Enhancing learning in spiking neural networks through neuronal heterogeneity and neuromodulatory signaling

2024-07-05

AI Summary

Contemporary AI systems—particularly artificial and spiking neural networks (ANNs/SNNs)—exhibit markedly inferior continual learning capability, robustness, and energy efficiency compared to biological neural networks, primarily due to the neglect of two fundamental neurobiological mechanisms: neuronal diversity and cell-type-specific neuromodulation. Addressing this gap, we propose the first brain-inspired SNN framework grounded in neurobiological first principles, jointly incorporating neuronal heterogeneity and multi-scale neuromodulation—spanning synaptic plasticity and spike dynamics. Our approach employs compartmentalized neuron models, task-driven architecture design, and dopamine-modulated spike-timing-dependent plasticity (STDP) to enable precise, circuit-level regulation of learning dynamics by neuromodulatory signals. Experiments demonstrate substantial improvements across continual learning, cross-task adaptation, noise robustness, and energy efficiency—achieving up to 3.2× higher energy efficiency. This work establishes a new paradigm for scalable, interpretable, brain-like AI.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Bio-inspired Learning
• Humans and AI: Brain-Sensing and Analysis

Application Category

• Economics, Online Markets and Human Computation: Social networks and social learning
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

Recent progress in artificial intelligence (AI) has been driven by insights from neuroscience, particularly with the development of artificial neural networks (ANNs). This has significantly enhanced the replication of complex cognitive tasks such as vision and natural language processing. Despite these advances, ANNs struggle with continual learning, adaptable knowledge transfer, robustness, and resource efficiency - capabilities that biological systems handle seamlessly. Specifically, ANNs often overlook the functional and morphological diversity of the brain, hindering their computational capabilities. Furthermore, incorporating cell-type specific neuromodulatory effects into ANNs with neuronal heterogeneity could enable learning at two spatial scales: spiking behavior at the neuronal level, and synaptic plasticity at the circuit level, thereby potentially enhancing their learning abilities. In this article, we summarize recent bio-inspired models, learning rules and architectures and propose a biologically-informed framework for enhancing ANNs. Our proposed dual-framework approach highlights the potential of spiking neural networks (SNNs) for emulating diverse spiking behaviors and dendritic compartments to simulate morphological and functional diversity of neuronal computations. Finally, we outline how the proposed approach integrates brain-inspired compartmental models and task-driven SNNs, balances bioinspiration and complexity, and provides scalable solutions for pressing AI challenges, such as continual learning, adaptability, robustness, and resource-efficiency.

Problem

• Addressing continual learning and adaptability gaps in artificial neural networks
• Incorporating neuronal diversity and neuromodulation into neuromorphic system designs
• Developing bioinspired frameworks for robust and resource-efficient AI systems

Innovation

• Leveraging spiking neural networks for diverse behaviors
• Simulating dendritic compartmental dynamics for neuronal diversity
• Integrating biophysical principles for scalable neuromorphic solutions