Analyzing an organism's sensors using Maximum Entropy (MaxEnt) models with bias, variance, and confusion matrices.

Overview

1. Department of Natural Sciences, Scripps and Pitzer Colleges, Claremont, California, United States of America
2. School of Arts and Sciences, Tufts University, Medford, Massachusetts, United States of America
3. Pioneer Academics, Jenkintown, Pennsylvania, United States of America
4. Université Claude Bernard Lyon1, CNRS UMR5292, INSERM U1028, Sleep Team, Center for Research in Neuroscience of LYON, Bron, France
5. Department of Clinical Neurophysiology, University of Twente, Enschede, The Netherlands
6. Kravis Department of Integrated Sciences, Claremont McKenna College, Claremont, California, United States of America
7. National Institute for Theory and Mathematics in Biology, Chicago, Illinois, United States of America

Abstract

Biological organisms have sensors that communicate information about the environment. Analyzing how well these biological sensors function has usually been done with mutual information between the sensor signal and the environment, but that can be computationally intractable and summarize something quite complex with just a single number. We suggest that alternatively, one may profitably analyze these biosensors using bias and variance or confusion matrices, depending on the kind of environment. Stimulus-dependent Maximum Entropy models are used to develop estimators of the environmental state given the sensor state, and these estimators in turn are then used to calculate either the bias and variance of the estimator or confusion matrices. We focus on several examples to understand the utility of non-information-based analyses: ligand-receptor binding models spanning from genetic regulation to neuronal communication to bacterial chemotaxis and spin-glass Ising models for neural activity in cultured neurons. These new computationally efficient analyses add insight into existing analyses based on mutual information; in particular, mutual information estimates give one number to characterize responses to all environmental inputs, and this analysis method characterizes how sensors respond to each environmental input. Categorical analyses, meanwhile, indicate the presence of memory without much prediction in confusion matrix elements in cultured neural networks, adding to previous understanding from mutual information estimates.

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

Code

The paper links to its data, not to its authors' code: see the Data section.

The paper's code and data availability statement is in the Data section.

Tracing map

A tracing map links a paper to the code its authors published: this paper has none, so it has no map.

Data

Datasets cited

• github.com/chrisw47/maxent-mempred-confusionmatrix — at github.com; found in “Data Availability”

Data Availability

Data is available at https://github.com/chrisw47/maxent-mempred-confusionmatrix.

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

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 3, 28 September 2026

• Funding: added Alfred P. Sloan Foundation: G-2024-22395; Air Force Office of Scientific Research: FA9550, FA9550-19-1-0411

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 6 MeSH terms, 27 references.

Cite

This paper

Wang, C., Schimke, E., Kako, T., Gao, A., Lamberti, M., le Feber, J., & Marzen, S. (2026). Analyzing an organism's sensors using Maximum Entropy (MaxEnt) models with bias, variance, and confusion matrices. PloS one, 21(8), e0342165. https://doi.org/10.1371/journal.pone.0342165

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1016/j.patter.2026.101619 [code]

Sampling bias corrections for discrete and Gaussian partial information decompositions.

Journal: Patterns (New York, N.Y.)

In common: 5 references

[2] doi:10.1016/j.bpr.2026.100278

Information flow in cultured neuronal networks by transfer entropy.

Journal: Biophysical reports

In common: 2 references

[3] doi:10.1371/journal.pone.0348005 [code]

THOI: An efficient and accessible library for computing higher-order interactions enhanced by batch-processing.

Journal: PloS one

In common: 1 reference

[4] doi:10.1016/j.isci.2026.115689 [code]

Unperturbed dye-based imaging of spontaneous synchronized calcium activity in iPSC-derived neuronal cultures.

Journal: iScience

In common: 1 reference

[5] doi:10.1007/s10827-026-00927-8 [code]

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

Journal: Journal of computational neuroscience

In common: 1 reference

[6] doi:10.3390/e28070769

Correlation-Induced Accessibility Bridges in Biomedical Networks: A Proof-of-Concept Relational Graph Model.

Journal: Entropy (Basel, Switzerland)

In common: 1 reference

[7] doi:10.1371/journal.pone.0348608

A novel approach for the EEG-driven assessment of divided attention through mutual information theory: A case study at the wheel.

Journal: PloS one

In common: 1 reference

[8] doi:10.1098/rstb.2024.0461 [code]

Shallow recurrent decoders for neural and behavioural dynamics.

Journal: Philosophical transactions of the Royal Society of London. Series B, Biological sciences

In common: 1 reference

[9] doi:10.1093/pnasnexus/pgag224 [code]

Preserving predictive information under biologically plausible compression.

Journal: PNAS nexus

In common: 1 reference

[10] doi:10.1038/s41567-026-03306-3 [code]

Simple input-output dependencies explain neuronal activity.

Journal: Nature physics

In common: 1 reference

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.