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Inferring neural population dynamics from multiple partial recordings of the same neural circuit
Srinivas C Turaga · Lars Buesing · Adam M Packer · Henry Dalgleish · Noah Pettit · Michael Hausser · Jakob H Macke

Fri Dec 06 03:34 PM -- 03:38 PM (PST) @ Harvey's Convention Center Floor, CC

Simultaneous recordings of the activity of large neural populations are extremely valuable as they can be used to infer the dynamics and interactions of neurons in a local circuit, shedding light on the computations performed. It is now possible to measure the activity of hundreds of neurons using 2-photon calcium imaging. However, many computations are thought to involve circuits consisting of thousands of neurons, such as cortical barrels in rodent somatosensory cortex. Here we contribute a statistical method for "stitching" together sequentially imaged sets of neurons into one model by phrasing the problem as fitting a latent dynamical system with missing observations. This method allows us to substantially expand the population-sizes for which population dynamics can be characterized---beyond the number of simultaneously imaged neurons. In particular, we demonstrate using recordings in mouse somatosensory cortex that this method makes it possible to predict noise correlations between non-simultaneously recorded neuron pairs.

Author Information

Srinivas C Turaga (HHMI Janelia Research Campus)
Lars Buesing (Columbia University)
Adam M Packer (UCL)
Henry Dalgleish (UCL)
Noah Pettit (UCL)
Michael Hausser (UCL)
Jakob H Macke (University of Tübingen & MPI IS Tübingen)

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