ArticleNature communications2022
Chalcogenide optomemristors for multi-factor neuromorphic computation.
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 11 papers.
What it found
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Who cites it
11 citing papers in PubMed.
- NeoHebbian synapses to accelerate online training of neuromorphic hardware.Scientific reports · 2026Article
- Organic Electrochemical Transistors for Neuromorphic Devices and Applications.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- SpikeAEC: a neuromodulation-based spiking controller for explore-exploit balancing in mobile robots.Frontiers in neurorobotics · 2026Article
- Recent Advances in Optoelectronic Synaptic Devices for Neuromorphic Computing.Biomimetics (Basel, Switzerland) · 2025Review
- Phase-Change Memory for In-Memory Computing.Chemical reviews · 2025Review
- Unsupervised post-training learning in spiking neural networks.Scientific reports · 2025Article
- Memristive Ion Dynamics to Enable Biorealistic Computing.Chemical reviews · 2025Review
- Actor-critic networks with analogue memristors mimicking reward-based learning.Nature machine intelligence · 2025Article
- Energy-Efficient Integrated Electro-Optic Memristors.Nano letters · 2024Article
- Memory-electroluminescence for multiple action-potentials combination in bio-inspired afferent nerves.Nature communications · 2024Article
- Cellular mechanisms of cooperative context-sensitive predictive inference.Current research in neurobiology · 2024Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Neuromorphic hardware that emulates biological computations is a key driver of progress in AI. For example, memristive technologies, including chalcogenide-based in-memory computing concepts, have been employed to dramatically accelerate and increase the efficiency of basic neural operations. However, powerful mechanisms such as reinforcement learning and dendritic computation require more advanced device operations involving multiple interacting signals. Here we show that nano-scaled films of chalcogenide semiconductors can perform such multi-factor in-memory computation where their tunable electronic and optical properties are jointly exploited. We demonstrate that ultrathin photoactive cavities of Ge-doped Selenide can emulate synapses with three-factor neo-Hebbian plasticity and dendrites with shunting inhibition. We apply these properties to solve a maze game through on-device reinforcement learning, as well as to provide a single-neuron solution to linearly inseparable XOR implementation.
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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.