Shared Latent Coordinates and Multi-Observable Phase-Diagram Placement Yield Directly Comparable Mechanistic Fingerprints of Whole-Brain Dynamics

Julian Kedys, Cezary Mazurek
Proceedings of the First Workshop on NeuroAI Multimodal Intelligence @ AAAI 2026, PMLR 308:205-213, 2026.

Abstract

We present a methods-first framework that turns high-dimensional population neural recordings into directly comparable, mechanistic fingerprints at the level of individual subjects. Our pipeline (i) constructs population-universal, shared latent coordinates that align heterogeneous subjects into a common representational space; (ii) fits pairwise maximum-entropy (Ising) models on binarised latent trajectories with rigorous convergence- and uncertainty-diagnostics; (iii) performs energy-landscape analysis (ELA) to obtain interpretable minima, barriers and kinetic descriptors; and (iv) introduces a new, variance-balanced multi-observable phase-diagram analysis (PDA) that places many subjects - including systematically heterogeneous sub-groups - onto a shared Sherrington-Kirkpatrick (SK) reference surface with uncertainty, making cross-subject comparisons direct and faithful. In a cohort of rodent whole-brain imaging time series (sensitive third-party data), our placement costs are typically 10e-6 - 10e-4, with tight bootstrap confidence regions and consistent ordering across pooled and subgroup references; estimated SK parameters fall in s = 0.155-0.320, u = -0.013 to +0.031. The result is a compact, uncertainty-aware subject “fingerprint” comprising ELA and kinetic descriptors together with the subject’s location on the phase diagram. This paper focuses on methodological reliability and cross-subject comparability; external replications on public datasets remain future work.

Cite this Paper

BibTeX
@InProceedings{pmlr-v308-kedys26a, title = {Shared Latent Coordinates and Multi-Observable Phase-Diagram Placement Yield Directly Comparable Mechanistic Fingerprints of Whole-Brain Dynamics}, author = {Kedys, Julian and Mazurek, Cezary}, booktitle = {Proceedings of the First Workshop on NeuroAI Multimodal Intelligence @ AAAI 2026}, pages = {205--213}, year = {2026}, editor = {Abbasi-Asl, Reza and Iqbal, Asim and Ito, Shinya and Arkhipov, Anton and Sanborn, Sophia}, volume = {308}, series = {Proceedings of Machine Learning Research}, month = {27 Jan}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v308/main/assets/kedys26a/kedys26a.pdf}, url = {https://proceedings.mlr.press/v308/kedys26a.html}, abstract = {We present a methods-first framework that turns high-dimensional population neural recordings into directly comparable, mechanistic fingerprints at the level of individual subjects. Our pipeline (i) constructs population-universal, shared latent coordinates that align heterogeneous subjects into a common representational space; (ii) fits pairwise maximum-entropy (Ising) models on binarised latent trajectories with rigorous convergence- and uncertainty-diagnostics; (iii) performs energy-landscape analysis (ELA) to obtain interpretable minima, barriers and kinetic descriptors; and (iv) introduces a new, variance-balanced multi-observable phase-diagram analysis (PDA) that places many subjects - including systematically heterogeneous sub-groups - onto a shared Sherrington-Kirkpatrick (SK) reference surface with uncertainty, making cross-subject comparisons direct and faithful. In a cohort of rodent whole-brain imaging time series (sensitive third-party data), our placement costs are typically 10e-6 - 10e-4, with tight bootstrap confidence regions and consistent ordering across pooled and subgroup references; estimated SK parameters fall in s = 0.155-0.320, u = -0.013 to +0.031. The result is a compact, uncertainty-aware subject “fingerprint” comprising ELA and kinetic descriptors together with the subject’s location on the phase diagram. This paper focuses on methodological reliability and cross-subject comparability; external replications on public datasets remain future work.} }
Endnote
%0 Conference Paper %T Shared Latent Coordinates and Multi-Observable Phase-Diagram Placement Yield Directly Comparable Mechanistic Fingerprints of Whole-Brain Dynamics %A Julian Kedys %A Cezary Mazurek %B Proceedings of the First Workshop on NeuroAI Multimodal Intelligence @ AAAI 2026 %C Proceedings of Machine Learning Research %D 2026 %E Reza Abbasi-Asl %E Asim Iqbal %E Shinya Ito %E Anton Arkhipov %E Sophia Sanborn %F pmlr-v308-kedys26a %I PMLR %P 205--213 %U https://proceedings.mlr.press/v308/kedys26a.html %V 308 %X We present a methods-first framework that turns high-dimensional population neural recordings into directly comparable, mechanistic fingerprints at the level of individual subjects. Our pipeline (i) constructs population-universal, shared latent coordinates that align heterogeneous subjects into a common representational space; (ii) fits pairwise maximum-entropy (Ising) models on binarised latent trajectories with rigorous convergence- and uncertainty-diagnostics; (iii) performs energy-landscape analysis (ELA) to obtain interpretable minima, barriers and kinetic descriptors; and (iv) introduces a new, variance-balanced multi-observable phase-diagram analysis (PDA) that places many subjects - including systematically heterogeneous sub-groups - onto a shared Sherrington-Kirkpatrick (SK) reference surface with uncertainty, making cross-subject comparisons direct and faithful. In a cohort of rodent whole-brain imaging time series (sensitive third-party data), our placement costs are typically 10e-6 - 10e-4, with tight bootstrap confidence regions and consistent ordering across pooled and subgroup references; estimated SK parameters fall in s = 0.155-0.320, u = -0.013 to +0.031. The result is a compact, uncertainty-aware subject “fingerprint” comprising ELA and kinetic descriptors together with the subject’s location on the phase diagram. This paper focuses on methodological reliability and cross-subject comparability; external replications on public datasets remain future work.
APA
Kedys, J. & Mazurek, C.. (2026). Shared Latent Coordinates and Multi-Observable Phase-Diagram Placement Yield Directly Comparable Mechanistic Fingerprints of Whole-Brain Dynamics. Proceedings of the First Workshop on NeuroAI Multimodal Intelligence @ AAAI 2026, in Proceedings of Machine Learning Research 308:205-213 Available from https://proceedings.mlr.press/v308/kedys26a.html.

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