ArticleNature communications2024
High-performance deep spiking neural networks with 0.3 spikes per neuron.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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
16 citing papers in PubMed.
- Review
- Sensor-Based Tracking and Localization of In-Line Inspection Tools in Oil and Gas Pipelines: A Review.Sensors (Basel, Switzerland) · 2026Review
- Low-Latency Visuotactile Neuron Using Self-Oscillating Memristor.Small (Weinheim an der Bergstrasse, Germany) · 2026Article
- Neuromorphic Technologies for Neuroengineering: From Adaptive Stimulation to SNN-Based Inference and Deployable Biointerfaces.Sensors (Basel, Switzerland) · 2026Review
- Hierarchical abstraction drives human-like 3-D shape processing in deep learning models.PLoS computational biology · 2026Article
- Spark: modular spiking neural networks.Frontiers in artificial intelligence · 2026Article
- Adaptive and lightweight surrogate gradients: enhancing training efficiency of spiking neural networks.Frontiers in neuroscience · 2026Article
- Portfolio Optimization: A Neurodynamic Approach Based on Spiking Neural Networks.Biomimetics (Basel, Switzerland) · 2025Article
- Efficient event-based delay learning in spiking neural networks.Nature communications · 2025Article
- Deep oscillatory neural network.Scientific reports · 2025Article
- Can neuromorphic computing help reduce AI's high energy cost?Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- DelGrad: exact event-based gradients for training delays and weights on spiking neuromorphic hardware.Nature communications · 2025Article
- A multisynaptic spiking neuron for simultaneously encoding spatiotemporal dynamics.Nature communications · 2025Article
- A Spike Train Production Mechanism Based on Intermittency Dynamics.Entropy (Basel, Switzerland) · 2025Article
- Neuromorphic energy economics: toward biologically inspired and sustainable power market design.Frontiers in computational neuroscience · 2025Article
- High-performance deep spiking neural networks with 0.3 spikes per neuron.Nature communications · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Communication by rare, binary spikes is a key factor for the energy efficiency of biological brains. However, it is harder to train biologically-inspired spiking neural networks than artificial neural networks. This is puzzling given that theoretical results provide exact mapping algorithms from artificial to spiking neural networks with time-to-first-spike coding. In this paper we analyze in theory and simulation the learning dynamics of time-to-first-spike-networks and identify a specific instance of the vanishing-or-exploding gradient problem. While two choices of spiking neural network mappings solve this problem at initialization, only the one with a constant slope of the neuron membrane potential at threshold guarantees the equivalence of the training trajectory between spiking and artificial neural networks with rectified linear units. For specific image classification architectures comprising feed-forward dense or convolutional layers, we demonstrate that deep spiking neural network models can be effectively trained from scratch on MNIST and Fashion-MNIST datasets, or fine-tuned on large-scale datasets, such as CIFAR10, CIFAR100 and PLACES365, to achieve the exact same performance as that of artificial neural networks, surpassing previous spiking neural networks. Our approach accomplishes high-performance classification with less than 0.3 spikes per neuron, lending itself for an energy-efficient implementation. We also show that fine-tuning spiking neural networks with our robust gradient descent algorithm enables their optimization for hardware implementations with low latency and resilience to noise and quantization.
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.