A pattern grouping algorithm for analysis of spatiotemporal patterns in neuronal spike trains. 1. Detection of repeated patterns

The existence of precise temporal relations in sequences of spike intervals , referred to as 'spatiotemporal patterns', is suggested by brain theories that emphasize the role of temporal coding. Specific analytical methods abl e to assess the significance of such patterned activity are extremely impor tant to establish its function for information processing in the brain. Thi s study proposes a new method called 'pattern grouping algorithm' (PGA), de signed to identify and evaluate the statistical significance of patterns wh ich differ from each other by a defined and small jitter in spike timing of the order of few ms. The algorithm performs a pre-selection of template pa tterns with a fast computational approach, optimizes the jitter for each sp ike in the template and evaluates the statistical significance of the patte rn group using three complementary statistical approaches. Simulated data s ets characterized by various types of known non stationarities are used for validation of PGA and for comparison of its performance to other methods. Applications of PCA to experimental data sets of simultaneously recorded sp ike trains are described in a companion paper (Tetko IV, Villa AEP. A patte rn grouping algorithm for analysis of spatiotemporal patterns in neuronal s pike trains. 2. Application to simultaneous single unit recordings. J Neuro sci Methods 2000; accompanying article). (C) 2001 Elsevier Science B.V. All rights reserved.