288 pp. per issue
6 x 9, illustrated
2014 Impact factor:

Neural Computation

July 2007, Vol. 19, No. 7, Pages 1720-1738
(doi: 10.1162/neco.2007.19.7.1720)
© 2007 Massachusetts Institute of Technology
Generation of Synthetic Spike Trains with Defined Pairwise Correlations
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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.