Incorporating Neuro-Inspired Adaptability for Continual Learning in Artificial Intelligence

• URL: http://arxiv.org/abs/2308.14991v2
• Date: Thu, 9 Nov 2023 12:00:49 GMT
• Title: Incorporating Neuro-Inspired Adaptability for Continual Learning in Artificial Intelligence
• Authors: Liyuan Wang, Xingxing Zhang, Qian Li, Mingtian Zhang, Hang Su, Jun Zhu, Yi Zhong
• Abstract summary: Continual learning aims to empower artificial intelligence with strong adaptability to the real world. Existing advances mainly focus on preserving memory stability to overcome catastrophic forgetting. We propose a generic approach that appropriately attenuates old memories in parameter distributions to improve learning plasticity.
• Score: 59.11038175596807
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Continual learning aims to empower artificial intelligence (AI) with strong adaptability to the real world. For this purpose, a desirable solution should properly balance memory stability with learning plasticity, and acquire sufficient compatibility to capture the observed distributions. Existing advances mainly focus on preserving memory stability to overcome catastrophic forgetting, but remain difficult to flexibly accommodate incremental changes as biological intelligence (BI) does. By modeling a robust Drosophila learning system that actively regulates forgetting with multiple learning modules, here we propose a generic approach that appropriately attenuates old memories in parameter distributions to improve learning plasticity, and accordingly coordinates a multi-learner architecture to ensure solution compatibility. Through extensive theoretical and empirical validation, our approach not only clearly enhances the performance of continual learning, especially over synaptic regularization methods in task-incremental settings, but also potentially advances the understanding of neurological adaptive mechanisms, serving as a novel paradigm to progress AI and BI together.

Related papers

• Super Level Sets and Exponential Decay: A Synergistic Approach to Stable Neural Network Training [0.0]
  We develop a dynamic learning rate algorithm that integrates exponential decay and advanced anti-overfitting strategies. We prove that the superlevel sets of the loss function, as influenced by our adaptive learning rate, are always connected.
  arXiv  Detail & Related papers  (2024-09-25T09:27:17Z)
• Neuromimetic metaplasticity for adaptive continual learning [2.1749194587826026]
  We propose a metaplasticity model inspired by human working memory to achieve catastrophic forgetting-free continual learning. A key aspect of our approach involves implementing distinct types of synapses from stable to flexible, and randomly intermixing them to train synaptic connections with different degrees of flexibility. The model achieved a balanced tradeoff between memory capacity and performance without requiring additional training or structural modifications.
  arXiv  Detail & Related papers  (2024-07-09T12:21:35Z)
• 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)
• Task adaption by biologically inspired stochastic comodulation [8.59194778459436]
  We show that fine-tuning convolutional networks by gain modulation improves on deterministic gain modulation. Our results suggest that comodulation representations can enhance learning efficiency and performance in multi-task learning.
  arXiv  Detail & Related papers  (2023-11-25T15:21:03Z)
• A Novel Neural-symbolic System under Statistical Relational Learning [50.747658038910565]
  We propose a general bi-level probabilistic graphical reasoning framework called GBPGR. In GBPGR, the results of symbolic reasoning are utilized to refine and correct the predictions made by the deep learning models. Our approach achieves high performance and exhibits effective generalization in both transductive and inductive tasks.
  arXiv  Detail & Related papers  (2023-09-16T09:15:37Z)
• Learning the Plasticity: Plasticity-Driven Learning Framework in Spiking Neural Networks [9.25919593660244]
  New paradigm for Spiking Neural Networks (SNNs) Plasticity-Driven Learning Framework (PDLF) PDLF redefines concepts of functional and Presynaptic-Dependent Plasticity.
  arXiv  Detail & Related papers  (2023-08-23T11:11:31Z)
• Improving Performance in Continual Learning Tasks using Bio-Inspired Architectures [4.2903672492917755]
  We develop a biologically inspired lightweight neural network architecture that incorporates synaptic plasticity mechanisms and neuromodulation. Our approach leads to superior online continual learning performance on Split-MNIST, Split-CIFAR-10, and Split-CIFAR-100 datasets. We further demonstrate the effectiveness of our approach by integrating key design concepts into other backpropagation-based continual learning algorithms.
  arXiv  Detail & Related papers  (2023-08-08T19:12:52Z)
• ConCerNet: A Contrastive Learning Based Framework for Automated Conservation Law Discovery and Trustworthy Dynamical System Prediction [82.81767856234956]
  This paper proposes a new learning framework named ConCerNet to improve the trustworthiness of the DNN based dynamics modeling. We show that our method consistently outperforms the baseline neural networks in both coordinate error and conservation metrics.
  arXiv  Detail & Related papers  (2023-02-11T21:07:30Z)
• The least-control principle for learning at equilibrium [65.2998274413952]
  We present a new principle for learning equilibrium recurrent neural networks, deep equilibrium models, or meta-learning. Our results shed light on how the brain might learn and offer new ways of approaching a broad class of machine learning problems.
  arXiv  Detail & Related papers  (2022-07-04T11:27:08Z)
• Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium Computation [93.52573037053449]
  H-MARL (Hallucinated Multi-Agent Reinforcement Learning) learns successful equilibrium policies after a few interactions with the environment. We demonstrate our approach experimentally on an autonomous driving simulation benchmark.
  arXiv  Detail & Related papers  (2022-03-14T17:24:03Z)

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.