Loeb Gerald E
Alfred E. Mann Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, United States.
Front Integr Neurosci. 2023 Mar 7;17:1108271. doi: 10.3389/fnint.2023.1108271. eCollection 2023.
Recent research has illuminated the complexity and importance of the thalamocortical system but it has been difficult to identify what computational functions it performs. Meanwhile, deep-learning artificial neural networks (ANNs) based on bio-inspired models of purely cortical circuits have achieved surprising success solving sophisticated cognitive problems associated historically with human intelligence. Nevertheless, the limitations and shortcomings of artificial intelligence (AI) based on such ANNs are becoming increasingly clear. This review considers how the addition of thalamocortical connectivity and its putative functions related to cortical attention might address some of those shortcomings. Such bio-inspired models are now providing both testable theories of biological cognition and improved AI technology, much of which is happening outside the usual academic venues.
近期的研究揭示了丘脑皮质系统的复杂性和重要性，但要确定它执行何种计算功能却并非易事。与此同时，基于纯皮质回路的生物启发模型构建的深度学习人工神经网络（ANN）在解决一些历来与人类智能相关的复杂认知问题上取得了惊人的成功。然而，基于此类ANN的人工智能（AI）的局限性和缺点正日益凸显。本综述探讨了增加丘脑皮质连接及其与皮质注意力相关的假定功能如何可能解决其中一些缺点。此类受生物启发的模型如今不仅提供了可检验的生物认知理论，还改进了人工智能技术，而且其中许多进展都发生在常规学术领域之外。
