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.

翻译：人工智能（AI）的最新进展得益于神经科学的启示，特别是人工神经网络（ANNs）的发展。这显著提升了视觉与自然语言处理等复杂认知任务的模拟能力。尽管取得了这些进步，ANNs 在持续学习、适应性知识迁移、鲁棒性及资源效率方面仍面临挑战——而这些能力在生物系统中却能自然实现。具体而言，ANNs 往往忽视了大脑的功能与形态多样性，这限制了其计算能力。此外，将细胞类型特异的神经调节效应与神经元异质性结合到 ANNs 中，可能实现两个空间尺度的学习：神经元层面的脉冲行为，以及回路层面的突触可塑性，从而潜在地增强其学习能力。本文总结了近期受生物启发的模型、学习规则与架构，并提出一个基于生物学知识的框架以增强 ANNs。我们提出的双框架方法强调了脉冲神经网络（SNNs）在模拟多样化脉冲行为方面的潜力，以及树突区室在模拟神经元计算的形态与功能多样性中的作用。最后，我们概述了所提方法如何整合脑启发的区室化模型与任务驱动的 SNNs，在生物启发与复杂性之间取得平衡，并为持续学习、适应性、鲁棒性和资源效率等紧迫的 AI 挑战提供可扩展的解决方案。