ArticleFrontiers in neuroscience2020
Toward Scalable, Efficient, and Accurate Deep Spiking Neural Networks With Backward Residual Connections, Stochastic Softmax, and Hybridization.
Article in Frontiers in neuroscience, 2020. 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.
- Low-power Spiking Neural Network audio source localisation using a Hilbert Transform audio event encoding scheme.Communications engineering · 2025Article
- Nmix: a hybrid deep learning model for precise prediction of 2'-O-methylation sites based on multi-feature fusion and ensemble learning.Briefings in bioinformatics · 2024Article
- Neuromorphic intermediate representation: A unified instruction set for interoperable brain-inspired computing.Nature communications · 2024Article
- Spike-based dynamic computing with asynchronous sensing-computing neuromorphic chip.Nature communications · 2024Article
- Rethinking skip connections in Spiking Neural Networks with Time-To-First-Spike coding.Frontiers in neuroscience · 2024Article
- Integrating Spatial and Temporal Information for Violent Activity Detection from Video Using Deep Spiking Neural Networks.Sensors (Basel, Switzerland) · 2023Article
- Direct training high-performance spiking neural networks for object recognition and detection.Frontiers in neuroscience · 2023Article
- Efficient human activity recognition with spatio-temporal spiking neural networks.Frontiers in neuroscience · 2023Article
- EXODUS: Stable and efficient training of spiking neural networks.Frontiers in neuroscience · 2023Article
- Backpropagation with biologically plausible spatiotemporal adjustment for training deep spiking neural networks.Patterns (New York, N.Y.) · 2022Article
- A surrogate gradient spiking baseline for speech command recognition.Frontiers in neuroscience · 2022Article
- MONETA: A Processing-In-Memory-Based Hardware Platform for the Hybrid Convolutional Spiking Neural Network With Online Learning.Frontiers in neuroscience · 2022Article
- Toward Reflective Spiking Neural Networks Exploiting Memristive Devices.Frontiers in computational neuroscience · 2022Review
- Visual explanations from spiking neural networks using inter-spike intervals.Scientific reports · 2021Article
- Revisiting Batch Normalization for Training Low-Latency Deep Spiking Neural Networks From Scratch.Frontiers in neuroscience · 2021Article
- Spatio-Temporal Sequential Memory Model With Mini-Column Neural Network.Frontiers in neuroscience · 2021Article
- Integration and Co-design of Memristive Devices and Algorithms for Artificial Intelligence.iScience · 2020Review
- Spike-Train Level Direct Feedback Alignment: Sidestepping Backpropagation for On-Chip Training of Spiking Neural Nets.Frontiers in neuroscience · 2020Article
- A Heterogeneous Spiking Neural Network for Unsupervised Learning of Spatiotemporal Patterns.Frontiers in neuroscience · 2020Article
- Toward Scalable, Efficient, and Accurate Deep Spiking Neural Networks With Backward Residual Connections, Stochastic Softmax, and Hybridization.Frontiers in neuroscience · 2020Article
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
Spiking Neural Networks (SNNs) may offer an energy-efficient alternative for implementing deep learning applications. In recent years, there have been several proposals focused on supervised (conversion, spike-based gradient descent) and unsupervised (spike timing dependent plasticity) training methods to improve the accuracy of SNNs on large-scale tasks. However, each of these methods suffer from
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.