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Uncovering temporal structure in neural data with statistical machine learning models

Abstract:

Methods for analysing neural dynamics have predominantly focussed on resting state activity, typically using unsupervised models to infer the main modes of spontaneous variation in the absence of deliberately controlled experimental stimuli. Analysis of neural task data, on the other hand, overwhelmingly assumes responses that are perfectly aligned in time and synchronised over multiple trials by the onset time of the stimulus. These assumptions contradict widespread evidence for a more dy...

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Institution:
University of Oxford
Division:
MSD
Department:
Psychiatry
Role:
Author

Contributors

Institution:
University of Oxford
Division:
MSD
Department:
Psychiatry
Role:
Supervisor
Institution:
University of Oxford
Division:
MSD
Department:
Clinical Neurosciences
Role:
Supervisor
Type of award:
DPhil
Level of award:
Doctoral
Awarding institution:
University of Oxford
Language:
English
Keywords:
Subjects:
UUID:
uuid:63d14863-e937-40fb-81a2-c23783edf951
Deposit date:
2020-01-17

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