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Stitching neural activity in space and time: theory and practice


Macke,  Jakob H
Max Planck Research Group Neural Systems Analysis, Center of Advanced European Studies and Research (caesar), Max Planck Society;

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Nonnenmacher, M., Buesing, L., Speiser, A., Turaga, S., & Macke, J. H. (2016). Stitching neural activity in space and time: theory and practice. Poster presented at Computational and Systems Neuroscience Meeting (COSYNE 2016), Salt Lake City, UT, USA.

Cite as: https://hdl.handle.net/21.11116/0000-0006-8DEA-4
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 in-vivo 2-photon calcium imaging. However,
this experimental technique imposes a trade-off between the number of neurons which can be simultaneously recorded, and the temporal resolution at which the activity of those neurons can be sampled. Previous work (Turaga et al 2012, Bishop Yu 2014) has shown that statistical models can be used to ameliorate this trade-off, by ‘stitching’ neural activity from subpopulations of neurons which have been imaged sequentially with overlap, rather than simultaneously. This makes it possible to estimate correlations even between non-simultaneously recorded neurons. In this work, we make two contributions: First, we show how taking into account correlations in the dynamics of neural activity gives rise to more general conditions under which stitching can be achieved, extending the work of (Bishop Yu 2014). Second, we extend this framework to stitch activity both in space and time, i.e. from multiple subpopulations which might be imaged at different temporal rates. We use low-dimensional linear latent dynamical systems (LDS) to model neural population activity, and present scalable algorithms to estimate the parameters of a globally accurate LDS model from incomplete measurements. Using simulated data, we show that this approach can provide more accurate estimates of neural correlations than conventional approaches, and gives insights into the underlying neural dynamics.