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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
In collaboration with
Martin Strube-Bloss Rafael Tuma
Reynaldo Pinto Brian Smith Ramon Huerta
Take-home message
Reaction times of neural populations:
multivariate change-point detection
Electric fish communication:
change-point as a time-series segmentation
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
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
Change point technique
Divergence:
Solution for the transition time:
Matteson and James ‘2014,
J American Statistical Association 109, 334–345.
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
Main olfactory pathway
Mosqueiro, Strube-Bloss,
Smith & Huerta,
to appear…
Proxy to reaction time
Strube-Bloss, et al. ‘2012,
PLOS One 7 e50322
Proxy to reaction time
Strube-Bloss, et al. ‘2012,
PLOS One 7 e50322
Using all spike trains
• To use all spike trains, we
get the first 5 components
from PCA
• We then find the change
point jointly
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
Pulse-type electric fish
Forlim & Pinto ‘2014, PLOS One 9 e84885
Time series segmentation
Coarse-grained time scale
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)
Clustering of the segments
• Both fish showed similar symbols — cue on vocabulary
• Mutual Information drops after bootstrapping/surrogating
Segments showed 3 clusters:
Clustering of the segments
• Both fish showed similar symbols — cue on vocabulary
• Mutual Information drops after bootstrapping/surrogating
Segments showed 3 clusters:
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…
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
Thanks, everyone, for
your attention

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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 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
  • 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