ArticleFrontiers in neuroscience2017
Learning to Generate Sequences with Combination of Hebbian and Non-hebbian Plasticity in Recurrent Spiking Neural Networks.
Article in Frontiers in neuroscience, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 32 citations in OpenAlex.
- Article
- Spiking Recurrent Neural Networks Represent Task-Relevant Neural Sequences in Rule-Dependent Computation.Cognitive computation · 2023Article
- Correcting the hebbian mistake: Toward a fully error-driven hippocampus.PLoS computational biology · 2022Article
- Heterogeneous Ensemble-Based Spike-Driven Few-Shot Online Learning.Frontiers in neuroscience · 2022Article
- Stimulus-Driven and Spontaneous Dynamics in Excitatory-Inhibitory Recurrent Neural Networks for Sequence Representation.Neural computation · 2021Article
- Gamma Oscillations Facilitate Effective Learning in Excitatory-Inhibitory Balanced Neural Circuits.Neural plasticity · 2021Article
- Review
- Bio-Inspired Evolutionary Model of Spiking Neural Networks in Ionic Liquid Space.Frontiers in neuroscience · 2019Article
- Reinforcement Learning With Low-Complexity Liquid State Machines.Frontiers in neuroscience · 2019Article
- Analysis of Liquid Ensembles for Enhancing the Performance and Accuracy of Liquid State Machines.Frontiers in neuroscience · 2019Article
- Information-Theoretic Intrinsic Plasticity for Online Unsupervised Learning in Spiking Neural Networks.Frontiers in neuroscience · 2019Article
- Learning to Recognize Actions From Limited Training Examples Using a Recurrent Spiking Neural Model.Frontiers in neuroscience · 2018Article
- SpiLinC: Spiking Liquid-Ensemble Computing for Unsupervised Speech and Image Recognition.Frontiers in neuroscience · 2018Article
- Deep Learning With Spiking Neurons: Opportunities and Challenges.Frontiers in neuroscience · 2018Review
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Synaptic Plasticity, the foundation for learning and memory formation in the human brain, manifests in various forms. Here, we combine the standard spike timing correlation based Hebbian plasticity with a non-Hebbian synaptic decay mechanism for training a recurrent spiking neural model to generate sequences. We show that inclusion of the adaptive decay of synaptic weights with standard STDP helps learn stable contextual dependencies between temporal sequences, while reducing the strong attractor states that emerge in recurrent models due to feedback loops. Furthermore, we show that the combined learning scheme suppresses the chaotic activity in the recurrent model substantially, thereby enhancing its' ability to generate sequences consistently even in the presence of perturbations.
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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.