Imperial College London
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ProfessorSimonSchultz

Faculty of Engineering, Department of Bioengineering

Professor of Neurotechnology
 
 
 
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Contact

 

s.schultz Website

 
 
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Location

 

4.11Royal School of MinesSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Reynolds:2018:10.1162/neco_a_01114,
author = {Reynolds, SC and abrahamsson, T and sjostrom, PJ and Schultz, S and Dragotti, PL},
doi = {10.1162/neco_a_01114},
journal = {Neural Computation},
pages = {2726--2756},
title = {CosMIC: a consistent metric for spike inference from calcium imaging},
url = {http://dx.doi.org/10.1162/neco_a_01114},
volume = {30},
year = {2018}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - In recent years, the development of algorithms to detect neuronal spiking activity from two-photon calcium imaging data has received much attention.   Meanwhile,  few researchers  have  examined  the  metrics  used  to  assess  the  similarity  of  detected  spike trains with the ground truth.  We highlight the limitations of the two most commonly used metrics, the spike train correlation and success rate, and propose an alternative, which we refer to as CosMIC. Rather than operating on the true and estimated spike trains directly, the proposed metric assesses the similarity of the pulse trains obtained from convolution of the spike trains with a smoothing pulse.  The pulse width, which is derived from the statistics of the imaging data, reflects the temporal tolerance of the metric.  The final metric score is the size of the commonalities of the pulse trains as a fraction of their average size. Viewed through the lens of set theory, CosMIC resembles a continuous Sørensen-Dice coefficient — an index commonly used to assess the similarity of discrete, presence/absence data.  We demonstrate the ability of the proposed metric to discriminate the precision and recall of spike train estimates. Unlike the spike train correlation, which appears to reward overestimation, the proposed metric score is maximised when the correct number of spikes have been detected.  Furthermore, we show that CosMIC is more sensitive to the temporal precision of estimates than the success rate.
AU  - Reynolds,SC
AU  - abrahamsson,T
AU  - sjostrom,PJ
AU  - Schultz,S
AU  - Dragotti,PL
DO  - 10.1162/neco_a_01114
EP  - 2756
PY  - 2018///
SN  - 0899-7667
SP  - 2726
TI  - CosMIC: a consistent metric for spike inference from calcium imaging
T2  - Neural Computation
UR  - http://dx.doi.org/10.1162/neco_a_01114
UR  - http://hdl.handle.net/10044/1/60305
VL  - 30
ER  -
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