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Temporal alignment and latent Gaussian process factor inference in population spike trains

Duncker, L; Sahani, M; (2018) Temporal alignment and latent Gaussian process factor inference in population spike trains. In: Bengio, S and Wallach, H and Larochelle, H and Grauman, K and CesaBianchi, N and Garnett, R, (eds.) Proceedings of Conference on Neural Information Processing Systems 31 (NIPS 2018). Neural Information Processing Systems (NIPS): Montreal, Canada. Green open access

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Abstract

We introduce a novel scalable approach to identifying common latent structure in neural population spike-trains, which allows for variability both in the trajectory and in the rate of progression of the underlying computation. Our approach is based on shared latent Gaussian processes (GPs) which are combined linearly, as in the Gaussian Process Factor Analysis (GPFA) algorithm. We extend GPFA to handle unbinned spike-train data by incorporating a continuous time point-process likelihood model, achieving scalability with a sparse variational approximation. Shared variability is separated into terms that express condition dependence, as well as trial-to-trial variation in trajectories. Finally, we introduce a nested GP formulation to capture variability in the rate of evolution along the trajectory. We show that the new method learns to recover latent trajectories in synthetic data, and can accurately identify the trial-to-trial timing of movement-related parameters from motor cortical data without any supervision.

Type: Proceedings paper
Title: Temporal alignment and latent Gaussian process factor inference in population spike trains
Event: Conference on Neural Information Processing Systems 31 (NIPS), 3-8 December 2018, Montreal, Canada
Open access status: An open access version is available from UCL Discovery
Publisher version: https://papers.nips.cc/paper/8245-temporal-alignme...
Language: English
Additional information: This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Gatsby Computational Neurosci Unit
URI: https://discovery.ucl.ac.uk/id/eprint/10075904
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