# Incorporating Neuro-Inspired Adaptability for Continual Learning in Artificial Intelligence

arXiv: https://arxiv.org/abs/2308.14991 · HTML: https://mlsanity.com/paper/2308.14991/

## Key facts

- Cited by 43 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 84 references that are in the map.
- First posted on arXiv 29 Aug 2023.

## Builds on (most cited first, 30 of 32)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [iCaRL: Incremental Classifier and Representation Learning](https://mlsanity.com/paper/1611.07725/) (2016)
- [Gradient Episodic Memory for Continual Learning](https://mlsanity.com/paper/1706.08840/) (2017)
- [Sharpness-Aware Minimization for Efficiently Improving Generalization](https://mlsanity.com/paper/2010.01412/) (2020)
- [Continual Lifelong Learning with Neural Networks: A Review](https://mlsanity.com/paper/1802.07569/) (2018)
- [Learning Transferable Features with Deep Adaptation Networks](https://mlsanity.com/paper/1502.02791/) (2015)
- [Continual Learning with Deep Generative Replay](https://mlsanity.com/paper/1705.08690/) (2017)
- [Memory Aware Synapses: Learning what (not) to forget](https://mlsanity.com/paper/1711.09601/) (2017)
- [Efficient Lifelong Learning with A-GEM](https://mlsanity.com/paper/1812.00420/) (2018)
- [A Comprehensive Survey of Continual Learning: Theory, Method and Application](https://mlsanity.com/paper/2302.00487/) (2023)
- [Optimizing Neural Networks with Kronecker-factored Approximate Curvature](https://mlsanity.com/paper/1503.05671/) (2015)
- [Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigence](https://mlsanity.com/paper/1801.10112/) (2018)
- [Overcoming catastrophic forgetting with hard attention to the task](https://mlsanity.com/paper/1801.01423/) (2018)
- [Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference](https://mlsanity.com/paper/1810.11910/) (2018)
- [Progress & Compress: A scalable framework for continual learning](https://mlsanity.com/paper/1805.06370/) (2018)
- [PathNet: Evolution Channels Gradient Descent in Super Neural Networks](https://mlsanity.com/paper/1701.08734/) (2017)
- [CORe50: a New Dataset and Benchmark for Continuous Object Recognition](https://mlsanity.com/paper/1705.03550/) (2017)
- [Online Structured Laplace Approximations For Overcoming Catastrophic Forgetting](https://mlsanity.com/paper/1805.07810/) (2018)
- [Understanding the Role of Training Regimes in Continual Learning](https://mlsanity.com/paper/2006.06958/) (2020)
- [Continual Learning with Node-Importance based Adaptive Group Sparse Regularization](https://mlsanity.com/paper/2003.13726/) (2020)
- [Optimal Continual Learning has Perfect Memory and is NP-hard](https://mlsanity.com/paper/2006.05188/) (2020)
- [Memory Replay with Data Compression for Continual Learning](https://mlsanity.com/paper/2202.06592/) (2022)
- [AFEC: Active Forgetting of Negative Transfer in Continual Learning](https://mlsanity.com/paper/2110.12187/) (2021)
- [Flattening Sharpness for Dynamic Gradient Projection Memory Benefits Continual Learning](https://mlsanity.com/paper/2110.04593/) (2021)
- [ORDisCo: Effective and Efficient Usage of Incremental Unlabeled Data for Semi-supervised Continual Learning](https://mlsanity.com/paper/2101.00407/) (2021)
- [CPR: Classifier-Projection Regularization for Continual Learning](https://mlsanity.com/paper/2006.07326/) (2020)
- [CoSCL: Cooperation of Small Continual Learners is Stronger than a Big One](https://mlsanity.com/paper/2207.06543/) (2022)

