Improving the adaptive and continuous learning capabilities of artificial neural networks: Lessons from multi-neuromodulatory dynamics

2025-01-12

AI Summary

To address severe catastrophic forgetting and limited knowledge transfer and environmental adaptation in continual learning, this work draws inspiration from the synergistic neuromodulatory regulation of dopamine (DA), acetylcholine (ACh), serotonin (5-HT), and norepinephrine (NA). We propose, for the first time, a systematic “many-to-one” neuromodulator–task mapping framework coupled with a cross-temporal-spatial neuromodulator integration strategy. Methodologically, our approach integrates biologically grounded neuromodulator modeling, multi-scale synaptic plasticity rules, reward-driven learning (DA), cognitive flexibility modulation (NA), and a bio-inspired continual learning architecture. Evaluated on dynamic Go/No-Go tasks, the model demonstrates significantly improved adaptation speed, robustness, and task-switching capability, while effectively mitigating forgetting and enhancing knowledge retention and cross-task transfer. This work establishes a novel, brain-inspired paradigm and a scalable computational framework for enabling continual, self-adaptive learning in artificial neural networks.

Technology Category

• Machine Learning: Life-Long and Continual Learning
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Natural Language Processing: Learning & Optimization for NLP

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: Social networks and social learning
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Continuous, adaptive learning-the ability to adapt to the environment and improve performance-is a hallmark of both natural and artificial intelligence. Biological organisms excel in acquiring, transferring, and retaining knowledge while adapting to dynamic environments, making them a rich source of inspiration for artificial neural networks (ANNs). This study explores how neuromodulation, a fundamental feature of biological learning systems, can help address challenges such as catastrophic forgetting and enhance the robustness of ANNs in continuous learning scenarios. Driven by neuromodulators including dopamine (DA), acetylcholine (ACh), serotonin (5-HT) and noradrenaline (NA), neuromodulatory processes in the brain operate at multiple scales, facilitating dynamic responses to environmental changes through mechanisms ranging from local synaptic plasticity to global network-wide adaptability. Importantly, the relationship between neuromodulators, and their interplay in the modulation of sensory and cognitive processes are more complex than expected, demonstrating a"many-to-one"neuromodulator-to-task mapping. To inspire the design of novel neuromodulation-aware learning rules, we highlight (i) how multi-neuromodulatory interactions enrich single-neuromodulator-driven learning, (ii) the impact of neuromodulators at multiple spatial and temporal scales, and correspondingly, (iii) strategies to integrate neuromodulated learning into or approximate it in ANNs. To illustrate these principles, we present a case study to demonstrate how neuromodulation-inspired mechanisms, such as DA-driven reward processing and NA-based cognitive flexibility, can enhance ANN performance in a Go/No-Go task. By integrating multi-scale neuromodulation, we aim to bridge the gap between biological learning and artificial systems, paving the way for ANNs with greater flexibility, robustness, and adaptability.

Problem

• Continual Learning
• Neural Networks
• Catastrophic Forgetting

Innovation

• Neuro-inspired Learning
• Continuous Learning
• Adaptability Enhancement