ArticleScience advances2018
Learning of spatiotemporal patterns in a spiking neural network with resistive switching synapses.
Article in Science advances, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers.
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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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Who cites it
49 citing papers in PubMed.
- Spectrally Partitioned All-Optical Memristors for Highly Linear Neuromorphic Vision Systems.Nano-micro letters · 2026Article
- Low-Latency Visuotactile Neuron Using Self-Oscillating Memristor.Small (Weinheim an der Bergstrasse, Germany) · 2026Article
- Gradient-distributed metal-halide dynamic memristors for adaptive and robust voiceprint recognition.Nature communications · 2026Article
- High encoding-sensitivity vision sensor with complementary nonlinear neuromorphic computing.Nature communications · 2026Article
- Homeostatic dendritic neuron based on co-integrated volatile and non-volatile memristors for neuromorphic processing.Nature communications · 2026Article
- The Role of Ionic Liquids at the Biological Interfaces in Bioelectronics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Organic Transistor-Based Neuromorphic Electronics and Their Recent Applications.Small methods · 2026Review
- IGZO-Based First Spike Timing Tactile Encoders and Coupling-Enhanced Transistor Synapses for Efficient Spiking Neural Networks.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Dynamic sensor adaptation based on efferent feedback for adaptive bio-inspired sound source localization.Frontiers in neuroscience · 2026Article
- 3D stacked IGZO 2T0C DRAM array with multibit capability for computing in memory applications.Science advances · 2025Article
- A Review of Nanowire Devices Applied in Simulating Neuromorphic Computing.Nanomaterials (Basel, Switzerland) · 2025Review
- Neuromorphic Floating-Gate Memory Based on 2D Materials.Cyborg and bionic systems (Washington, D.C.) · 2025Review
- Contrastive signal-dependent plasticity: Self-supervised learning in spiking neural circuits.Science advances · 2024Article
- Advanced Neuromorphic Applications Enabled by Synaptic Ion-Gating Vertical Transistors.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Review
- An artificial visual neuron with multiplexed rate and time-to-first-spike coding.Nature communications · 2024Article
- VONature communications · 2024Article
- Artificial sensory system based on memristive devices.Exploration (Beijing, China) · 2024Review
- Artificial Intelligence Meets Flexible Sensors: Emerging Smart Flexible Sensing Systems Driven by Machine Learning and Artificial Synapses.Nano-micro letters · 2023Review
- The Enhanced Performance of Neuromorphic Computing Hardware in an ITO/ZnO/HfONanomaterials (Basel, Switzerland) · 2023Article
- SnONanomaterials (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
The human brain is a complex integrated spatiotemporal system, where space (which neuron fires) and time (when a neuron fires) both carry information to be processed by cognitive functions. To parallel the energy efficiency and computing functionality of the brain, methodologies operating over both the space and time domains are thus essential. Implementing spatiotemporal functions within nanoscale devices capable of synaptic plasticity would contribute a significant step toward constructing a large-scale neuromorphic system that emulates the computing and energy performances of the human brain. We present a neuromorphic approach to brain-like spatiotemporal computing using resistive switching synapses. To process the spatiotemporal spike pattern, time-coded spikes are reshaped into exponentially decaying signals that are fed to a McCulloch-Pitts neuron. Recognition of spike sequences is demonstrated after supervised training of a multiple-neuron network with resistive switching synapses. Finally, we show that, due to the sensitivity to precise spike timing, the spatiotemporal neural network is able to mimic the sound azimuth detection of the human brain.
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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.