Abstract
BackgroundNeural activation patterns proceed often by schemes or motifs distributed across the involved cortical networks. As neurons are correlated, the estimate of all possible dependencies quickly goes out of control. The complex nesting of different oscillation frequencies and their high non-stationariety further hamper any quantitative evaluation of spiking network activities. The problem is exacerbated by the intrinsic variability of neural patterns.Methodology/Principal FindingsOur technique introduces two important novelties and enables to insulate essential patterns on larger sets of spiking neurons and brain activity regimes. First, the sampling procedure over N units is based on a fixed spike number k in order to detect N-dimensional arrays (k-sequences), whose sum over all dimension is k. Then k-sequences variability is greatly reduced by a hierarchical separative clustering, that assigns large amounts of distinct k-sequences to few classes. Iterative separations are stopped when the dimension of each cluster comes to be smaller than a certain threshold. As threshold tuning critically impacts on the number of classes extracted, we developed an effective cost criterion to select the shortest possible description of our dataset. Finally we described three indexes (C,S,R) to evaluate the average pattern complexity, the structure of essential classes and their stability in time.Conclusions/SignificanceWe validated this algorithm with four kinds of surrogated activity, ranging from random to very regular patterned. Then we characterized a selection of ongoing activity recordings. By the S index we identified unstable, moderatly and strongly stable patterns while by the C and the R indices we evidenced their non-random structure. Our algorithm seems able to extract interesting and non-trivial spatial dynamics from multisource neuronal recordings of ongoing and potentially stimulated activity. Combined with time-frequency analysis of LFPs could provide a powerful multiscale approach linking population oscillations with multisite discharge patterns.
Highlights
In the last twenty years, studies on information encoding in the nervous system have provided fundamental insights into the nature of neural inner dynamics and of sensorimotor representation and coding of the external world
In this paper we present an original method to extract and characterize sequences with fixed numbers of spikes and distributed on multisite sources in multiple electrode recordings
Being the samples collected on the base of fixed spike counts, their occurrence is detected in a time-independent fashion k across the spiking sources
Summary
In the last twenty years, studies on information encoding in the nervous system have provided fundamental insights into the nature of neural inner dynamics and of sensorimotor representation and coding of the external world. Scant attention has been paid to other dynamic features such as spontaneous or ongoing activity. This feature represents 90% percent of the whole metabolic exertion of the brain [6]. An exhaustive description of ongoing activity as a kind of substrate intermingling with signals generated by external sources, could provide fundamental insights into nervous system dynamics. Spontaneous neuronal population activities from many sites in the central nervous system present complex combinations of different oscillation frequencies [7,8,9]. The complex nesting of different oscillation frequencies and their high non-stationariety further hamper any quantitative evaluation of spiking network activities. The problem is exacerbated by the intrinsic variability of neural patterns
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