Two techniques of non-parametric change point detection are applied to two different neuroscience datasets. In the first 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 fish. We are also sharing our software implementation Chapolins at GitHub.

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 fish 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 τ

6. Change point technique
Divergence:
Solution for the transition time:
Matteson and James ‘2014,
J American Statistical Association 109, 334–345.

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 first 5 components
from PCA
• We then find 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 find
change points within brain regions
• Can be performed on the fly

13. Pulse-type electric fish
Forlim & Pinto ‘2014, PLOS One 9 e84885

14. Time series segmentation

15. Coarse-grained time scale

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 fish showed similar symbols — cue on vocabulary
• Mutual Information drops after bootstrapping/surrogating
Segments showed 3 clusters:

18. Clustering of the segments
• Both fish 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 fixed size
• Coarser time scale may link to behavior
• Clustering symbols seems the same for three
different fish — 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 efficient 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