Improved inference in coupling, encoding, and decoding models and its consequence for neuroscientific interpretation.
J Neurosci Methods
; 358: 109195, 2021 07 01.
Article
en En
| MEDLINE
| ID: mdl-33905791
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. NEWMETHOD:
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., â1-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.Palabras clave
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Base de datos:
MEDLINE
Asunto principal:
Algoritmos
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Tipo de estudio:
Prognostic_studies
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Risk_factors_studies
Idioma:
En
Revista:
J Neurosci Methods
Año:
2021
Tipo del documento:
Article
País de afiliación:
Estados Unidos