| Autor: |
Sachdeva P; Physics Department, UC Berkeley.; Redwood Center for Theoretical Neuroscience, UC Berkeley., Bak JH; Kavli Institute for Fundamental Neuroscience, UC San Francisco.; Biological Systems and Engineering Division, Lawrence Berkeley National Lab., Livezey J; Biological Systems and Engineering Division, Lawrence Berkeley National Lab., Kirst C; Kavli Institute for Fundamental Neuroscience, UC San Francisco.; Scientific Data Division, Lawrence Berkeley National Lab.; Deptartment of Anatomy, UC San Francisco., Frank L; Kavli Institute for Fundamental Neuroscience, UC San Francisco.; Departments of Physiology and Psychiatry, UC San Francisco.; Howard Hughes Medical Institute., Bhattacharyya S; Department of Statistics; Oregon State University., Bouchard KE; Redwood Center for Theoretical Neuroscience, UC Berkeley.; Kavli Institute for Fundamental Neuroscience, UC San Francisco.; Biological Systems and Engineering Division, Lawrence Berkeley National Lab.; Scientific Data Division, Lawrence Berkeley National Lab.; Helen Wills Neuroscience Institute, UC Berkeley. |
| Jazyk: |
angličtina |
| Zdroj: |
BioRxiv : the preprint server for biology [bioRxiv] 2023 Jul 12. Date of Electronic Publication: 2023 Jul 12. |
| DOI: |
10.1101/2023.07.11.548615 |
| Abstrakt: |
In the brain, all neurons are driven by the activity of other neurons, some of which maybe simultaneously recorded, but most are not. As such, models of neuronal activity need to account for simultaneously recorded neurons and the influences of unmeasured neurons. This can be done through inclusion of model terms for observed external variables (e.g., tuning to stimuli) as well as terms for latent sources of variability. Determining the influence of groups of neurons on each other relative to other influences is important to understand brain functioning. The parameters of statistical models fit to data are commonly used to gain insight into the relative importance of those influences. Scientific interpretation of models hinge upon unbiased parameter estimates. However, evaluation of biased inference is rarely performed and sources of bias are poorly understood. Through extensive numerical study and analytic calculation, we show that common inference procedures and models are typically biased. We demonstrate that accurate parameter selection before estimation resolves model non-identifiability and mitigates bias. In diverse neurophysiology data sets, we found that contributions of coupling to other neurons are often overestimated while tuning to exogenous variables are underestimated in common methods. We explain heterogeneity in observed biases across data sets in terms of data statistics. Finally, counter to common intuition, we found that model non-identifiability contributes to bias, not variance, making it a particularly insidious form of statistical error. Together, our results identify the causes of statistical biases in common models of neural data, provide inference procedures to mitigate that bias, and reveal and explain the impact of those biases in diverse neural data sets. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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