UNCOVERING THE SYNCHRONIZATION DYNAMICS FROM CORRELATED NEURONAL-ACTIVITY QUANTIFIES ASSEMBLY FORMATION

Synchronous network excitation is believed to play an outstanding role in neuronal information processing. Due to the stochastic nature of t he contributing neurons, however, those synchronized states are diffic ult to detect in electrode recordings. We present a framework and a mo del for the identification of such network states and of their dynamic s in a specific experimental situation. Our approach operationalizes t he notion of neuronal groups forming assemblies via synchronization ba sed on experimentally obtained spike trains. The dynamics of such grou ps is reflected in the sequence of synchronized states, which we descr ibe as a renewal dynamics. We furthermore introduce a rate function wh ich is dependent on the internal network phase that quantifies the act ivity of neurons contributing to the observed spike train. This consti tutes a hidden state model which is formally equivalent to a hidden Ma rkov model, and all its parameters can be accurately determined from t he experimental time series using the Baum-Welch algorithm. We apply o ur method to recordings from the cat visual cortex which exhibit oscil lations and synchronizations. The parameters obtained for the hidden s tate model uncover characteristic properties of the system including s ynchronization, oscillation, switching, background activity and correl ations. In applications involving multielectrode recordings, the extra cted models quantify the extent of assembly formation and can be used for a temporally precise localization of system states underlying a sp ecific spike train.