Improved inference in coupling, encoding, and decoding models and its consequence for neuroscientific interpretation

Pratik S Sachdeva; Jesse A Livezey; Maximilian E Dougherty; Bon-Mi Gu; Joshua D Berke; Kristofer E Bouchard

doi:10.1016/j.jneumeth.2021.109195

Improved inference in coupling, encoding, and decoding models and its consequence for neuroscientific interpretation

J Neurosci Methods. 2021 Jul 1:358:109195. doi: 10.1016/j.jneumeth.2021.109195. Epub 2021 Apr 24.

Authors

Pratik S Sachdeva¹, Jesse A Livezey², Maximilian E Dougherty³, Bon-Mi Gu⁴, Joshua D Berke⁵, Kristofer E Bouchard⁶

Affiliations

¹ Redwood Center for Theoretical Neuroscience, University of California, Berkeley, 94720, CA, USA; Department of Physics, University of California, Berkeley, 94720, CA, USA; Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, 94720, CA, USA.
² Redwood Center for Theoretical Neuroscience, University of California, Berkeley, 94720, CA, USA; Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, 94720, CA, USA.
³ Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, 94720, CA, USA.
⁴ Department of Neurology, University of California, San Francisco, San Francisco, 94143, CA, USA.
⁵ Department of Neurology, University of California, San Francisco, San Francisco, 94143, CA, USA; Department of Psychiatry; Neuroscience Graduate Program; Kavli Institute for Fundamental Neuroscience; Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, 94143, CA, USA.
⁶ Redwood Center for Theoretical Neuroscience, University of California, Berkeley, 94720, CA, USA; Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, 94720, CA, USA; Computational Resources Division, Lawrence Berkeley National Laboratory, Berkeley, 94720, CA, USA; Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, 94720, CA, USA.

PMID: 33905791
DOI: 10.1016/j.jneumeth.2021.109195

Abstract

Background: 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. Parametric statistical models (e.g., coupling, encoding, and decoding models), play an instrumental role in accomplishing this goal. However, extracting conclusions from a parametric model requires that it is fit using an inference algorithm 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. The 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.

New method: We used algorithms based on Union of Intersections, a statistical inference framework based on stability principles, capable of improved selection and estimation.

Comparison: We fit functional coupling, encoding, and decoding models across a battery of neural datasets using both UoI and baseline inference procedures (e.g., ℓ₁-penalized GLMs), and compared the structure of their fitted parameters.

Results: Across recording modality, brain region, and task, we found that UoI inferred models with increased sparsity, improved stability, and qualitatively different parameter distributions, while maintaining predictive performance. We obtained highly sparse functional coupling networks with substantially different community structure, more parsimonious encoding models, and decoding models that relied on fewer single-units.

Conclusions: Together, these results demonstrate that improved parameter inference, achieved via UoI, reshapes interpretation in diverse neuroscience contexts.

Keywords: Decoding; Encoding; Functional coupling; Inference.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Algorithms*
Brain*

Abstract

Publication types

MeSH terms

Grants and funding