Unitary events in multiple single-neuron spiking activity: II. Nonstationary data

In order to detect members of a functional group (cell assembly) in simulta neously recorded neuronal spiking activity, we adopted the widely used oper ational definition that membership in a common assembly is expressed in nea r-simultaneous spike activity. Unitary event analysis, a statistical method to detect the significant occurrence of coincident spiking activity in sta tionary data, was recently developed (see the companion article in this iss ue). The technique for the detection of unitary events is based on the assu mption that the underlying processes are stationary in time. This requireme nt, however, is usually not fulfilled in neuronal data. Here we describe a method that properly normalizes for changes of rate: the unitary events by moving window analysis (UEMWA). Analysis for unitary events is performed se parately in overlapping time segments by sliding a window of constant width along the data. In each window, stationarity is assumed. Performance and s ensitivity are demonstrated by use of simulated spike trains of independent ly firing neurons, into which coincident events are inserted. If cortical n eurons organize dynamically into functional groups, the occurrence of near- simultaneous spike activity should be time varying and related to behavior and stimuli. UEMWA also accounts for these potentially interesting nonstati onarities and allows locating them in time. The potential of the new method is illustrated by results from multiple single-unit recordings from fronta l and motor cortical areas in awake, behaving monkey.