## Built on by (most cited first, 30 of 43)

- [Knowledge Conflicts for LLMs: A Survey](https://mlsanity.com/paper/2403.08319/) (2024)
- [Hierarchical Decomposition of Prompt-Based Continual Learning: Rethinking Obscured Sub-optimality](https://mlsanity.com/paper/2310.07234/) (2023)
- [Continual Learning and Catastrophic Forgetting](https://mlsanity.com/paper/2403.05175/) (2024)
- [Weighted Ensemble Models Are Strong Continual Learners](https://mlsanity.com/paper/2312.08977/) (2023)
- [HiDe-PET: Continual Learning via Hierarchical Decomposition of Parameter-Efficient Tuning](https://mlsanity.com/paper/2407.05229/) (2024)
- [KnowPO: Knowledge-aware Preference Optimization for Controllable Knowledge Selection in Retrieval-Augmented Language Models](https://mlsanity.com/paper/2408.03297/) (2024)
- [Disentangling and Mitigating the Impact of Task Similarity for Continual Learning](https://mlsanity.com/paper/2405.20236/) (2024)
- [Orchestrate Latent Expertise: Advancing Online Continual Learning with Multi-Level Supervision and Reverse Self-Distillation](https://mlsanity.com/paper/2404.00417/) (2024)
- [Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting](https://mlsanity.com/paper/2502.02797/) (2025)
- [Emulating Human-like Adaptive Vision for Efficient and Flexible Machine Visual Perception](https://mlsanity.com/paper/2509.15333/) (2025)
- [Human-inspired Perspectives: A Survey on AI Long-term Memory](https://mlsanity.com/paper/2411.00489/) (2024)
- [Overcoming Recency Bias of Normalization Statistics in Continual Learning: Balance and Adaptation](https://mlsanity.com/paper/2310.08855/) (2023)
- [Continual Learning in the Presence of Repetition](https://mlsanity.com/paper/2405.04101/) (2024)
- [Towards Building Specialized Generalist AI with System 1 and System 2 Fusion](https://mlsanity.com/paper/2407.08642/) (2024)
- [Continual Learning with Neuromorphic Computing: Foundations, Methods, and Emerging Applications](https://mlsanity.com/paper/2410.09218/) (2024)
- [Order-Robust Class Incremental Learning: Graph-Driven Dynamic Similarity Grouping](https://mlsanity.com/paper/2502.20032/) (2025)
- [Versatile Cardiovascular Signal Generation with a Unified Diffusion Transformer](https://mlsanity.com/paper/2505.22306/) (2025)
- [Towards Incremental Learning in Large Language Models: A Critical Review](https://mlsanity.com/paper/2404.18311/) (2024)
- [Adaptive Intelligence: leveraging insights from adaptive behavior in animals to build flexible AI systems](https://mlsanity.com/paper/2411.15234/) (2024)
- [ELENA: Epigenetic Learning through Evolved Neural Adaptation](https://mlsanity.com/paper/2501.05735/) (2025)
- [Beyond representational alignment with brain-guided language models for robust reasoning](https://mlsanity.com/paper/2606.11893/) (2026)
- [Filling Memory Gaps: Enhancing Continual Semantic Parsing via SQL Syntax Variance-Guided LLMs without Real Data Replay](https://mlsanity.com/paper/2412.07246/) (2024)
- [C-LoRA: Continual Low-Rank Adaptation for Pre-trained Visual Models](https://mlsanity.com/paper/2502.17920/) (2025)
- [Personalized Artificial General Intelligence (AGI) via Neuroscience-Inspired Continuous Learning Systems](https://mlsanity.com/paper/2504.20109/) (2025)
- [Flashbacks to Harmonize Stability and Plasticity in Continual Learning](https://mlsanity.com/paper/2506.00477/) (2025)
- [H2C: Hippocampal Circuit-inspired Continual Learning for Lifelong Trajectory Prediction in Autonomous Driving](https://mlsanity.com/paper/2508.01158/) (2025)
- [The Lie of the Average: How Class Incremental Learning Evaluation Deceives You?](https://mlsanity.com/paper/2509.22580/) (2025)
- [Domain Generalizable Continual Learning](https://mlsanity.com/paper/2510.16914/) (2025)
- [Forward-Backward Knowledge Distillation for Continual Clustering](https://mlsanity.com/paper/2405.19234/) (2024)
- [Achieving Deep Continual Learning via Evolution](https://mlsanity.com/paper/2502.06210/) (2025)

## Abstract (arXiv)

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.