In Two Minds about Lifelong Learning: Exploring Hemispheric Redundancy and Specialisation in Neural Models

• URL: http://arxiv.org/abs/2608.19514v1
• Date: Thu, 20 Aug 2026 00:15:18 GMT
• Title: In Two Minds about Lifelong Learning: Exploring Hemispheric Redundancy and Specialisation in Neural Models
• Abstract summary: This paper attempts to build a high-level framework for how animals learn and preserve knowledge.<n>We focus on three concepts: experience replay, REM sleep, and bilaterality.<n>We propose 4MAS, a novel macroarchitecture demonstrating how machine learning models might benefit from asymmetric hemispheres.
• Score: 6.404762820001178
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Persistent intelligent systems require the ability to learn continually, but current machine learning approaches face significant challenges in this area compared to biological learning systems. Machine learning algorithms typically trade off retention of previously learned information and adaptation to new or changing data patterns. When continual learning capabilities are absent, algorithms must undergo retraining using the entire data set, an approach that becomes impractical when original training data are unavailable due to storage constraints, financial or computational costs, or privacy restrictions. However, biological animals can learn continually, without experiencing catastrophic forgetting. This paper attempts to build a high-level framework for how animals learn and preserve knowledge by modelling neural components and states that are known to be related to memory consolidation. We focus on three concepts: experience replay, REM sleep, and bilaterality. We propose 4MAS (4 Module Awake/Sleep), a novel macroarchitecture demonstrating how machine learning models might benefit from asymmetric hemispheres, each with their own long- and short-term memory mechanisms, and how a period of sleep between incremental learning tasks might benefit memory consolidation. Finally, we present results showing that our architecture achieves competitive results on the Split-MNIST, Split-Fashion-MNIST and Split-CIFAR-100 datasets, with 98.3%, 84.9%, and 29.29% accuracy respectively.

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