Non-parametric change point detection for spike trains

Thiago S MosqueiroBioCircuits Institute

University of California San Diego thmosqueiro.vandroiy.com

Conference on Information Sciences and SystemsPrinceton (NJ), 03/15/2016

In collaboration withMartin Strube-Bloss Rafael Tuma

Reynaldo Pinto Brian Smith Ramon Huerta

Take-home messageReaction times of neural populations:

multivariate change-point detection

Electric fish communication:change-point as a time-series segmentation

Complexity of Odorant Time SeriesVergara 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 systemsWe 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