ArticleFrontiers in neuroscience2020
Synaptic Plasticity Dynamics for Deep Continuous Local Learning (DECOLLE).
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 41 papers.
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Who cites it
41 citing papers in PubMed.
- Closed-form feedback-free learning with forward projection.Nature communications · 2026Article
- Three factor delay learning rules for spiking neural networks.Frontiers in neuroscience · 2026Article
- Cone-Specific Filter-Based Neuromodulation: A Proposed Clinical Framework for Amblyopia, Strabismus, and ADHD.Clinics and practice · 2025Article
- Artificial Intelligence and Neuroscience: Transformative Synergies in Brain Research and Clinical Applications.Journal of clinical medicine · 2025Review
- Actor-critic networks with analogue memristors mimicking reward-based learning.Nature machine intelligence · 2025Article
- Probabilistic metaplasticity for continual learning with memristors in spiking networks.Scientific reports · 2024Article
- Emergence of brain-inspired small-world spiking neural network through neuroevolution.iScience · 2024Article
- Neural reshaping: the plasticity of human brain and artificial intelligence in the learning process.American journal of neurodegenerative disease · 2024Review
- Fourier or Wavelet bases as counterpart self-attention in spikformer for efficient visual classification.Frontiers in neuroscience · 2024Article
- Exploring neural oscillations during speech perception via surrogate gradient spiking neural networks.Frontiers in neuroscience · 2024Article
- Auto-Spikformer: Spikformer architecture search.Frontiers in neuroscience · 2024Article
- Scalable network emulation on analog neuromorphic hardware.Frontiers in neuroscience · 2024Article
- Spike-HAR++: an energy-efficient and lightweight parallel spiking transformer for event-based human action recognition.Frontiers in computational neuroscience · 2024Article
- Neuromorphic Photonics Circuits: Contemporary Review.Nanomaterials (Basel, Switzerland) · 2023Review
- Learnable Leakage and Onset-Spiking Self-Attention in SNNs with Local Error Signals.Sensors (Basel, Switzerland) · 2023Article
- SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence.Science advances · 2023Article
- Critically synchronized brain waves form an effective, robust and flexible basis for human memory and learning.Scientific reports · 2023Article
- Presynaptic spike-driven plasticity based on eligibility trace for on-chip learning system.Frontiers in neuroscience · 2023Article
- Efficient training of spiking neural networks with temporally-truncated local backpropagation through time.Frontiers in neuroscience · 2023Article
- First-spike coding promotes accurate and efficient spiking neural networks for discrete events with rich temporal structures.Frontiers in neuroscience · 2023Article
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3 authors.
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Abstract
A growing body of work underlines striking similarities between biological neural networks and recurrent, binary neural networks. A relatively smaller body of work, however, addresses the similarities between learning dynamics employed in deep artificial neural networks and synaptic plasticity in spiking neural networks. The challenge preventing this is largely caused by the discrepancy between the dynamical properties of synaptic plasticity and the requirements for gradient backpropagation. Learning algorithms that approximate gradient backpropagation using local error functions can overcome this challenge. Here, we introduce Deep Continuous Local Learning (DECOLLE), a spiking neural network equipped with local error functions for online learning with no memory overhead for computing gradients. DECOLLE is capable of learning deep spatio temporal representations from spikes relying solely on local information, making it compatible with neurobiology and neuromorphic hardware. Synaptic plasticity rules are derived systematically from user-defined cost functions and neural dynamics by leveraging existing autodifferentiation methods of machine learning frameworks. We benchmark our approach on the event-based neuromorphic dataset N-MNIST and DvsGesture, on which DECOLLE performs comparably to the state-of-the-art. DECOLLE networks provide continuously learning machines that are relevant to biology and supportive of event-based, low-power computer vision architectures matching the accuracies of conventional computers on tasks where temporal precision and speed are essential.
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