TY - GEN
T1 - A robust and efficient method to recover neural events from noisy and corrupted data
AU - Dyer, Eva L.
AU - Studer, Christoph
AU - Robinson, Jacob T.
AU - Baraniuk, Richard G.
N1 - Copyright:
Copyright 2014 Elsevier B.V., All rights reserved.
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
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U2 - 10.1109/NER.2013.6696004
DO - 10.1109/NER.2013.6696004
M3 - Conference contribution
AN - SCOPUS:84897713924
SN - 9781467319690
T3 - International IEEE/EMBS Conference on Neural Engineering, NER
SP - 593
EP - 596
BT - 2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013
T2 - 2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013
Y2 - 6 November 2013 through 8 November 2013
ER -