There is a concerted effort to build domain-general artificial intelligence in the form of universal neural network models with sufficient computational flexibility to solve a wide variety of cognitive tasks but without requiring fine-tuning on individual problem spaces and domains. To do this, models need appropriate priors and inductive biases, such that trained models can generalise to out-of-distribution examples and new problem sets. Here we provide an overview of the hallmarks endowing biological neural networks with the functionality needed for flexible cognition, in order to establish which features might also be important to achieve similar functionality in artificial systems. We specifically discuss the role of system-level distribution of network communication and recurrence, in addition to the role of short-term topological changes for efficient local computation. As machine learning models become more complex, these principles may provide valuable directions in an otherwise vast space of possible architectures. In addition, testing these inductive biases within artificial systems may help us to understand the biological principles underlying domain-general cognition.

翻译：人们正共同努力，以通用神经网络模型的形式构建域通用人工智能，这些模型具有足够的计算灵活性，能够解决各种认知任务，而无需针对个别问题空间和领域进行微调。为此，模型需要适当的先验知识和归纳偏差，以便训练后的模型能够泛化到分布外的样本和新的问题集。在此，我们概述了赋予生物神经网络灵活认知所需功能的标志性特征，以确定哪些特征可能对在人工系统中实现类似功能也至关重要。具体而言，我们讨论了网络通信和递归的系统级分布的作用，以及短期拓扑变化对高效局部计算的作用。随着机器学习模型变得越来越复杂，这些原则可能在原本庞大的可能架构空间中提供有价值的指导方向。此外，在人工系统中测试这些归纳偏差可能有助于我们理解域通用认知背后的生物原理。