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PreprintCC BY 4.0PsyArXiv

Structure Guides Function: Beyond Pairwise Neural Interactions

  • Alexander Maier
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Abstract

A central theme in Jon Kaas’ research, celebrated in this issue, is the relationship between brain structure and function. As a tribute, we examine how certain neuroanatomical structures enable higher-order (higher arity) functions whose joint effects cannot be recovered from pairwise measurements. Specifically, we argue that neurons exploit coincident input detection to realize higher-order operations that cannot be derived by studying any of the pairwise combinations in isolation. Pairwise statistics can miss these irreducible higher order (more-than-pairwise) effects. We advocate for an extension of graph-based models to hypergraphs to formally capture and analyze these irreducible higher-order neural interactions. Based on several recent studies, we discuss both the applicability and benefits of higher order neural data analyses.

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Cite

BibTeXRIS
BibTeX
@misc{maier2026structure,
  title = {{Structure Guides Function: Beyond Pairwise Neural Interactions}},
  author = {Alexander Maier},
  howpublished = {PsyArXiv},
  year = {2026},
  month = sep,
  doi = {10.31234/osf.io/hpvsa_v2},
  url = {https://osf.io/preprints/psyarxiv/hpvsa_v2/},
  note = {CC BY 4.0},
}
RIS
TY  - UNPB
TI  - Structure Guides Function: Beyond Pairwise Neural Interactions
AU  - Alexander Maier
PY  - 2026
DA  - 2026/09/03
PB  - PsyArXiv
DO  - 10.31234/osf.io/hpvsa_v2
UR  - https://osf.io/preprints/psyarxiv/hpvsa_v2/
AB  - A central theme in Jon Kaas’ research, celebrated in this issue, is the relationship between brain structure and function. As a tribute, we examine how certain neuroanatomical structures enable higher-order (higher arity) functions whose joint effects cannot be recovered from pairwise measurements. Specifically, we argue that neurons exploit coincident input detection to realize higher-order operations that cannot be derived by studying any of the pairwise combinations in isolation. Pairwise statistics can miss these irreducible higher order (more-than-pairwise) effects. We advocate for an extension of graph-based models to hypergraphs to formally capture and analyze these irreducible higher-order neural interactions. Based on several recent studies, we discuss both the applicability and benefits of higher order neural data analyses.
ER  -