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Neural Network Poisson Models for Behavioural and Neural Spike Train Data

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Dayan,  P
Department of Computational Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Dezfouli, A., Nock, R., Arabzadeh, E., & Dayan, P. (submitted). Neural Network Poisson Models for Behavioural and Neural Spike Train Data.


Cite as: http://hdl.handle.net/21.11116/0000-0006-DA83-0
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.