Semi-parametric Memory Consolidation: Towards Brain-like Deep Continual Learning

• URL: http://arxiv.org/abs/2504.14727v1
• Date: Sun, 20 Apr 2025 19:53:13 GMT
• Title: Semi-parametric Memory Consolidation: Towards Brain-like Deep Continual Learning
• Authors: Geng Liu, Fei Zhu, Rong Feng, Zhiqiang Yi, Shiqi Wang, Gaofeng Meng, Zhaoxiang Zhang,
• Abstract summary: We propose a novel biomimetic continual learning framework that integrates semi-parametric memory and the wake-sleep consolidation mechanism.<n>For the first time, our method enables deep neural networks to retain high performance on novel tasks while maintaining prior knowledge in real-world challenging continual learning scenarios.
• Score: 59.35015431695172
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Humans and most animals inherently possess a distinctive capacity to continually acquire novel experiences and accumulate worldly knowledge over time. This ability, termed continual learning, is also critical for deep neural networks (DNNs) to adapt to the dynamically evolving world in open environments. However, DNNs notoriously suffer from catastrophic forgetting of previously learned knowledge when trained on sequential tasks. In this work, inspired by the interactive human memory and learning system, we propose a novel biomimetic continual learning framework that integrates semi-parametric memory and the wake-sleep consolidation mechanism. For the first time, our method enables deep neural networks to retain high performance on novel tasks while maintaining prior knowledge in real-world challenging continual learning scenarios, e.g., class-incremental learning on ImageNet. This study demonstrates that emulating biological intelligence provides a promising path to enable deep neural networks with continual learning capabilities.

Related papers

• Hybrid Learners Do Not Forget: A Brain-Inspired Neuro-Symbolic Approach to Continual Learning [20.206972068340843]
  Continual learning is crucial for creating AI agents that can learn and improve themselves autonomously.<n>Inspired by the two distinct systems in the human brain, we propose a Neuro-Symbolic Brain-Inspired Continual Learning framework.
  arXiv  Detail & Related papers  (2025-03-16T20:09:19Z)
• Continual Learning with Neuromorphic Computing: Theories, Methods, and Applications [5.213243471774097]
  Continual learning is proposed for enabling autonomous systems to acquire new knowledge and adapt to changing environments. Neuromorphic computing, with brain-inspired Spiking Neural Networks (SNNs), offers inherent advantages for enabling low-power/energy continual learning.
  arXiv  Detail & Related papers  (2024-10-11T19:49:53Z)
• Enhancing learning in spiking neural networks through neuronal heterogeneity and neuromodulatory signaling [52.06722364186432]
  We propose a biologically-informed framework for enhancing artificial neural networks (ANNs) Our proposed dual-framework approach highlights the potential of spiking neural networks (SNNs) for emulating diverse spiking behaviors. We outline how the proposed approach integrates brain-inspired compartmental models and task-driven SNNs, bioinspiration and complexity.
  arXiv  Detail & Related papers  (2024-07-05T14:11:28Z)
• Neuro-mimetic Task-free Unsupervised Online Learning with Continual Self-Organizing Maps [56.827895559823126]
  Self-organizing map (SOM) is a neural model often used in clustering and dimensionality reduction. We propose a generalization of the SOM, the continual SOM, which is capable of online unsupervised learning under a low memory budget. Our results, on benchmarks including MNIST, Kuzushiji-MNIST, and Fashion-MNIST, show almost a two times increase in accuracy.
  arXiv  Detail & Related papers  (2024-02-19T19:11:22Z)
• Hebbian Learning based Orthogonal Projection for Continual Learning of Spiking Neural Networks [74.3099028063756]
  We develop a new method with neuronal operations based on lateral connections and Hebbian learning. We show that Hebbian and anti-Hebbian learning on recurrent lateral connections can effectively extract the principal subspace of neural activities. Our method consistently solves for spiking neural networks with nearly zero forgetting.
  arXiv  Detail & Related papers  (2024-02-19T09:29:37Z)
• Brain-Inspired Machine Intelligence: A Survey of Neurobiologically-Plausible Credit Assignment [65.268245109828]
  We examine algorithms for conducting credit assignment in artificial neural networks that are inspired or motivated by neurobiology. We organize the ever-growing set of brain-inspired learning schemes into six general families and consider these in the context of backpropagation of errors. The results of this review are meant to encourage future developments in neuro-mimetic systems and their constituent learning processes.
  arXiv  Detail & Related papers  (2023-12-01T05:20:57Z)
• Towards continual task learning in artificial neural networks: current approaches and insights from neuroscience [0.0]
  The innate capacity of humans and other animals to learn a diverse, and often interfering, range of knowledge is a hallmark of natural intelligence. The ability of artificial neural networks to learn across a range of tasks and domains is a clear goal of artificial intelligence.
  arXiv  Detail & Related papers  (2021-12-28T13:50:51Z)
• Learning to acquire novel cognitive tasks with evolution, plasticity and meta-meta-learning [3.8073142980733]
  In meta-learning, networks are trained with external algorithms to learn tasks that require acquiring, storing and exploiting unpredictable information for each new instance of the task. Here we evolve neural networks, endowed with plastic connections, over a sizable set of simple meta-learning tasks based on a neuroscience modelling framework. The resulting evolved network can automatically acquire a novel simple cognitive task, never seen during training, through the spontaneous operation of its evolved neural organization and plasticity structure.
  arXiv  Detail & Related papers  (2021-12-16T03:18:01Z)
• Reducing Catastrophic Forgetting in Self Organizing Maps with Internally-Induced Generative Replay [67.50637511633212]
  A lifelong learning agent is able to continually learn from potentially infinite streams of pattern sensory data. One major historic difficulty in building agents that adapt is that neural systems struggle to retain previously-acquired knowledge when learning from new samples. This problem is known as catastrophic forgetting (interference) and remains an unsolved problem in the domain of machine learning to this day.
  arXiv  Detail & Related papers  (2021-12-09T07:11:14Z)
• Learning offline: memory replay in biological and artificial reinforcement learning [1.0136215038345011]
  We review the functional roles of replay in the fields of neuroscience and AI. Replay is important for memory consolidation in biological neural networks. It is also key to stabilising learning in deep neural networks.
  arXiv  Detail & Related papers  (2021-09-21T08:57:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.