Neurodynamical Computing at the Information Boundaries of Intelligent Systems
Neurodynamical Computing at the Information Boundaries of Intelligent Systems
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DOI：
10.1007/s12559-022-10081-9
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发表时间：
2022-12-27
影响因子：
5.4
通讯作者：
Hwang，Grace M.
中科院分区：
文献类型：
--
作者：
Monaco，Joseph D.;Hwang，Grace M.
人工智能还没有达到生物智能的定义特征，尽管模型拥有比人脑神经元更多的参数。在这篇观点文章中，我们综合了理解智能系统的历史方法，并认为这些领域的方法论和认识偏见可以通过远离认知主义的大脑作为计算机理论并认识到大脑存在于大型相互依存的生命系统中来解决。将认知的动力系统观点与知觉控制理论的大规模分布式反馈相结合，突出了我们对非还原神经机制的理解中的一个理论空白。细胞神经元-正确地认为是可重入的动力流，而不仅仅是确定的神经元群-可以通过提供一个最小的超神经元层次的组织来填补这一空白，从而为计算建立一个神经动力学的基础层。通过考虑信息流的物理体现和情境嵌入，我们讨论了这个计算基础层的保守振荡和结构特性的皮质-海马网络。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。
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.