Pattern filtering for detection of neural activity, with examples from HVc activity during sleep in zebra finches. Export

@article{citeulike:804433, abstract = {The detection of patterned spiking activity is important in the study of neural coding. A pattern filtering approach is developed for pattern detection under the framework of point processes, which offers flexibility in combining temporal details and firing rates. The detection combines multiple steps of filtering in a coarse-to-fine manner. Under some conditional Poisson assumptions on the spiking activity, each filtering step is equivalent to classifying by likelihood ratios all the data segments as targets or as background sequences. Unlike previous studies, where global surrogate data were used to evaluate the statistical significance of the detected patterns, a localized p-test procedure is developed, which better accounts for firing modulation and nonstationarity in spiking activity. Common temporal structures of patterned activity are learned using an entropy-based alignment procedure, without relying on metrics or pair-wise alignment. Applications of pattern filtering to single, presumptive interneurons recorded in the nucleus HVc of zebra finch are illustrated. These demonstrate a match between the auditory-evoked response to playback of the individual bird's own song and spontaneous activity during sleep. Small temporal compression or expansion, or both, is required for optimal matching of spontaneous patterns to stimulus-evoked activity.}, address = {Department of Statistics, Committee on Computational Neuroscience, University of Chicago, Chicago, IL 60637, USA. chi@galton.uchicago.edu}, author = {Chi, Z. and Rauske, P. L. and Margoliash, D.}, citeulike-article-id = {804433}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=944197}, citeulike-linkout-1 = {http://dx.doi.org/10.1162/089976603322362374}, citeulike-linkout-2 = {http://view.ncbi.nlm.nih.gov/pubmed/14511523}, citeulike-linkout-3 = {http://www.hubmed.org/display.cgi?uids=14511523}, doi = {10.1162/089976603322362374}, issn = {0899-7667}, journal = {Neural Comput}, keywords = {bird, birdsong, method, neurophysiology, reactivation}, month = {October}, number = {10}, pages = {2307--2337}, posted-at = {2006-08-17 21:14:29}, priority = {2}, title = {Pattern filtering for detection of neural activity, with examples from HVc activity during sleep in zebra finches.}, url = {http://dx.doi.org/10.1162/089976603322362374}, volume = {15}, year = {2003} }

TY - JOUR ID - citeulike:804433 L3 - citeulike-article-id:804433 N2 - The detection of patterned spiking activity is important in the study of neural coding. A pattern filtering approach is developed for pattern detection under the framework of point processes, which offers flexibility in combining temporal details and firing rates. The detection combines multiple steps of filtering in a coarse-to-fine manner. Under some conditional Poisson assumptions on the spiking activity, each filtering step is equivalent to classifying by likelihood ratios all the data segments as targets or as background sequences. Unlike previous studies, where global surrogate data were used to evaluate the statistical significance of the detected patterns, a localized p-test procedure is developed, which better accounts for firing modulation and nonstationarity in spiking activity. Common temporal structures of patterned activity are learned using an entropy-based alignment procedure, without relying on metrics or pair-wise alignment. Applications of pattern filtering to single, presumptive interneurons recorded in the nucleus HVc of zebra finch are illustrated. These demonstrate a match between the auditory-evoked response to playback of the individual bird's own song and spontaneous activity during sleep. Small temporal compression or expansion, or both, is required for optimal matching of spontaneous patterns to stimulus-evoked activity. IS - 10 JF - Neural Comput SN - 0899-7667 AD - Department of Statistics, Committee on Computational Neuroscience, University of Chicago, Chicago, IL 60637, USA. chi@galton.uchicago.edu EP - 2337 TI - Pattern filtering for detection of neural activity, with examples from HVc activity during sleep in zebra finches. VL - 15 SP - 2307 KW - bird KW - birdsong KW - method KW - neurophysiology KW - reactivation AU - Chi, Z AU - Rauske, PL AU - Margoliash, D PY - 2003/10// UR - http://dx.doi.org/10.1162/089976603322362374 ER -