ReviewNature2019
Towards spike-based machine intelligence with neuromorphic computing.
Review in Nature, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 276 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
276 citing papers in PubMed.
- Multifunctional Vertical Bulk-Heterojunction Transistors With Amorphous GaOAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Amplitude-controllable event-driven organic photosensors based on ionic-mediated inhibition.Nature materials · 2026Article
- Viscoelastic nanomechanical devices for neuromorphic information processing.Science advances · 2026Article
- Light-tunable charge dynamics enable ultralow-power bidirectional lead-free perovskite synapses for intelligent vision.Science advances · 2026Article
- Defect-Engineered Floating-Gate Synapses With Programmable Relaxation Dynamics for Multi-Timescale Neuromorphic Computing.Small (Weinheim an der Bergstrasse, Germany) · 2026Article
- Directional Spike Feature Learning with Progressive Reweighting for Energy-Efficient Cross-View Geo-Localization.Sensors (Basel, Switzerland) · 2026Article
- Neuromorphic Optical Tracking and Imaging of Randomly Moving Targets Through Dynamic Dense Scattering Media.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- A Reconfigurable Memristive Spiking Neuron Enabling Advanced Neuromorphic Computing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Analysis and Applications of Neuromorphic Memristors in Artificial Intelligence Computing.Nano-micro letters · 2026Review
- Electrical Characterization of Mesh-Structured Floating-Gate Neuromorphic Transistors with Varying Mesh Sizes.Micromachines · 2026Article
- Neuromorphic Cardiac Sensing: A Bio-Inspired Spiking Neural Network with Sensory-Adaptive Encoding for Energy-Efficient Arrhythmia Detection from ECG and PPG Signals.Biomimetics (Basel, Switzerland) · 2026Article
- Anion-Engineered Organic Electrochemical Transistors With Multi-Timescale Synaptic Dynamics for Task-Adaptive Spiking Neural Networks.Small (Weinheim an der Bergstrasse, Germany) · 2026Article
- Charge-Encoded Sidechains Enable Deterministic Ion Ingress and Memory Retention in Organic Electrochemical Synaptic Transistors.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- Task-State fMRI-Derived Whole-Brain Functional Topology-Constrained Spiking Neural Network with an Embedded Auditory Core Circuit for Speech Recognition.Biomimetics (Basel, Switzerland) · 2026Article
- Cross-modal applications of a neuromorphic olfactory learning algorithm.bioRxiv : the preprint server for biology · 2026Article
- Amorphous metal-oxide semiconductor thin-film neuron, synapse, and neuromorphic system.Scientific reports · 2026Article
- A ferroelectric-ionic-trapping transistor for low power and secure neuromorphic computing.Nature communications · 2026Article
- Self-Oscillatory Neuron-like Devices for Unconventional Computing Applications.Chemical reviews · 2026Review
- Neuromorphic Near-Sensor and In-Sensor Computing Enabled by Next-Generation Material-Based Sensors.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Dopamine-Mediated Attenuation of OECT-Based Aqueous Artificial Chemical Synapses.ACS applied materials & interfaces · 2026Article
216 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Guided by brain-like 'spiking' computational frameworks, neuromorphic computing-brain-inspired computing for machine intelligence-promises to realize artificial intelligence while reducing the energy requirements of computing platforms. This interdisciplinary field began with the implementation of silicon circuits for biological neural routines, but has evolved to encompass the hardware implementation of algorithms with spike-based encoding and event-driven representations. Here we provide an overview of the developments in neuromorphic computing for both algorithms and hardware and highlight the fundamentals of learning and hardware frameworks. We discuss the main challenges and the future prospects of neuromorphic computing, with emphasis on algorithm-hardware codesign.
Indexed as
Identifiers
31776490What 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.