Author ORCID Identifier
https://orcid.org/0009-0007-0066-9918
Abstract
We propose an extension of renormalization into the domain of spiking neural networks, thereby providing a novel framework for coarse-graining neural networks without disrupting their critical properties. The proposed coarse-graining technique merges neurons and synaptic connections based on a graph-theoretic distance derived from synaptic weight strength and is configured to effectively prune the reservoir size while preserving the scale-free spiking dynamics indicative of criticality. Criticality in spiking neural networks may provide information-theoretic advantages by optimizing information processing and sensitivity to input. Using time-series prediction benchmarks, we demonstrate that networks operating at criticality exhibit up to 32% higher prediction accuracy before coarse-graining and up to 30% higher prediction accuracy after coarse-graining when compared to non critical networks for a sufficiently complex task.
Recommended Citation
Mastin, Tucker X. and Teuscher, Christof
(2025)
"Coarse-Graining Spiking Reservoirs: Reducing Reservoir Size While Preserving Critical Dynamics,"
Northeast Journal of Complex Systems (NEJCS): Vol. 7
:
No.
2
, Article 2.
DOI: https://doi.org/10.63562/2577-8439.1106
Available at:
https://orb.binghamton.edu/nejcs/vol7/iss2/2
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