Continuous learning of spiking networks trained with local rules

Author:

Antonov D.I.ORCID,Sviatov K.V.ORCID,Sukhov S.ORCID

Funder

Russian Foundation for Basic Research

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience

Reference71 articles.

1. Online continual learning with maximal interfered retrieval;Aljundi;Advances in Neural Information Processing Systems,2019

2. Gradient based sample selection for online continual learning;Aljundi;Advances in Neural Information Processing Systems,2019

3. Selfless sequential learning;Aljundi,2018

4. Controlled forgetting: Targeted stimulation and dopaminergic plasticity modulation for unsupervised lifelong learning in spiking neural networks;Allred;Frontiers in Neuroscience,2020

5. Pseudo-recursal: Solving the catastrophic forgetting problem in deep neural networks;Atkinson,2018

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Spiking generative networks empowered by multiple dynamic experts for lifelong learning;Expert Systems with Applications;2024-03

2. Brain-inspired neural circuit evolution for spiking neural networks;Proceedings of the National Academy of Sciences;2023-09-20

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