Monaco Joseph D, Hwang Grace M
Dept of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD USA.
Johns Hopkins University Applied Physics Laboratory, Laurel, MD USA.
Cognit Comput. 2024;16(5):1-13. doi: 10.1007/s12559-022-10081-9. Epub 2022 Dec 27.
Artificial intelligence has not achieved defining features of biological intelligence despite models boasting more parameters than neurons in the human brain. In this perspective article, we synthesize historical approaches to understanding intelligent systems and argue that methodological and epistemic biases in these fields can be resolved by shifting away from cognitivist brain-as-computer theories and recognizing that brains exist within large, interdependent living systems. Integrating the dynamical systems view of cognition with the massive distributed feedback of perceptual control theory highlights a theoretical gap in our understanding of nonreductive neural mechanisms. Cell assemblies-properly conceived as reentrant dynamical flows and not merely as identified groups of neurons-may fill that gap by providing a minimal supraneuronal level of organization that establishes a neurodynamical base layer for computation. By considering information streams from physical embodiment and situational embedding, we discuss this computational base layer in terms of conserved oscillatory and structural properties of cortical-hippocampal networks. Our synthesis of embodied cognition, based in dynamical systems and perceptual control, aims to bypass the neurosymbolic stalemates that have arisen in artificial intelligence, cognitive science, and computational neuroscience.
尽管人工智能模型所拥有的参数比人类大脑中的神经元还要多，但它尚未具备生物智能的决定性特征。在这篇观点文章中，我们综合了理解智能系统的历史方法，并认为这些领域中的方法和认知偏差可以通过摒弃认知主义的“大脑即计算机”理论，以及认识到大脑存在于庞大的、相互依存的生命系统中来解决。将认知的动力系统观点与感知控制理论的大规模分布式反馈相结合，凸显了我们在理解非还原神经机制方面的理论差距。细胞集合——恰当地理解为折返动态流，而不仅仅是确定的神经元群体——可能通过提供一个最小的超神经元组织水平来填补这一差距，该水平为计算建立了一个神经动力学基础层。通过考虑来自身体体现和情境嵌入的信息流，我们从皮质 - 海马网络的保守振荡和结构特性方面讨论了这个计算基础层。我们基于动力系统和感知控制的具身认知综合，旨在绕过人工智能、认知科学和计算神经科学中出现的神经符号僵局。
