Extraction and characterization of essential discharge patterns from multisite recordings of spiking ongoing activity

• Published in: PloS one 2009-01, Vol.4 (1), p.e4299-e4299
• Main Authors: Storchi, Riccardo, Biella, Gabriele E M, Liberati, Diego, Baselli, Giuseppe
• Format: Article
• Language: English
• Subjects: Action Potentials - physiology; Algorithms; Animals; Brain; Brain research; Cluster Analysis; Clustering; Complexity; Computational Biology/Computational Neuroscience; Computer Science; Computer Simulation; Consumer goods; Cortex; Discharge; Firing pattern; Frequency analysis; Frequency dependence; Mathematics/Algorithms; Mathematics/Statistics; Nervous system; Nervous System Diseases - physiopathology; Nesting; Neural networks; Neurons; Neuroscience/Theoretical Neuroscience; Noise; Oscillations; Physiology; Quantitative analysis; Rats; Sequences; Spatial distribution; Spiking; Structural stability; Time-frequency analysis; Trends; Variability
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0004299

Neural 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. Our 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. We 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.