and correlations within a population are discussed. The algorithm is highly efficient, its complexity increasing linearly with the number of spike trains generated and therefore inversely with the number of cross-correlated pairs.
Niebur
Ernst
E
Krieger Mind/Brain Institute and Department of Neuroscience, Johns Hopkins University, Baltimore, MD 21218, USA. niebur@jhu.edu
D000200:Action Potentials / Q000502:physiology*; D000465:Algorithms; D000818:Animals; D006801:Humans; D008959:Models, Neurological; D009474:Neurons / Q000502:physiology*; D013269:Stochastic Processes; D013569:Synapses / Q000502:physiology