A robust and efficient method to recover neural events from noisy and corrupted data

Eva L. Dyer, Christoph Studer, Jacob T. Robinson, Richard G. Baraniuk

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations

Abstract

In a variety of neural data analysis problems, 'neural events' such as action potentials (APs) or post-synaptic potentials (PSPs), must be recovered from noisy and possibly corrupted measurements. For instance, in calcium imaging, an AP or group of APs generate a stereotyped calcium signal with a quick rise time and slow decay. In this work, we develop a general-purpose method for: (i) learning a template waveform that signifies the presence of a neural event and (ii) neural event recovery to determine the times at which such events occur. Our approach is based upon solving a sparse signal separation problem to separate the neural signal of interest from any noise and other corruptions that arise due to baseline drift, measurement noise, and breathing/motion artifacts. For both synthetic and real measured data, we demonstrate that our approach accurately learns the underlying template waveform and detects neural events, even in the presence of strong amounts of noise and corruption. The method's robustness, simplicity, and computational efficiency makes it amenable for use in the analysis of data arising in large-scale studies of both time-varying calcium imaging and whole-cell electrophysiology.

Original languageEnglish (US)
Title of host publication2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013
Pages593-596
Number of pages4
DOIs
StatePublished - 2013
Event2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013 - San Diego, CA, United States
Duration: Nov 6 2013Nov 8 2013

Publication series

NameInternational IEEE/EMBS Conference on Neural Engineering, NER
ISSN (Print)1948-3546
ISSN (Electronic)1948-3554

Other

Other2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013
CountryUnited States
CitySan Diego, CA
Period11/6/1311/8/13

ASJC Scopus subject areas

  • Artificial Intelligence
  • Mechanical Engineering

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