ArticleNature communications2024
The backpropagation algorithm implemented on spiking neuromorphic hardware.
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.
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Who cites it
16 citing papers in PubMed.
- Neuromorphic Devices and Computing for Sensing, Memory, and Control.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Programmable Speech Recognition Based on Cu/CuBiSeACS omega · 2026Article
- Cellular and subcellular specialization enables biology-constrained deep learning.Cell reports · 2026Article
- A highly energy-efficient multi-core neuromorphic architecture for training deep spiking neural networks.Nature communications · 2026Article
- Artificial intelligence-driven nano-enhanced stem cell therapy for neurodegenerative diseases: from rational design to clinical translation.Journal of nanobiotechnology · 2026Review
- Learned adaptive properties for mitigation of weight perturbations in embedded spiking networks.Frontiers in neuroscience · 2026Article
- AI-Driven Monitoring for Fish Welfare in Aquaponics: A Predictive Approach.Sensors (Basel, Switzerland) · 2025Article
- Learning and inference with correlated neural variability.PNAS nexus · 2025Article
- DelGrad: exact event-based gradients for training delays and weights on spiking neuromorphic hardware.Nature communications · 2025Article
- Emulating sensation by bridging neuromorphic computing and multisensory integration.Patterns (New York, N.Y.) · 2025Review
- An Introduction to Generative Artificial Intelligence for Academics.F1000Research · 2025Article
- Foveal vision reduces neural resources in agent-based game learning.Frontiers in neuroscience · 2025Article
- Mapless mobile robot navigation at the edge using self-supervised cognitive map learners.Frontiers in robotics and AI · 2024Article
- Editorial: Understanding and bridging the gap between neuromorphic computing and machine learning, volume II.Frontiers in computational neuroscience · 2024Article
- SENECA: building a fully digital neuromorphic processor, design trade-offs and challenges.Frontiers in neuroscience · 2023Article
- Toward robust and scalable deep spiking reinforcement learning.Frontiers in neurorobotics · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
The capabilities of natural neural systems have inspired both new generations of machine learning algorithms as well as neuromorphic, very large-scale integrated circuits capable of fast, low-power information processing. However, it has been argued that most modern machine learning algorithms are not neurophysiologically plausible. In particular, the workhorse of modern deep learning, the backpropagation algorithm, has proven difficult to translate to neuromorphic hardware. This study presents a neuromorphic, spiking backpropagation algorithm based on synfire-gated dynamical information coordination and processing implemented on Intel's Loihi neuromorphic research processor. We demonstrate a proof-of-principle three-layer circuit that learns to classify digits and clothing items from the MNIST and Fashion MNIST datasets. To our knowledge, this is the first work to show a Spiking Neural Network implementation of the exact backpropagation algorithm that is fully on-chip without a computer in the loop. It is competitive in accuracy with off-chip trained SNNs and achieves an energy-delay product suitable for edge computing. This implementation shows a path for using in-memory, massively parallel neuromorphic processors for low-power, low-latency implementation of modern deep learning applications.
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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.