Non-parametric Change Point Detection for Spike Trains
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Two techniques of non-parametric change point detection are applied to two different neuroscience datasets. In the 鍖rst dataset, we show how the multivariate non-parametric change point detection can precisely estimate reaction times to input stimulation in the olfactory system using joint information of spike trains from several neurons. In the second example, we propose to analyze communication and sequence coding using change point formalism as a time segmentation of homogeneous pieces of information, revealing cues to elucidate directionality of the communication in electric 鍖sh. We are also sharing our software implementation Chapolins at GitHub.
Non-parametric Change Point Detection for Spike Trains
- 1. Non-parametric change point detection for spike trains Thiago S Mosqueiro BioCircuits Institute University of California San Diego thmosqueiro.vandroiy.com Conference on Information Sciences and Systems Princeton (NJ), 03/15/2016
- 2. In collaboration with Martin Strube-Bloss Rafael Tuma Reynaldo Pinto Brian Smith Ramon Huerta
- 3. Take-home message Reaction times of neural populations: multivariate change-point detection Electric 鍖sh communication: change-point as a time-series segmentation
- 4. Complexity of Odorant Time Series Vergara et al. 2013, Sensors Actuators B 185 462 M. Trincavelli et al. 2009, Sensors Actuators B 139 165 Picture by Kim S. Mosqueiro (Apr 2015) Rodriguez-Lujan & J. Fonollosa et al. '2014, Chem and Intell Lab Systems 30 123 Courtesy of M Trincavelli
- 5. Change point technique The (single) change point problem can be stated as the hypothesis testing below: We are interested in two aspects: How likely is H0 vs H1? Estimate the transition point
- 6. Change point technique Divergence: Solution for the transition time: Matteson and James 2014, J American Statistical Association 109, 334345.
- 7. Mosqueiro & Maia 2012, Phys Rev E 88 012712 Neural systems We know some coding mechanisms In insects, anatomy is well documented Mosqueiro & Huerta 2014, Current opinion in insect science
- 8. Main olfactory pathway Mosqueiro, Strube-Bloss, Smith & Huerta, to appear
- 9. Proxy to reaction time Strube-Bloss, et al. 2012, PLOS One 7 e50322
- 10. Proxy to reaction time Strube-Bloss, et al. 2012, PLOS One 7 e50322
- 11. Using all spike trains To use all spike trains, we get the 鍖rst 5 components from PCA We then 鍖nd the change point jointly
- 12. Neural reaction times No need for proxies and a single general concept Use the information of the whole spike train Yield much more precise results Could be applied to fMRI or EEGs, to jointly 鍖nd change points within brain regions Can be performed on the 鍖y
- 13. Pulse-type electric 鍖sh Forlim & Pinto 2014, PLOS One 9 e84885
- 16. Fast time scale Change points are very close (most of time <2s apart) Average of 1.6 symbols / sec To turn it into a symbolic dynamic, we construct features: (variance, avg slope, area under curve, interval duration)
- 17. Clustering of the segments Both 鍖sh showed similar symbols cue on vocabulary Mutual Information drops after bootstrapping/surrogating Segments showed 3 clusters:
- 18. Clustering of the segments Both 鍖sh showed similar symbols cue on vocabulary Mutual Information drops after bootstrapping/surrogating Segments showed 3 clusters:
- 19. Cues to Time-series segmentation No need for bins with 鍖xed size Coarser time scale may link to behavior Clustering symbols seems the same for three different 鍖sh is there a general vocabulary? Symbolic dynamics is there a grammar? Current methods are VERY slow for such number of change points we have a new strategy coming soon
- 20. Free implementation github.com/VandroiyLabs/chapolins Parallel, multiple change points implementation in C for ef鍖cient of several algorithms with an API for Python Logo courtesy of Andre MR Santos Change Point Library for Non-parametric Statistics
- 21. Thanks, everyone, for your attention