A pattern grouping algorithm for analysis of spatiotemporal patterns in neuronal spike trains. 2. Application to simultaneous single unit recordings

This study demonstrates the practical application of the pattern grouping a lgorithm (PGA), presented in the companion paper (Tetko IV, Villa AEP. A pa ttern grouping algorithm for analysis of spatiotemporal patterns in neurona l spike trains. 1. Detection of repeated patterns. J. Neurosci. Methods 200 0; accompanying article), to data sets including np to 30 simultaneously re corded spike trains. The analysis of a large network of simulated neurons s hows that the incidence of patterns cannot be simply related to an increase in firing rates obtained after Hebbian learning. Patterns that disappeared and reappeared in the thalamus of anesthetized rats when the cerebral cort ex was reversibly inactivated suggest that widespread cell assemblies contr ibute to the generation and propagation of precisely timed activity. In an another experiment multiple spike trains were recorded from the temporal co t tex of freely moving rats performing a complex two-choice discrimination task. The presence or absence of particular patterns in the period precedin g the cue was associated with changes in reaction time. In conclusion, neur onal network interactions may generate spatiotemporal firing patterns detec table by PGA. We provide evidence of such patterned activity associated wit h specific animal's behavior, thus suggesting the existence of complex temp oral coding schemes in the higher nervous centers of the brain. (C) 2001 El sevier Science B.V. All rights reserved.