ArticleNature communications2022
Introducing principles of synaptic integration in the optimization of deep neural networks.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed, 30 citations in OpenAlex.
- Advances and Perspectives in Graphene-Based Quantum Dots Enabled Neuromorphic Devices.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Multisensory Neuromorphic Devices: From Physics to Integration.Nano-micro letters · 2026Review
- Personalized brain models link cognitive decline progression to underlying synaptic and connectivity degeneration.Alzheimer's research & therapy · 2025Article
- Synaptic Transistors Based on PVA: Chitosan Biopolymer Blended Electric-Double-Layer with High Ionic Conductivity.Polymers · 2023Article
- Explaining cocktail party effect and McGurk effect with a spiking neural network improved by Motif-topology.Frontiers in neuroscience · 2023Article
- Introducing principles of synaptic integration in the optimization of deep neural networks.Nature communications · 2022Article
- Editorial: Closed-loop iterations between neuroscience and artificial intelligence.Frontiers in systems neuroscience · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Plasticity circuits in the brain are known to be influenced by the distribution of the synaptic weights through the mechanisms of synaptic integration and local regulation of synaptic strength. However, the complex interplay of stimulation-dependent plasticity with local learning signals is disregarded by most of the artificial neural network training algorithms devised so far. Here, we propose a novel biologically inspired optimizer for artificial and spiking neural networks that incorporates key principles of synaptic plasticity observed in cortical dendrites: GRAPES (Group Responsibility for Adjusting the Propagation of Error Signals). GRAPES implements a weight-distribution-dependent modulation of the error signal at each node of the network. We show that this biologically inspired mechanism leads to a substantial improvement of the performance of artificial and spiking networks with feedforward, convolutional, and recurrent architectures, it mitigates catastrophic forgetting, and it is optimally suited for dedicated hardware implementations. Overall, our work indicates that reconciling neurophysiology insights with machine intelligence is key to boosting the performance of neural networks.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.