Resolving non-identifiability mitigates systematic errors in simultaneous models of neural tuning and functional coupling.

Overview

1. Physics Department, UC Berkeley, 110 Sproul Hall, Berkeley, CA 94720 USA
2. Redwood Center for Theoretical Neuroscience, UC Berkeley, 110 Sproul Hall, Berkeley, CA 94720 USA
3. Kavli Institute for Fundamental Neuroscience, UC San Francisco, 505 Parnassus Ave., San Francisco, CA 94143 USA
4. Biological Systems and Engineering Division, Lawrence Berkeley National Lab, 1 Cyclotron Road, Berkeley, CA 94720 USA
5. Scientific Data Division, Lawrence Berkeley National Lab, 1 Cyclotron Road, Berkeley, CA 94720 USA
6. Deptartment of Anatomy, UC San Francisco, 505 Parnassus Ave., San Francisco, CA 94143 USA
7. Departments of Physiology and Psychiatry, UC San Francisco, 505 Parnassus Ave., San Francisco, CA 94143 USA
8. Howard Hughes Medical Institute, 4000 Jones Bridge Road, Chevy Chase, MD 20815-6789 USA
9. Department of Statistics, Oregon State University, 1500 SW Jefferson Way, Corvallis, OR 97331 USA
10. Helen Wills Neuroscience Institute, UC Berkeley, 175 Li Ka Shing Center, MC#3370, Berkeley, CA 94720 USA

Abstract

A key component to understanding the brain is determining the influence of groups of neurons on each other relative to other influences. In the brain, all neurons are driven by the activity of other neurons, some of which may be 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 the inclusion of model terms for observed external variables (e.g., tuning to stimuli), observed internal variables (e.g., coupling to recorded neural activities), as well as terms for latent sources of variability. Despite broad utilization, however, evaluation of systematic errors during inference is rarely performed, and sources of systematic error are poorly understood. Through extensive numerical study and analytic calculation, we show that common inference procedures for static and dynamic models typically have systematic errors. Counter to common intuition, we found that model non-identifiability contributes to systematic errors in parameter estimation, not variance inflation, making it a particularly insidious form of statistical error. We demonstrate that accurate parameter selection before estimation resolves model non-identifiability and mitigates the associated systematic errors. In diverse neurophysiology data sets (multiple single unit recordings in primary visual cortex and hippocampus, ECoG from primary auditory cortex), we found that common methods typically overestimate the contributions of interactions between neurons, while the influence of exogenous variables is underestimated. Essentially, when there are positive correlations due to unobserved shared variability, estimated coupling amongst neurons will be inflated at the expense of tuning. We explain heterogeneity in observed systematic errors across neurophysiology data sets in terms of data statistics and experimental design. Together, our results identify the causes of statistical errors in structural equation models of simultaneous systems with endogenous, exogenous, and latent variables, provide inference procedures to mitigate those errors, and reveal and explain the impact of those errors in diverse neural data sets.

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Data

Datasets cited

• doi:10.6080/k0nc5z4x — at the source; found in the references
• doi:10.6080/k0nk3bzj — at the source; found in the references

A.11 Software and data availability

All data used in this study have been previously published and are available in public archives described in the original published works (see References). All software is publicly available at: https://github.com/BouchardLab/neuroerrors.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 2, 28 September 2026

• Publisher: — → Springer Science+Business Media

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 6 keywords, 7 MeSH terms, 5 funders, 40 references.

Cite

This paper

Sachdeva, P., Bak, J. H., Livezey, J., Kirst, C., Frank, L., Bhattacharyya, S., & Bouchard, K. E. (2026). Resolving non-identifiability mitigates systematic errors in simultaneous models of neural tuning and functional coupling. Journal of computational neuroscience, 54(2), 239-274. https://doi.org/10.1007/s10827-026-00927-8

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