RIKEN BSI-TOYOTA Collaboration Center, RIKEN, Wako-shi, Saitama, Japan.
PLoS One. 2011;6(9):e24007. doi: 10.1371/journal.pone.0024007. Epub 2011 Sep 8.
Mounting lines of evidence suggest the significant computational ability of a single neuron empowered by active dendritic dynamics. This motivates us to study what functionality can be acquired by a network of such neurons. The present paper studies how such rich single-neuron dendritic dynamics affects the network dynamics, a question which has scarcely been specifically studied to date. We simulate neurons with active dendrites networked locally like cortical pyramidal neurons, and find that naturally arising localized activity--called a bump--can be in two distinct modes, mobile or immobile. The mode can be switched back and forth by transient input to the cortical network. Interestingly, this functionality arises only if each neuron is equipped with the observed slow dendritic dynamics and with in vivo-like noisy background input. If the bump activity is considered to indicate a point of attention in the sensory areas or to indicate a representation of memory in the storage areas of the cortex, this would imply that the flexible mode switching would be of great potential use for the brain as an information processing device. We derive these conclusions using a natural extension of the conventional field model, which is defined by combining two distinct fields, one representing the somatic population and the other representing the dendritic population. With this tool, we analyze the spatial distribution of the degree of after-spike adaptation and explain how we can understand the presence of the two distinct modes and switching between the modes. We also discuss the possible functional impact of this mode-switching ability.
越来越多的证据表明，活跃的树突动力学赋予单个神经元强大的计算能力。这促使我们研究这种神经元网络可以获得什么功能。本文研究了丰富的单个神经元树突动力学如何影响网络动力学，这一问题迄今几乎没有得到专门研究。我们模拟了具有活跃树突的神经元，这些神经元像皮质锥体细胞一样局部联网，并发现自然产生的局部活动（称为凸起）可以处于两种不同的模式，即移动或固定。这种模式可以通过对皮质网络的短暂输入来回切换。有趣的是，如果每个神经元都配备了观察到的缓慢树突动力学和类似于体内的噪声背景输入，那么这种功能才会出现。如果将凸起活动视为感觉区域的注意力点，或者表示皮质存储区域的记忆表示，则这意味着灵活的模式切换将对大脑作为信息处理设备具有巨大的潜在用途。我们使用传统场模型的自然扩展来得出这些结论，该模型通过结合代表体细胞群和树突群的两个不同场来定义。使用此工具，我们分析了峰后适应程度的空间分布，并解释了如何理解两种不同模式的存在以及模式之间的切换。我们还讨论了这种模式切换能力的可能功能影响。
