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

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

doi:10.1016/j.jneumeth.2021.109195

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

Journal Article · Thu Jul 01 00:00:00 EDT 2021 · Journal of Neuroscience Methods

DOI:https://doi.org/10.1016/j.jneumeth.2021.109195· OSTI ID:1784410

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

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. We used algorithms based on Union of Intersections, a statistical inference framework based on stability principles, capable of improved selection and estimation. 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. 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. Together, these results demonstrate that improved parameter inference, achieved via UoI, reshapes interpretation in diverse neuroscience contexts.

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Research Organization:: Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Biological and Environmental Research (BER)

Grant/Contract Number:: R01DA045783; R01MH101697; R01NS078435; AC02-05CH11231

OSTI ID:: 1784410

Alternate ID(s):: OSTI ID: 1826715

Journal Information:: Journal of Neuroscience Methods, Journal Name: Journal of Neuroscience Methods Vol. 358 Journal Issue: C; ISSN 0165-0270

Publisher:: ElsevierCopyright Statement

Country of Publication:: Netherlands

Language:: English

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