ReviewFrontiers in neuroscience2022
Neuromorphic artificial intelligence systems.
Review in Frontiers in neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 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
19 citing papers in PubMed.
- Celestial neuromorphics based on ferroelectric gallium oxide.National science review · 2026Article
- Modeling and classifying neuronal activity with a fusion of mathematical and machine learning techniques.BMC bioinformatics · 2026Article
- On the molecular basis of enduring memory in neurons, and cell fate in fibroblasts.Nucleic acids research · 2026Review
- Recent Advances and Perspectives on Field-Effect Transistors for Artificial Visual Neuromorphic Systems.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Virtualization as a New Scaling Law for Semiconductor Devices Beyond Geometric Scaling.Small (Weinheim an der Bergstrasse, Germany) · 2026Article
- Polyacid Solutions as an Analogue of a Neural Network.Polymers · 2026Article
- Three-factor learning in spiking neural networks: An overview of methods and trends from a machine learning perspective.Patterns (New York, N.Y.) · 2025Review
- Article
- Alzheimer's disease and other memory disorders in the age of AI: reflection and perspectives on the 120th anniversary of the birth of Dr. John von Neumann.GeroScience · 2025Review
- ON-OFF neuromorphic ISING machines using Fowler-Nordheim annealers.Nature communications · 2025Article
- Neural network compression for reinforcement learning tasks.Scientific reports · 2025Article
- Bridging neuromorphic computing and deep learning for next-generation neural data interpretation.Frontiers in computational neuroscience · 2025Article
- Neuromorphic neuromodulation: Towards the next generation of closed-loop neurostimulation.PNAS nexus · 2024Review
- Application of Event Cameras and Neuromorphic Computing to VSLAM: A Survey.Biomimetics (Basel, Switzerland) · 2024Review
- Convolutional spiking neural networks for intent detection based on anticipatory brain potentials using electroencephalogram.Scientific reports · 2024Article
- Neurochallenges in smart cities: state-of-the-art, perspectives, and research directions.Frontiers in neuroscience · 2024Review
- Deep reinforcement learning with significant multiplications inference.Scientific reports · 2023Article
- Overview of Spiking Neural Network Learning Approaches and Their Computational Complexities.Sensors (Basel, Switzerland) · 2023Review
- Efficient parameter calibration and real-time simulation of large-scale spiking neural networks with GeNN and NEST.Frontiers in neuroinformatics · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Modern artificial intelligence (AI) systems, based on von Neumann architecture and classical neural networks, have a number of fundamental limitations in comparison with the mammalian brain. In this article we discuss these limitations and ways to mitigate them. Next, we present an overview of currently available neuromorphic AI projects in which these limitations are overcome by bringing some brain features into the functioning and organization of computing systems (TrueNorth, Loihi, Tianjic, SpiNNaker, BrainScaleS, NeuronFlow, DYNAP, Akida, Mythic). Also, we present the principle of classifying neuromorphic AI systems by the brain features they use: connectionism, parallelism, asynchrony, impulse nature of information transfer, on-device-learning, local learning, sparsity, analog, and in-memory computing. In addition to reviewing new architectural approaches used by neuromorphic devices based on existing silicon microelectronics technologies, we also discuss the prospects for using a new memristor element base. Examples of recent advances in the use of memristors in neuromorphic applications are also given.
Indexed as
Identifiers
What 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.