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Localizing neuronal somata from Multi-Electrode Array in-vivo recordings using deep learning

Buccino, Alessio Paolo; Ness, Torbjørn Vefferstad; Einevoll, Gaute; Cauwenberghs, Gert; Häfliger, Philipp
Journal article; PublishedVersion; Peer reviewed
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Year
2017
Permanent link
http://urn.nb.no/URN:NBN:no-68207

CRIStin
1529839

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  • Institutt for informatikk [3604]
  • CRIStin høstingsarkiv [15898]
Original version
IEEE Engineering in Medicine and Biology Society. Conference Proceedings. 2017, 974-977, DOI: http://dx.doi.org/10.1109/EMBC.2017.8036988
Abstract
With the latest development in the design and fabrication of high-density Multi-Electrode Arrays (MEA) for in-vivo neural recordings, the spatiotemporal information in the recorded signals allows for refined estimation of a neuron’s location around the probe. In parallel, advances in computational models for neural activity enables simulation of recordings from neurons with detailed morphology. Our approach uses deep learning algorithms on a large set of such simulation data to extract the 3D position of the neuronal somata. Multi-compartment models from 13 different neural morphologies in layer 5 (L5) of the rat’s neocortex are placed at random locations and with different alignments with respect to the MEA. The sodium trough and repolarisation peak images on the MEA serve as input features for a Convolutional Neural Network (CNN), which predicts the neural location robustly and with low error rates. The forward modeling/machine learning approach yields very accurate results for the different morphologies and is able to cope with different neuron alignments.

© 2017 IEEE
 
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