ArticleNature communications2022
Self-organization of an inhomogeneous memristive hardware for sequence learning.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Analysis and Applications of Neuromorphic Memristors in Artificial Intelligence Computing.Nano-micro letters · 2026Review
- Article
- A free energy landscape analysis of resistance fluctuations in a memristive device.Nature materials · 2026Article
- Exploring synergies: Advancing neuroscience with machine learning.Signal processing · 2026Article
- Neuromorphic computing at scale.Nature · 2025Review
- A tunable multi-timescale Indium-Gallium-Zinc-Oxide thin-film transistor neuron towards hybrid solutions for spiking neuromorphic applications.Communications engineering · 2024Article
- Synchronized stepwise control of firing and learning thresholds in a spiking randomly connected neural network toward hardware implementation.Frontiers in neuroscience · 2024Article
- Online dynamical learning and sequence memory with neuromorphic nanowire networks.Nature communications · 2023Article
- Implementation of Kalman Filtering with Spiking Neural Networks.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
- Erratum issued
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Learning is a fundamental component of creating intelligent machines. Biological intelligence orchestrates synaptic and neuronal learning at multiple time scales to self-organize populations of neurons for solving complex tasks. Inspired by this, we design and experimentally demonstrate an adaptive hardware architecture Memristive Self-organizing Spiking Recurrent Neural Network (MEMSORN). MEMSORN incorporates resistive memory (RRAM) in its synapses and neurons which configure their state based on Hebbian and Homeostatic plasticity respectively. For the first time, we derive these plasticity rules directly from the statistical measurements of our fabricated RRAM-based neurons and synapses. These "technologically plausible" learning rules exploit the intrinsic variability of the devices and improve the accuracy of the network on a sequence learning task by 30%. Finally, we compare the performance of MEMSORN to a fully-randomly-set-up spiking recurrent network on the same task, showing that self-organization improves the accuracy by more than 15%. This work demonstrates the importance of the device-circuit-algorithm co-design approach for implementing brain-inspired computing hardware.
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Registered trials
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