Kasai Haruo, Ziv Noam E, Okazaki Hitoshi, Yagishita Sho, Toyoizumi Taro
Laboratory of Structural Physiology, Center for Disease Biology and Integrative Medicine, Faculty of Medicine, The University of Tokyo, Tokyo, Japan.
International Research Center for Neurointelligence (WPI-IRCN), UTIAS, The University of Tokyo, Bunkyo-ku, Tokyo, Japan.
Nat Rev Neurosci. 2021 Jul;22(7):407-422. doi: 10.1038/s41583-021-00467-3. Epub 2021 May 28.
In the brain, most synapses are formed on minute protrusions known as dendritic spines. Unlike their artificial intelligence counterparts, spines are not merely tuneable memory elements: they also embody algorithms that implement the brain's ability to learn from experience and cope with new challenges. Importantly, they exhibit structural dynamics that depend on activity, excitatory input and inhibitory input (synaptic plasticity or 'extrinsic' dynamics) and dynamics independent of activity ('intrinsic' dynamics), both of which are subject to neuromodulatory influences and reinforcers such as dopamine. Here we succinctly review extrinsic and intrinsic dynamics, compare these with parallels in machine learning where they exist, describe the importance of intrinsic dynamics for memory management and adaptation, and speculate on how disruption of extrinsic and intrinsic dynamics may give rise to mental disorders. Throughout, we also highlight algorithmic features of spine dynamics that may be relevant to future artificial intelligence developments.
在大脑中，大多数突触形成于被称为树突棘的微小突起上。与人工智能中的对应物不同，树突棘不仅仅是可调节的记忆元件：它们还体现了实现大脑从经验中学习并应对新挑战能力的算法。重要的是，它们表现出依赖于活动、兴奋性输入和抑制性输入（突触可塑性或“外在”动力学）以及独立于活动的动力学（“内在”动力学）的结构动力学，这两种动力学都受到神经调节影响和诸如多巴胺等强化物的作用。在这里，我们简要回顾外在和内在动力学，将它们与机器学习中存在的类似情况进行比较，描述内在动力学对记忆管理和适应的重要性，并推测外在和内在动力学的破坏如何可能导致精神障碍。在整个过程中，我们还强调了树突棘动力学的算法特征，这些特征可能与未来的人工智能发展相关。
