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  Interpreting wide-band neural activity using convolutional neural networks

Frey, M., Tanni, S., Perrodin, C., O'Leary, A., Nau, M., Kelly, J., et al. (2021). Interpreting wide-band neural activity using convolutional neural networks. eLife, 10: e66551. doi:10.7554/eLife.66551.

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Frey, Markus1, 2, Autor           
Tanni, Sander3, Autor
Perrodin, Catherine4, Autor
O'Leary, Alice3, Autor
Nau, Matthias1, 2, Autor           
Kelly, Jack5, Autor
Banino, Andrea6, Autor
Bendor, Daniel4, Autor
Lefort, Julie3, Autor
Doeller, Christian F.1, 2, 7, Autor           
Barry, Caswell3, Autor
Affiliations:
1Egil and Pauline Braathen and Fred Kavli Centre for Cortical Microcircuits, Kavli Institute, Norwegian University of Science and Technology, Trondheim, Norway, ou_persistent22              
2Department Psychology (Doeller), MPI for Human Cognitive and Brain Sciences, Max Planck Society, Leipzig, DE, ou_2591710              
3Department of Cell and Developmental Biology, University College London, United Kingdom, ou_persistent22              
4Institute of Behavioural Neuroscience, University College London, United Kingdom, ou_persistent22              
5Open Climate Fix, London, United Kingdom, ou_persistent22              
6DeepMind, London, United Kingdom, ou_persistent22              
7Institute of Psychology, University of Leipzig, Germany, ou_persistent22              

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Schlagwörter: Calcium imaging; Decoding; Deep learning; Electrophysiology; Neuroscience; Rat
 Zusammenfassung: Rapid progress in technologies such as calcium imaging and electrophysiology has seen a dramatic increase in the size and extent of neural recordings. Even so, interpretation of this data requires considerable knowledge about the nature of the representation and often depends on manual operations. Decoding provides a means to infer the information content of such recordings but typically requires highly processed data and prior knowledge of the encoding scheme. Here, we developed a deep-learning framework able to decode sensory and behavioral variables directly from wide-band neural data. The network requires little user input and generalizes across stimuli, behaviors, brain regions, and recording techniques. Once trained, it can be analyzed to determine elements of the neural code that are informative about a given variable. We validated this approach using electrophysiological and calcium-imaging data from rodent auditory cortex and hippocampus as well as human electrocorticography (ECoG) data. We show successful decoding of finger movement, auditory stimuli, and spatial behaviors - including a novel representation of head direction - from raw neural activity.

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Sprache(n): eng - English
 Datum: 2021-01-152021-07-132021-08-02
 Publikationsstatus: Online veröffentlicht
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 Identifikatoren: DOI: 10.7554/eLife.66551
PMID: 34338632
PMC: PMC8328518
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Projektname : -
Grant ID : ERC-CoG GEOCOG 724836
Förderprogramm : Horizon 2020
Förderorganisation : European Research Council (ERC)
Projektname : -
Grant ID : 212281/Z/18/Z; 110238/Z/15/Z
Förderprogramm : -
Förderorganisation : Wellcome Trust

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Titel: eLife
Genre der Quelle: Zeitschrift
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Seiten: - Band / Heft: 10 Artikelnummer: e66551 Start- / Endseite: - Identifikator: ISSN: 2050-084X
CoNE: https://pure.mpg.de/cone/journals/resource/2050-084X