Stable recurrent dynamics in heterogeneous neuromorphic computing systems using excitatory and inhibitory plasticity

Maryada (), Saray Soldado-Magraner, Martino Sorbaro, Rodrigo Laje, Dean V. Buonomano and Giacomo Indiveri
Additional contact information
Maryada: University of Zurich and ETH Zurich
Saray Soldado-Magraner: University of California
Martino Sorbaro: University of Zurich and ETH Zurich
Rodrigo Laje: Universidad Nacional de Quilmes
Dean V. Buonomano: University of California
Giacomo Indiveri: University of Zurich and ETH Zurich

Nature Communications, 2025, vol. 16, issue 1, 1-13

Abstract: Abstract Many neural computations emerge from self-sustained patterns of activity in recurrent neural circuits, which rely on balanced excitation and inhibition. Neuromorphic electronic circuits represent a promising approach for implementing the brain’s computational primitives. However, achieving the same robustness of biological networks in neuromorphic systems remains a challenge due to the variability in their analog components. Inspired by real cortical networks, we apply a biologically-plausible cross-homeostatic rule to balance neuromorphic implementations of spiking recurrent networks. We demonstrate how this rule can autonomously tune the network to produce robust, self-sustained dynamics in an inhibition-stabilized regime, even in presence of device mismatch. It can implement multiple, co-existing stable memories, with emergent soft-winner-take-all and reproduce the “paradoxical effect” observed in cortical circuits. In addition to validating neuroscience models on a substrate sharing many similar limitations with biological systems, this enables the automatic configuration of ultra-low power, mixed-signal neuromorphic technologies despite the large chip-to-chip variability.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-025-60697-2 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-60697-2

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-025-60697-2

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().