Graphon Signal Processing for Spiking and Biological Neural Networks.

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος:	Graphon Signal Processing for Spiking and Biological Neural Networks.
Συγγραφείς:	Sumi T; Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, Alberta T2N 1N4, Canada takuma.sumi@ucalgary.ca., Medvedev GS; Department of Mathematics, Drexel University, Philadelphia, PA 19104, USA medvedev@drexel.edu.
Πηγή:	Neural computation [Neural Comput] 2026 May 20; Vol. 38 (6), pp. 1090-1115.
Τύπος έκδοσης:	Journal Article
Γλώσσα:	English
Στοιχεία περιοδικού:	Publisher: MIT Press Country of Publication: United States NLM ID: 9426182 Publication Model: Print Cited Medium: Internet ISSN: 1530-888X (Electronic) Linking ISSN: 08997667 NLM ISO Abbreviation: Neural Comput Subsets: MEDLINE
Imprint Name(s):	Original Publication: Cambridge, Mass. : MIT Press, c1989-
Ιατρικοί όροι (MeSH):	Action Potentials/physiology , Neurons/physiology , Nerve Net/physiology , Neural Networks, Computer , Signal Processing, Computer-Assisted* , Models, Neurological*, Animals ; Computer Simulation ; Algorithms ; Humans
Περίληψη:	Graph signal processing (GSP) extends classical signal processing to signals defined on graphs, enabling filtering, spectral analysis, and sampling of data generated by networks of various kinds. Graphon signal processing (GnSP) develops this framework further by employing the theory of graphons. Graphons are measurable functions on the unit square that represent graphs and limits of convergent graph sequences. The use of graphons provides stability of GSP methods to stochastic variability in network data and improves computational efficiency for very large networks. We use GnSP to address the stimulus identification problem (SIP) in computational and biological neural networks. The SIP is an inverse problem that aims to infer the unknown stimulus sfrom the observed network output f. We first validate the approach in spiking neural network simulations and then analyze calcium imaging recordings. Graphon-based spectral projections yield trial-invariant, low-dimensional embeddings that improve stimulus classification over principal component analysis and discrete GSP baselines. The embeddings remain stable under variations in network stochasticity, providing robustness to different network sizes and noise levels. To the best of our knowledge, this is the first application of GnSP to biological neural networks, opening new avenues for graphon-based analysis in neuroscience. (© 2026 Massachusetts Institute of Technology.)
Entry Date(s):	Date Created: 20260428 Date Completed: 20260715 Latest Revision: 20260715
Update Code:	20260716
DOI:	10.1162/NECO.a.1522
PMID:	42048399
Βάση Δεδομένων:	MEDLINE

Περιγραφή
ISSN:	1530-888X
DOI:	10.1162/NECO.a.1522