ReviewMaterials (Basel, Switzerland)2019
Neuromorphic Spiking Neural Networks and Their Memristor-CMOS Hardware Implementations.
Review in Materials (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- SpiKon-E: Hybrid Soft Artificial Muscle Control Using Hardware Spiking Neural Network.Biomimetics (Basel, Switzerland) · 2025Article
- Dynamic Vision Sensor-Driven Spiking Neural Networks for Low-Power Event-Based Tracking and Recognition.Sensors (Basel, Switzerland) · 2025Article
- Self-powered artificial vibrissal system with anemotaxis behavior.Science advances · 2025Article
- Neuromorphic system using capacitor synapses.Scientific reports · 2025Article
- Neuromorphic Sensor Based on Force-Sensing Resistors.Biomimetics (Basel, Switzerland) · 2024Article
- Neuromorphic Sentiment Analysis Using Spiking Neural Networks.Sensors (Basel, Switzerland) · 2023Article
- A Compact and Low-Power SoC Design for Spiking Neural Network Based on Current Multiplier Charge Injector Synapse.Sensors (Basel, Switzerland) · 2023Article
- Dual functional states of working memory realized by memristor-based neural network.Frontiers in neuroscience · 2023Article
- Memristive Neural Networks for Predicting Seizure Activity.Sovremennye tekhnologii v meditsine · 2023Article
- Modeling and characterization of stochastic resistive switching in single AgScientific reports · 2022Article
- Towards the Neuroevolution of Low-level artificial general intelligence.Frontiers in robotics and AI · 2022Article
- Neuromorphic artificial intelligence systems.Frontiers in neuroscience · 2022Review
- A Low-Power Spiking Neural Network Chip Based on a Compact LIF Neuron and Binary Exponential Charge Injector Synapse Circuits.Sensors (Basel, Switzerland) · 2021Article
- Hardware Implementation of Differential Oscillatory Neural Networks Using VOFrontiers in neuroscience · 2021Article
- Emerging Materials for Neuromorphic Devices and Systems.iScience · 2020Review
- Memristors for Neuromorphic Circuits and Artificial Intelligence Applications.Materials (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Inspired by biology, neuromorphic systems have been trying to emulate the human brain for decades, taking advantage of its massive parallelism and sparse information coding. Recently, several large-scale hardware projects have demonstrated the outstanding capabilities of this paradigm for applications related to sensory information processing. These systems allow for the implementation of massive neural networks with millions of neurons and billions of synapses. However, the realization of learning strategies in these systems consumes an important proportion of resources in terms of area and power. The recent development of nanoscale memristors that can be integrated with Complementary Metal-Oxide-Semiconductor (CMOS) technology opens a very promising solution to emulate the behavior of biological synapses. Therefore, hybrid memristor-CMOS approaches have been proposed to implement large-scale neural networks with learning capabilities, offering a scalable and lower-cost alternative to existing CMOS systems.
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Registered trials
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