mlsanityData to 7 Oct 2026

Eligibility Traces and Plasticity on Behavioral Time Scales: Experimental Support of neoHebbian Three-Factor Learning Rules

arXiv 1801.05219 ↗First posted 16 Jan 2018q-bio.NCcited by 64

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Abstract (arXiv)

Most elementary behaviors such as moving the arm to grasp an object or walking into the next room to explore a museum evolve on the time scale of seconds; in contrast, neuronal action potentials occur on the time scale of a few milliseconds. Learning rules of the brain must therefore bridge the gap between these two different time scales. Modern theories of synaptic plasticity have postulated that the co-activation of pre- and postsynaptic neurons sets a flag at the synapse, called an eligibility trace, that leads to a weight change only if an additional factor is present while the flag is set. This third factor, signaling reward, punishment, surprise, or novelty, could be implemented by the phasic activity of neuromodulators or specific neuronal inputs signaling special events. While the theoretical framework has been developed over the last decades, experimental evidence in support of eligibility traces on the time scale of seconds has been collected only during the last few years. Here we review, in the context of three-factor rules of synaptic plasticity, four key experiments that support the role of synaptic eligibility traces in combination with a third factor as a biological implementation of neoHebbian three-factor learning rules.

Key facts. Cited by 64 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Jan 2018.

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Citing papers per year: 2021: 4; 2022: 13; 2023: 11; 2024: 7; 2025: 16; 2026: 13.

64 papers.

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  1. 2021Sparse Spiking Gradient Descentcited by 41
  2. 2023Brain-inspired learning in artificial neural networks: a reviewcited by 26
  3. 2023Neuro-Modulated Hebbian Learning for Fully Test-Time Adaptationcited by 23
  4. 2021Bottom-up and top-down approaches for the design of neuromorphic processing systems: Tradeoffs and synergies between natural and artificial intelligencecited by 21
  5. 2023Exploiting Noise as a Resource for Computation and Learning in Spiking Neural Networkscited by 17
  6. 2022Deep Reinforcement Learning with Spiking Q-learningcited by 15
  7. 2022Bayesian Continual Learning via Spiking Neural Networkscited by 12
  8. 2024Fully Spiking Actor Network with Intra-layer Connections for Reinforcement Learningcited by 10
  9. 2022Meta-learning Spiking Neural Networks with Surrogate Gradient Descentcited by 9
  10. 2022Learning efficient backprojections across cortical hierarchies in real timecited by 8
  11. 2023Multiplexed gradient descent: Fast online training of modern datasets on hardware neural networks without backpropagationcited by 8
  12. 2023Spiking Neural Networks and Bio-Inspired Supervised Deep Learning: A Surveycited by 8
38 more
  1. 2021Supervised training of spiking neural networks for robust deployment on mixed-signal neuromorphic processorscited by 7
  2. 2022A taxonomy of surprise definitionscited by 7
  3. 2023S-TLLR: STDP-inspired Temporal Local Learning Rule for Spiking Neural Networkscited by 7
  4. 2023Synaptic Plasticity Models and Bio-Inspired Unsupervised Deep Learning: A Surveycited by 7
  5. 2022Distinguishing Learning Rules with Brain Machine Interfacescited by 5
  6. 2022Short-Term Plasticity Neurons Learning to Learn and Forgetcited by 5
  7. 2021SpikePropamine: Differentiable Plasticity in Spiking Neural Networkscited by 4
  8. 2025TESS: A Scalable Temporally and Spatially Local Learning Rule for Spiking Neural Networkscited by 4
  9. 2025Proxy Target: Bridging the Gap Between Discrete Spiking Neural Networks and Continuous Controlcited by 4
  10. 2022Beyond accuracy: generalization properties of bio-plausible temporal credit assignment rulescited by 3
  11. 2023Synaptic motor adaptation: A three-factor learning rule for adaptive robotic control in spiking neural networkscited by 3
  12. 2025Three-Factor Learning in Spiking Neural Networks: An Overview of Methods and Trends from a Machine Learning Perspectivecited by 3
  13. 2022Learning to learn online with neuromodulated synaptic plasticity in spiking neural networkscited by 2
  14. 2023A Neuromorphic Architecture for Reinforcement Learning from Real-Valued Observationscited by 2
  15. 2023Estimating Post-Synaptic Effects for Online Training of Feed-Forward SNNscited by 2
  16. 2024Benchmarking Spiking Neural Network Learning Methods with Varying Localitycited by 2
  17. 2026Modern Neuromorphic AI: From Intra-Token to Inter-Token Processingcited by 2
  18. 2022Including STDP to eligibility propagation in multi-layer recurrent spiking neural networkscited by 1
  19. 2022A Computational Model of Learning Flexible Navigation in a Maze by Layout-Conforming Replay of Place Cellscited by 1
  20. 2022Emergent Computations in Trained Artificial Neural Networks and Real Brainscited by 1
  21. 2024Emerging NeoHebbian Dynamics in Forward-Forward Learning: Implications for Neuromorphic Computingcited by 1
  22. 2025A Truly Sparse and General Implementation of Gradient-Based Synaptic Plasticitycited by 1
  23. 2025Can Biologically Plausible Temporal Credit Assignment Rules Match BPTT for Neural Similarity? E-prop as an Examplecited by 1
  24. 2025Traces Propagation: Memory-Efficient and Scalable Forward-Only Learning in Spiking Neural Networkscited by 1
  25. 2025Self-Evidencing Through Hierarchical Gradient Decomposition: A Dissipative System That Maintains Non-Equilibrium Steady-State by Minimizing Variational Free Energycited by 1
  26. 2026Three factor delay learning rules for spiking neural networkscited by 1
  27. 2026Self-supervised local learning rules learn the hidden hierarchical structure of high-dimensional datacited by 1
  28. 2022A Computational Theory of Learning Flexible Reward-Seeking Behavior with Place Cellscited by 0
  29. 2023Neuromorphic Online Learning for Spatiotemporal Patterns with a Forward-only Timelinecited by 0
  30. 2024Learning-to-learn enables rapid learning with phase-change memory-based in-memory computingcited by 0
  31. 2024Emulating Brain-like Rapid Learning in Neuromorphic Edge Computingcited by 0
  32. 2024Stimulus-to-Stimulus Learning in RNNs with Cortical Inductive Biasescited by 0
  33. 2024Recurrent Joint Embedding Predictive Architecture with Recurrent Forward Propagation Learningcited by 0
  34. 2025Scaling of hardware-compatible perturbative training algorithmscited by 0
  35. 2025Noise-based reward-modulated learningcited by 0
  36. 2025Multi-Plasticity Synergy with Adaptive Mechanism Assignment for Training Spiking Neural Networkscited by 0
  37. 2025Flexible inference for animal learning rules using neural networkscited by 0
  38. 2025Does Feedback Alignment Work at Biological Timescales?cited by 0

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