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Parallel inference of hierarchical latent dynamics in two-photon calcium imaging of neuronal populations

View ORCID ProfileLuke Y. Prince, View ORCID ProfileShahab Bakhtiari, View ORCID ProfileColleen J. Gillon, View ORCID ProfileBlake A. Richards
doi: https://doi.org/10.1101/2021.03.05.434105
Luke Y. Prince
1School of Computer Science, McGill University, Montreal, Quebec, Canada
2Mila, Montreal, Quebec, Canada
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Shahab Bakhtiari
1School of Computer Science, McGill University, Montreal, Quebec, Canada
2Mila, Montreal, Quebec, Canada
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Colleen J. Gillon
2Mila, Montreal, Quebec, Canada
3Department of Biological Sciences, University of Toronto Scarborough, Toronto, Ontario, Canada
4Department of Cell and Systems Biology, University of Toronto, Toronto, Ontario, Canada
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Blake A. Richards
1School of Computer Science, McGill University, Montreal, Quebec, Canada
2Mila, Montreal, Quebec, Canada
5Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
6Learning in Machines and Brains Program, Canadian Institute for Advanced Research, Toronto, Ontario, Canada
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  • For correspondence: blake.richards@mila.quebec
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Abstract

Dynamic latent variable modelling has provided a powerful tool for understanding how populations of neurons compute. For spiking data, such latent variable modelling can treat the data as a set of point-processes, due to the fact that spiking dynamics occur on a much faster timescale than the computational dynamics being inferred. In contrast, for other experimental techniques, the slow dynamics governing the observed data are similar in timescale to the computational dynamics that researchers want to infer. An example of this is in calcium imaging data, where calcium dynamics can have timescales on the order of hundreds of milliseconds. As such, the successful application of dynamic latent variable modelling to modalities like calcium imaging data will rest on the ability to disentangle the deeper- and shallower-level dynamical systems’ contributions to the data. To-date, no techniques have been developed to directly achieve this. Here we solve this problem by extending recent advances using sequential variational autoencoders for dynamic latent variable modelling of neural data. Our system VaLPACa (Variational Ladders for Parallel Autoencoding of Calcium imaging data) solves the problem of disentangling deeper- and shallower-level dynamics by incorporating a ladder architecture that can infer a hierarchy of dynamical systems. Using some built-in inductive biases for calcium dynamics, we show that we can disentangle calcium flux from the underlying dynamics of neural computation. First, we demonstrate with synthetic calcium data that we can correctly disentangle an underlying Lorenz attractor from calcium dynamics. Next, we show that we can infer appropriate rotational dynamics in spiking data from macaque motor cortex after it has been converted into calcium fluorescence data via a calcium dynamics model. Finally, we show that our method applied to real calcium imaging data from primary visual cortex in mice allows us to infer latent factors that carry salient sensory information about unexpected stimuli. These results demonstrate that variational ladder autoencoders are a promising approach for inferring hierarchical dynamics in experimental settings where the measured variable has its own slow dynamics, such as calcium imaging data. Our new, open-source tool thereby provides the neuroscience community with the ability to apply dynamic latent variable modelling to a wider array of data modalities.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted March 08, 2021.
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Parallel inference of hierarchical latent dynamics in two-photon calcium imaging of neuronal populations
Luke Y. Prince, Shahab Bakhtiari, Colleen J. Gillon, Blake A. Richards
bioRxiv 2021.03.05.434105; doi: https://doi.org/10.1101/2021.03.05.434105
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Parallel inference of hierarchical latent dynamics in two-photon calcium imaging of neuronal populations
Luke Y. Prince, Shahab Bakhtiari, Colleen J. Gillon, Blake A. Richards
bioRxiv 2021.03.05.434105; doi: https://doi.org/10.1101/2021.03.05.434105

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