Abstract
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.
Original language | English (US) |
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Pages (from-to) | 1720-1738 |
Number of pages | 19 |
Journal | Neural Computation |
Volume | 19 |
Issue number | 7 |
DOIs | |
State | Published - Jul 2007 |
ASJC Scopus subject areas
- Arts and Humanities (miscellaneous)
- Cognitive Neuroscience