Computational Neuroscience Journal Club

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• Rui Xia and David Lui
• Tuesday 06 June 2023, 14:00-16:00
• In Person (CBL Seminar Room) and Online on Zoom.

If you have a question about this talk, please contact Luke Johnston.

Please join us for our fortnightly Computational Neuroscience journal club on Tuesday 6th June at 2pm UK time in the CBL seminar room, or online on zoom. The title is ‘Switching models to uncover hidden behavioural and neural states’, presented by Rui Xia and David Lui.

Zoom information: https://eng-cam.zoom.us/j/84204498431?pwd=Um1oU284b1YxWThObGw4ZU9XZitWdz09 Meeting ID: 842 0449 8431 Passcode: 684140

Summary:

Advances in modern recording technologies have enable large-scale measurements of neural activity in orders of magnitude more than we could only a few years ago. These datasets offer unprecedented opportunities to study how neural circuits function, process sensory information and generate behaviour. To gain better insight to these complex and heterogeneous time series data with nonlinear dynamics, one approach is decomposing the data into segments that each can be explained by simple, linear dynamics. Linderman et al [1] proposed recurrent switching linear dynamical systems which can automatically divide latent space of population neural activities into discrete, behaviourally significant states. We will present two papers applying this flexible and interpretable model to recordings of head ganglia neurons in nematode C. elegans [2], and neuronal subpopulations within MPOA (medial preoptic area) and VMHvl (ventromedial hypothalamus) in mice during innate social behaviours [3]. In the experiments of C. elegans, the framework reveals states that closely match manual labels of different behaviours, such as forward crawling, reversals and turns. For hypothalamus recordings in mice, an approximate line attractor is uncovered, progression along which is discovered to be correlated with an escalation of agonistic behaviour.

References:

[1] Linderman, Scott, et al. “Bayesian learning and inference in recurrent switching linear dynamical systems.” Artificial Intelligence and Statistics. PMLR , 2017.

[2] Linderman, Scott, et al. “Hierarchical recurrent state space models reveal discrete and continuous dynamics of neural activity in C. elegans.” BioRxiv (2019): 621540.

[3] Nair, Aditya, et al. “An approximate line attractor in the hypothalamus encodes an aggressive state.” Cell 186.1 (2023): 178-193.

This talk is part of the Computational Neuroscience series.

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