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Accurate inference in parametric models reshapes neuroscientific interpretation and improves data-driven discovery

View ORCID ProfilePratik S. Sachdeva, View ORCID ProfileJesse A. Livezey, View ORCID ProfileMaximilian E. Dougherty, View ORCID ProfileBon-Mi Gu, View ORCID ProfileJoshua D. Berke, View ORCID ProfileKristofer E. Bouchard
doi: https://doi.org/10.1101/2020.04.10.036244
Pratik S. Sachdeva
1Redwood Center for Theoretical Neuroscience, University of California, Berkeley, Berkeley, California, USA
2Department of Physics, University of California, Berkeley, Berkeley, California, USA
3Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA
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  • For correspondence: pratik.sachdeva@berkeley.edu
Jesse A. Livezey
1Redwood Center for Theoretical Neuroscience, University of California, Berkeley, Berkeley, California, USA
3Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA
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Maximilian E. Dougherty
3Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA
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Bon-Mi Gu
4Department of Neurology, University of California, San Francisco, San Francisco, California, USA
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Joshua D. Berke
4Department of Neurology, University of California, San Francisco, San Francisco, California, USA
5Department of Psychiatry; Neuroscience Graduate Program; Kavli Institute for Fundamental Neuroscience; Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California, USA
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Kristofer E. Bouchard
1Redwood Center for Theoretical Neuroscience, University of California, Berkeley, Berkeley, California, USA
3Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA
6Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA
7Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, California, USA
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Abstract

A central goal of systems neuroscience is to understand the relationships amongst constituent units in neural populations and their modulation by external factors using high-dimensional and stochastic neural recordings. Statistical models, particularly parametric models, play an instrumental role in accomplishing this goal, because their fitted parameters can provide insight into the underlying biological processes that generated the data. However, extracting conclusions from a parametric model requires that it is fit using an inference procedure capable of selecting the correct parameters and properly estimating their values. Traditional approaches to parameter inference have been shown to suffer from failures in both selection and estimation. Recent development of algorithms that ameliorate these deficiencies raises the question of whether past work relying on such inference procedures have produced inaccurate systems neuroscience models, thereby impairing their interpretation. Here, we used the Union of Intersections, a statistical inference framework capable of state-of-the-art selection and estimation performance, to fit functional coupling, encoding, and decoding models across a battery of neural datasets. We found that, compared to baseline procedures, UoI inferred models with increased sparsity, improved stability, and qualitatively different parameter distributions, while maintaining predictive performance across recording modality, brain region, and task. Specifically, we obtained highly sparse functional coupling networks with substantially different community structure, more parsimonious encoding models, and decoding models that rely on fewer single-units. Together, these results demonstrate that accurate parameter inference reshapes interpretation in diverse neuroscience contexts. The ubiquity of model-based data-driven discovery in biology suggests that analogous results would be seen in other fields.

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 4.0 International license.
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Posted April 13, 2020.
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Accurate inference in parametric models reshapes neuroscientific interpretation and improves data-driven discovery
Pratik S. Sachdeva, Jesse A. Livezey, Maximilian E. Dougherty, Bon-Mi Gu, Joshua D. Berke, Kristofer E. Bouchard
bioRxiv 2020.04.10.036244; doi: https://doi.org/10.1101/2020.04.10.036244
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Accurate inference in parametric models reshapes neuroscientific interpretation and improves data-driven discovery
Pratik S. Sachdeva, Jesse A. Livezey, Maximilian E. Dougherty, Bon-Mi Gu, Joshua D. Berke, Kristofer E. Bouchard
bioRxiv 2020.04.10.036244; doi: https://doi.org/10.1101/2020.04.10.036244

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