Neural Network Poisson Models for Behavioural and Neural Spike Train Data

Moein Khajehnejad, Forough Habibollahi, Richard Nock, Ehsan Arabzadeh, Peter Dayan, Amir Dezfouli
Proceedings of the 39th International Conference on Machine Learning, PMLR 162:10974-10996, 2022.

Abstract

One of the most important and challenging application areas for complex machine learning methods is to predict, characterize and model rich, multi-dimensional, neural data. Recent advances in neural recording techniques have made it possible to monitor the activity of a large number of neurons across different brain regions as animals perform behavioural tasks. This poses the critical challenge of establishing links between neural activity at a microscopic scale, which might for instance represent sensory input, and at a macroscopic scale, which then generates behaviour. Predominant modeling methods apply rather disjoint techniques to these scales; by contrast, we suggest an end-to-end model which exploits recent developments of flexible, but tractable, neural network point-process models to characterize dependencies between stimuli, actions, and neural data. We apply this model to a public dataset collected using Neuropixel probes in mice performing a visually-guided behavioural task as well as a synthetic dataset produced from a hierarchical network model with reciprocally connected sensory and integration circuits intended to characterize animal behaviour in a fixed-duration motion discrimination task. We show that our model outperforms previous approaches and contributes novel insights into the relationships between neural activity and behaviour.

Cite this Paper

BibTeX
@InProceedings{pmlr-v162-khajehnejad22a, title = {Neural Network Poisson Models for Behavioural and Neural Spike Train Data}, author = {Khajehnejad, Moein and Habibollahi, Forough and Nock, Richard and Arabzadeh, Ehsan and Dayan, Peter and Dezfouli, Amir}, booktitle = {Proceedings of the 39th International Conference on Machine Learning}, pages = {10974--10996}, year = {2022}, editor = {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan}, volume = {162}, series = {Proceedings of Machine Learning Research}, month = {17--23 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v162/khajehnejad22a/khajehnejad22a.pdf}, url = {https://proceedings.mlr.press/v162/khajehnejad22a.html}, abstract = {One of the most important and challenging application areas for complex machine learning methods is to predict, characterize and model rich, multi-dimensional, neural data. Recent advances in neural recording techniques have made it possible to monitor the activity of a large number of neurons across different brain regions as animals perform behavioural tasks. This poses the critical challenge of establishing links between neural activity at a microscopic scale, which might for instance represent sensory input, and at a macroscopic scale, which then generates behaviour. Predominant modeling methods apply rather disjoint techniques to these scales; by contrast, we suggest an end-to-end model which exploits recent developments of flexible, but tractable, neural network point-process models to characterize dependencies between stimuli, actions, and neural data. We apply this model to a public dataset collected using Neuropixel probes in mice performing a visually-guided behavioural task as well as a synthetic dataset produced from a hierarchical network model with reciprocally connected sensory and integration circuits intended to characterize animal behaviour in a fixed-duration motion discrimination task. We show that our model outperforms previous approaches and contributes novel insights into the relationships between neural activity and behaviour.} }
Endnote
%0 Conference Paper %T Neural Network Poisson Models for Behavioural and Neural Spike Train Data %A Moein Khajehnejad %A Forough Habibollahi %A Richard Nock %A Ehsan Arabzadeh %A Peter Dayan %A Amir Dezfouli %B Proceedings of the 39th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2022 %E Kamalika Chaudhuri %E Stefanie Jegelka %E Le Song %E Csaba Szepesvari %E Gang Niu %E Sivan Sabato %F pmlr-v162-khajehnejad22a %I PMLR %P 10974--10996 %U https://proceedings.mlr.press/v162/khajehnejad22a.html %V 162 %X One of the most important and challenging application areas for complex machine learning methods is to predict, characterize and model rich, multi-dimensional, neural data. Recent advances in neural recording techniques have made it possible to monitor the activity of a large number of neurons across different brain regions as animals perform behavioural tasks. This poses the critical challenge of establishing links between neural activity at a microscopic scale, which might for instance represent sensory input, and at a macroscopic scale, which then generates behaviour. Predominant modeling methods apply rather disjoint techniques to these scales; by contrast, we suggest an end-to-end model which exploits recent developments of flexible, but tractable, neural network point-process models to characterize dependencies between stimuli, actions, and neural data. We apply this model to a public dataset collected using Neuropixel probes in mice performing a visually-guided behavioural task as well as a synthetic dataset produced from a hierarchical network model with reciprocally connected sensory and integration circuits intended to characterize animal behaviour in a fixed-duration motion discrimination task. We show that our model outperforms previous approaches and contributes novel insights into the relationships between neural activity and behaviour.
APA
Khajehnejad, M., Habibollahi, F., Nock, R., Arabzadeh, E., Dayan, P. & Dezfouli, A.. (2022). Neural Network Poisson Models for Behavioural and Neural Spike Train Data. Proceedings of the 39th International Conference on Machine Learning, in Proceedings of Machine Learning Research 162:10974-10996 Available from https://proceedings.mlr.press/v162/khajehnejad22a.html.

Related Material