Eligibility Traces and Plasticity on Behavioral Time Scales: Experimental Support of neoHebbian Three-Factor Learning Rules
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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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50 most cited of 64 All 64 (JSON)
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- 2025A Unified Perspective on Optimization in Machine Learning and Neuroscience: From Gradient Descent to Neural Adaptation21 Oct 2025
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- 2025Self-Evidencing Through Hierarchical Gradient Decomposition: A Dissipative System That Maintains Non-Equilibrium Steady-State by Minimizing Variational Free Energy20 Oct 2025
- 2025Traces Propagation: Memory-Efficient and Scalable Forward-Only Learning in Spiking Neural Networks16 Sep 2025
- 2025Flexible inference for animal learning rules using neural networks4 Sep 2025
- 2025Multi-Plasticity Synergy with Adaptive Mechanism Assignment for Training Spiking Neural Networks19 Aug 2025
- 2025Can Biologically Plausible Temporal Credit Assignment Rules Match BPTT for Neural Similarity? E-prop as an Example7 Jun 2025
- 2025Proxy Target: Bridging the Gap Between Discrete Spiking Neural Networks and Continuous Control30 May 2025
- 2025Three-Factor Learning in Spiking Neural Networks: An Overview of Methods and Trends from a Machine Learning Perspective6 Apr 2025
- 2025Noise-based reward-modulated learning31 Mar 2025
- 2025TESS: A Scalable Temporally and Spatially Local Learning Rule for Spiking Neural Networks3 Feb 2025
- 2025Scaling of hardware-compatible perturbative training algorithms26 Jan 2025
- 2025A Truly Sparse and General Implementation of Gradient-Based Synaptic Plasticity20 Jan 2025
- 2024Recurrent Joint Embedding Predictive Architecture with Recurrent Forward Propagation Learning10 Nov 2024
- 2024Stimulus-to-Stimulus Learning in RNNs with Cortical Inductive Biases20 Sep 2024
- 2024Emulating Brain-like Rapid Learning in Neuromorphic Edge Computing28 Aug 2024
- 2024Emerging NeoHebbian Dynamics in Forward-Forward Learning: Implications for Neuromorphic Computing24 Jun 2024
- 2024Learning-to-learn enables rapid learning with phase-change memory-based in-memory computing22 Apr 2024
- 2024Benchmarking Spiking Neural Network Learning Methods with Varying Locality1 Feb 2024
- 2024Fully Spiking Actor Network with Intra-layer Connections for Reinforcement Learning9 Jan 2024
- 2023Estimating Post-Synaptic Effects for Online Training of Feed-Forward SNNs7 Nov 2023
- 2023Synaptic Plasticity Models and Bio-Inspired Unsupervised Deep Learning: A Survey30 Jul 2023
- 2023Spiking Neural Networks and Bio-Inspired Supervised Deep Learning: A Survey30 Jul 2023
- 2023Neuromorphic Online Learning for Spatiotemporal Patterns with a Forward-only Timeline21 Jul 2023
- 2023A Neuromorphic Architecture for Reinforcement Learning from Real-Valued Observations6 Jul 2023
- 2023S-TLLR: STDP-inspired Temporal Local Learning Rule for Spiking Neural Networks27 Jun 2023
- 2023Synaptic motor adaptation: A three-factor learning rule for adaptive robotic control in spiking neural networks2 Jun 2023
- 2023Exploiting Noise as a Resource for Computation and Learning in Spiking Neural Networks25 May 2023
- 2023Brain-inspired learning in artificial neural networks: a review18 May 2023
- 2023Multiplexed gradient descent: Fast online training of modern datasets on hardware neural networks without backpropagation5 Mar 2023
- 2023Neuro-Modulated Hebbian Learning for Fully Test-Time Adaptation2 Mar 2023
- 2022Learning efficient backprojections across cortical hierarchies in real time20 Dec 2022
- 2022Emergent Computations in Trained Artificial Neural Networks and Real Brains9 Dec 2022
- 2022A Computational Model of Learning Flexible Navigation in a Maze by Layout-Conforming Replay of Place Cells18 Sep 2022
50 newest of 64 All 64 (JSON)
- 2021Sparse Spiking Gradient Descentcited by 41
- 2023Brain-inspired learning in artificial neural networks: a reviewcited by 26
- 2023Neuro-Modulated Hebbian Learning for Fully Test-Time Adaptationcited by 23
- 2021Bottom-up and top-down approaches for the design of neuromorphic processing systems: Tradeoffs and synergies between natural and artificial intelligencecited by 21
- 2023Exploiting Noise as a Resource for Computation and Learning in Spiking Neural Networkscited by 17
- 2022Deep Reinforcement Learning with Spiking Q-learningcited by 15
- 2022Bayesian Continual Learning via Spiking Neural Networkscited by 12
- 2024Fully Spiking Actor Network with Intra-layer Connections for Reinforcement Learningcited by 10
- 2022Meta-learning Spiking Neural Networks with Surrogate Gradient Descentcited by 9
- 2022Learning efficient backprojections across cortical hierarchies in real timecited by 8
- 2023Multiplexed gradient descent: Fast online training of modern datasets on hardware neural networks without backpropagationcited by 8
- 2023Spiking Neural Networks and Bio-Inspired Supervised Deep Learning: A Surveycited by 8
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- 2026Common-Mode Collapse and Recovery in Direct Feedback Alignment25 Sep 2026
- 2026Gradland: On Phenomenal Experience, Differentiated Across Many Dimensions8 Sep 2026
- 2026Effective pruning of task-trained recurrent neural networks using noisy fluctuations and connection rescaling5 Aug 2026
- 2026Provably Sub-Linear Two-Timescale NeuroEvolution with Online Plasticity18 Jun 2026
- 2026Score Broadcast and Decorrelation: A General Framework for Broadcast-Based Credit Assignment28 May 2026
- 2026Self-supervised local learning rules learn the hidden hierarchical structure of high-dimensional data18 May 2026
- 2026Scalable Learning in Structured Recurrent Spiking Neural Networks without Backpropagation1 May 2026
- 2026NeuroPlastic: A Plasticity-Modulated Optimizer for Biologically Inspired Learning Dynamics29 Apr 2026
- 2026Spark: Modular Spiking Neural Networks2 Feb 2026
- 2026Error Amplification Limits ANN-to-SNN Conversion in Continuous Control29 Jan 2026
- 2026Can Local Learning Match Self-Supervised Backpropagation?29 Jan 2026
- 2026Three factor delay learning rules for spiking neural networks2 Jan 2026
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