Stable recurrent dynamics in heterogeneous neuromorphic computing systems using excitatory and inhibitory plasticity
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...
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| Format: | Article |
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Nature Portfolio
2025-07-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-60697-2 |
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| author | Maryada Saray Soldado-Magraner Martino Sorbaro Rodrigo Laje Dean V. Buonomano Giacomo Indiveri |
| author_facet | Maryada Saray Soldado-Magraner Martino Sorbaro Rodrigo Laje Dean V. Buonomano Giacomo Indiveri |
| author_sort | Maryada |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-045f3121f9c74a23adfe5ebecdc80bed |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-045f3121f9c74a23adfe5ebecdc80bed2025-08-20T04:01:41ZengNature PortfolioNature Communications2041-17232025-07-0116111310.1038/s41467-025-60697-2Stable recurrent dynamics in heterogeneous neuromorphic computing systems using excitatory and inhibitory plasticityMaryada0Saray Soldado-Magraner1Martino Sorbaro2Rodrigo Laje3Dean V. Buonomano4Giacomo Indiveri5Institute of Neuroinformatics, University of Zurich and ETH ZurichDepartment of Neurobiology, University of CaliforniaInstitute of Neuroinformatics, University of Zurich and ETH ZurichDepartment of Science and Technology, Universidad Nacional de QuilmesDepartment of Neurobiology, University of CaliforniaInstitute of Neuroinformatics, University of Zurich and ETH ZurichAbstract 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.https://doi.org/10.1038/s41467-025-60697-2 |
| spellingShingle | Maryada Saray Soldado-Magraner Martino Sorbaro Rodrigo Laje Dean V. Buonomano Giacomo Indiveri Stable recurrent dynamics in heterogeneous neuromorphic computing systems using excitatory and inhibitory plasticity Nature Communications |
| title | Stable recurrent dynamics in heterogeneous neuromorphic computing systems using excitatory and inhibitory plasticity |
| title_full | Stable recurrent dynamics in heterogeneous neuromorphic computing systems using excitatory and inhibitory plasticity |
| title_fullStr | Stable recurrent dynamics in heterogeneous neuromorphic computing systems using excitatory and inhibitory plasticity |
| title_full_unstemmed | Stable recurrent dynamics in heterogeneous neuromorphic computing systems using excitatory and inhibitory plasticity |
| title_short | Stable recurrent dynamics in heterogeneous neuromorphic computing systems using excitatory and inhibitory plasticity |
| title_sort | stable recurrent dynamics in heterogeneous neuromorphic computing systems using excitatory and inhibitory plasticity |
| url | https://doi.org/10.1038/s41467-025-60697-2 |
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