ArticleNature communications2021
An adaptive threshold neuron for recurrent spiking neural networks with nanodevice hardware implementation.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 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
20 citing papers in PubMed.
- Acetylcholine enhances deviance detection in Hodgkin-Huxley neuronal networks.Cognitive neurodynamics · 2026Article
- Damage-Free Passivation of Ambipolar OECTs with Fluoropolymer Film for Enhanced Performance and Stability Toward Reliable Biosignal Processing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- A Reconfigurable Memristive Spiking Neuron Enabling Advanced Neuromorphic Computing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Homeostatic dendritic neuron based on co-integrated volatile and non-volatile memristors for neuromorphic processing.Nature communications · 2026Article
- Algorithm-hardware co-design of neuromorphic networks with dual memory pathways.Nature machine intelligence · 2026Article
- Backpropagation through space, time and the brain.Nature communications · 2025Article
- Dynamic Vision Sensor-Driven Spiking Neural Networks for Low-Power Event-Based Tracking and Recognition.Sensors (Basel, Switzerland) · 2025Article
- Efficient and robust temporal processing with neural oscillations modulated spiking neural networks.Nature communications · 2025Article
- A multisynaptic spiking neuron for simultaneously encoding spatiotemporal dynamics.Nature communications · 2025Article
- Selective UV Sensing for Energy-Efficient UV-A Artificial Synapses Using a ZnO/ZnGaSmall (Weinheim an der Bergstrasse, Germany) · 2025Article
- Cardiac Heterogeneity Prediction by Cardio-Neural Network Simulation.Neuroinformatics · 2025Article
- Computational elements based on coupled VONature communications · 2024Article
- Exploring neural oscillations during speech perception via surrogate gradient spiking neural networks.Frontiers in neuroscience · 2024Article
- Spontaneous Threshold Lowering Neuron using Second-Order Diffusive Memristor for Self-Adaptive Spatial Attention.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Article
- A neuromorphic physiological signal processing system based on VONature communications · 2023Article
- A survey and perspective on neuromorphic continual learning systems.Frontiers in neuroscience · 2023Review
- Adaptive cognition implemented with a context-aware and flexible neuron for next-generation artificial intelligence.PNAS nexus · 2022Article
- Extended liquid state machines for speech recognition.Frontiers in neuroscience · 2022Article
- A surrogate gradient spiking baseline for speech command recognition.Frontiers in neuroscience · 2022Article
- Ionic liquid multistate resistive switching characteristics in two terminal soft and flexible discrete channels for neuromorphic computing.Microsystems & nanoengineering · 2022Article
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
We propose a Double EXponential Adaptive Threshold (DEXAT) neuron model that improves the performance of neuromorphic Recurrent Spiking Neural Networks (RSNNs) by providing faster convergence, higher accuracy and a flexible long short-term memory. We present a hardware efficient methodology to realize the DEXAT neurons using tightly coupled circuit-device interactions and experimentally demonstrate the DEXAT neuron block using oxide based non-filamentary resistive switching devices. Using experimentally extracted parameters we simulate a full RSNN that achieves a classification accuracy of 96.1% on SMNIST dataset and 91% on Google Speech Commands (GSC) dataset. We also demonstrate full end-to-end real-time inference for speech recognition using real fabricated resistive memory circuit based DEXAT neurons. Finally, we investigate the impact of nanodevice variability and endurance illustrating the robustness of DEXAT based RSNNs.
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.