These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
402 related articles for article (PubMed ID: 32053106)
1. Energy efficient synaptic plasticity. Li HL; van Rossum MC Elife; 2020 Feb; 9():. PubMed ID: 32053106 [TBL] [Abstract][Full Text] [Related]
2. Evolving interpretable plasticity for spiking networks. Jordan J; Schmidt M; Senn W; Petrovici MA Elife; 2021 Oct; 10():. PubMed ID: 34709176 [TBL] [Abstract][Full Text] [Related]
3. Neural learning rules for generating flexible predictions and computing the successor representation. Fang C; Aronov D; Abbott LF; Mackevicius EL Elife; 2023 Mar; 12():. PubMed ID: 36928104 [TBL] [Abstract][Full Text] [Related]
4. Structural plasticity on an accelerated analog neuromorphic hardware system. Billaudelle S; Cramer B; Petrovici MA; Schreiber K; Kappel D; Schemmel J; Meier K Neural Netw; 2021 Jan; 133():11-20. PubMed ID: 33091719 [TBL] [Abstract][Full Text] [Related]
5. A biologically plausible supervised learning method for spiking neural networks using the symmetric STDP rule. Hao Y; Huang X; Dong M; Xu B Neural Netw; 2020 Jan; 121():387-395. PubMed ID: 31593843 [TBL] [Abstract][Full Text] [Related]
6. Overcoming Long-Term Catastrophic Forgetting Through Adversarial Neural Pruning and Synaptic Consolidation. Peng J; Tang B; Jiang H; Li Z; Lei Y; Lin T; Li H IEEE Trans Neural Netw Learn Syst; 2022 Sep; 33(9):4243-4256. PubMed ID: 33577459 [TBL] [Abstract][Full Text] [Related]
7. Synergies between intrinsic and synaptic plasticity based on information theoretic learning. Li Y; Li C PLoS One; 2013; 8(5):e62894. PubMed ID: 23671642 [TBL] [Abstract][Full Text] [Related]
10. A forecast-based STDP rule suitable for neuromorphic implementation. Davies S; Galluppi F; Rast AD; Furber SB Neural Netw; 2012 Aug; 32():3-14. PubMed ID: 22386500 [TBL] [Abstract][Full Text] [Related]
11. Synaptic weight dynamics underlying memory consolidation: implications for learning rules, circuit organization, and circuit function. Bhasin BJ; Raymond JL; Goldman MS bioRxiv; 2024 Jul; ():. PubMed ID: 38585936 [TBL] [Abstract][Full Text] [Related]
12. Mirrored STDP Implements Autoencoder Learning in a Network of Spiking Neurons. Burbank KS PLoS Comput Biol; 2015 Dec; 11(12):e1004566. PubMed ID: 26633645 [TBL] [Abstract][Full Text] [Related]
13. Sparse coding with a somato-dendritic rule. Drix D; Hafner VV; Schmuker M Neural Netw; 2020 Nov; 131():37-49. PubMed ID: 32750603 [TBL] [Abstract][Full Text] [Related]
14. Neuromorphic implementations of neurobiological learning algorithms for spiking neural networks. Walter F; Röhrbein F; Knoll A Neural Netw; 2015 Dec; 72():152-67. PubMed ID: 26422422 [TBL] [Abstract][Full Text] [Related]
15. Synaptic weight dynamics underlying memory consolidation: Implications for learning rules, circuit organization, and circuit function. Bhasin BJ; Raymond JL; Goldman MS Proc Natl Acad Sci U S A; 2024 Oct; 121(41):e2406010121. PubMed ID: 39365821 [TBL] [Abstract][Full Text] [Related]
16. Fast learning without synaptic plasticity in spiking neural networks. Subramoney A; Bellec G; Scherr F; Legenstein R; Maass W Sci Rep; 2024 Apr; 14(1):8557. PubMed ID: 38609429 [TBL] [Abstract][Full Text] [Related]
17. Octopamine integrates the status of internal energy supply into the formation of food-related memories. Berger M; Fraatz M; Auweiler K; Dorn K; El Khadrawe T; Scholz H Elife; 2024 Apr; 12():. PubMed ID: 38655926 [TBL] [Abstract][Full Text] [Related]