ArticleFrontiers in neuroscience2016
Unsupervised Learning by Spike Timing Dependent Plasticity in Phase Change Memory (PCM) Synapses.
Article in Frontiers in neuroscience, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed.
- Neuromorphic Devices and Computing for Sensing, Memory, and Control.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- What is next for LLMs? Pushing the boundaries of next-gen AI computing hardware with photonic chips.Nanophotonics (Berlin, Germany) · 2025Review
- SpiKon-E: Hybrid Soft Artificial Muscle Control Using Hardware Spiking Neural Network.Biomimetics (Basel, Switzerland) · 2025Article
- Scalable Microscale Artificial Synapses of Lead Halide Perovskite with Femtojoule Energy Consumption.ACS energy letters · 2024Article
- Synchronized stepwise control of firing and learning thresholds in a spiking randomly connected neural network toward hardware implementation.Frontiers in neuroscience · 2024Article
- A scalable solution recipe for a Ag-based neuromorphic device.Discover nano · 2023Article
- Ionic-electronic halide perovskite memdiodes enabling neuromorphic computing with a second-order complexity.Science advances · 2022Article
- Low-Consumption Synaptic Devices Based on Gate-All-Around InAs Nanowire Field-Effect Transistors.Nanoscale research letters · 2022Article
- Palimpsest memories stored in memristive synapses.Science advances · 2022Article
- Structural and Electrical Properties of Annealed GeNanomaterials (Basel, Switzerland) · 2022Article
- Chalcogenide optomemristors for multi-factor neuromorphic computation.Nature communications · 2022Article
- Voltage-dependent synaptic plasticity: Unsupervised probabilistic Hebbian plasticity rule based on neurons membrane potential.Frontiers in neuroscience · 2022Article
- Post-silicon nano-electronic device and its application in brain-inspired chips.Frontiers in neurorobotics · 2022Review
- Core-Shell Dual-Gate Nanowire Charge-Trap Memory for Synaptic Operations for Neuromorphic Applications.Nanomaterials (Basel, Switzerland) · 2021Article
- Article
- Neuromorphic Devices for Bionic Sensing and Perception.Frontiers in neuroscience · 2021Review
- A Brain-Inspired Homeostatic Neuron Based on Phase-Change Memories for Efficient Neuromorphic Computing.Frontiers in neuroscience · 2021Article
- Considerations for Neuromorphic Supercomputing in Semiconducting and Superconducting Optoelectronic Hardware.Frontiers in neuroscience · 2021Article
- Non-linear Memristive Synaptic Dynamics for Efficient Unsupervised Learning in Spiking Neural Networks.Frontiers in neuroscience · 2021Article
- Integration and Co-design of Memristive Devices and Algorithms for Artificial Intelligence.iScience · 2020Review
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We present a novel one-transistor/one-resistor (1T1R) synapse for neuromorphic networks, based on phase change memory (PCM) technology. The synapse is capable of spike-timing dependent plasticity (STDP), where gradual potentiation relies on set transition, namely crystallization, in the PCM, while depression is achieved via reset or amorphization of a chalcogenide active volume. STDP characteristics are demonstrated by experiments under variable initial conditions and number of pulses. Finally, we support the applicability of the 1T1R synapse for learning and recognition of visual patterns by simulations of fully connected neuromorphic networks with 2 or 3 layers with high recognition efficiency. The proposed scheme provides a feasible low-power solution for on-line unsupervised machine learning in smart reconfigurable sensors.
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