ArticleNature communications2023
A self-adaptive hardware with resistive switching synapses for experience-based neurocomputing.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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Who cites it
12 citing papers in PubMed.
- Analysis and Applications of Neuromorphic Memristors in Artificial Intelligence Computing.Nano-micro letters · 2026Review
- Bio-Inspired Full-Spectrum Bidirectional Optoelectronic Synaptic Transistor for In-Sensor Neuromorphic Vision.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- Sustainable Synaptic Device with Two-Dimensional Ferroelectric Materials for Neuromorphic Computing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Intrinsic gradient oxygen-driven second-order memristors for continual reinforcement learning.Nature communications · 2026Article
- Interface-Induced Synaptic Performance in CeOACS applied materials & interfaces · 2025Article
- Article
- Conductive dendrite engineering of single-crystalline two-dimensional dielectric memristors.Innovation (Cambridge (Mass.)) · 2025Article
- Actor-critic networks with analogue memristors mimicking reward-based learning.Nature machine intelligence · 2025Article
- A ferroelectric-memristor memory for both training and inference.Nature electronics · 2025Article
- Information bottleneck-based Hebbian learning rule naturally ties working memory and synaptic updates.Frontiers in computational neuroscience · 2024Article
- Adaptive structure evolution and biologically plausible synaptic plasticity for recurrent spiking neural networks.Scientific reports · 2023Article
- Open-loop analog programmable electrochemical memory array.Nature communications · 2023Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Neurobiological systems continually interact with the surrounding environment to refine their behaviour toward the best possible reward. Achieving such learning by experience is one of the main challenges of artificial intelligence, but currently it is hindered by the lack of hardware capable of plastic adaptation. Here, we propose a bio-inspired recurrent neural network, mastered by a digital system on chip with resistive-switching synaptic arrays of memory devices, which exploits homeostatic Hebbian learning for improved efficiency. All the results are discussed experimentally and theoretically, proposing a conceptual framework for benchmarking the main outcomes in terms of accuracy and resilience. To test the proposed architecture for reinforcement learning tasks, we study the autonomous exploration of continually evolving environments and verify the results for the Mars rover navigation. We also show that, compared to conventional deep learning techniques, our in-memory hardware has the potential to achieve a significant boost in speed and power-saving.
Identifiers
What OpenQuestion holds
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.