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Neural Network Poisson Models for Behavioural and Neural Spike Train Data

Amir Dezfouli, Richard Nock, Ehsan Arabzadeh, Peter Dayan
doi: https://doi.org/10.1101/2020.07.13.201673
Amir Dezfouli
1Data61, CSIRO
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  • For correspondence: amir.dezfouli@data61.csiro.au
Richard Nock
1Data61, CSIRO
2Australian National University
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Ehsan Arabzadeh
2Australian National University
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Peter Dayan
3Max Planck Institute
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Abstract

It is now possible to monitor the activity of a large number of neurons across the brain as animals perform behavioural tasks. A primary aim for modeling is to reveal (i) how sensory inputs are represented in neural activities and (ii) how these representations translate into behavioural responses. Predominant methods apply rather disjoint techniques to (i) and (ii); by contrast, we suggest an end-to-end model which jointly fits both behaviour and neural activities and tracks their covariabilities across trials using inferred noise correlations. Our model exploits recent developments of flexible, but tractable, neural network point-process models to characterize dependencies between stimuli, actions and neural data. We apply the framework to a dataset collected using Neuropixel probes in a visual discrimination task and analyse noise correlations to gain novel insights into the relationships between neural activities and behaviour.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted July 14, 2020.
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Neural Network Poisson Models for Behavioural and Neural Spike Train Data
Amir Dezfouli, Richard Nock, Ehsan Arabzadeh, Peter Dayan
bioRxiv 2020.07.13.201673; doi: https://doi.org/10.1101/2020.07.13.201673
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Neural Network Poisson Models for Behavioural and Neural Spike Train Data
Amir Dezfouli, Richard Nock, Ehsan Arabzadeh, Peter Dayan
bioRxiv 2020.07.13.201673; doi: https://doi.org/10.1101/2020.07.13.201673

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