首页> 外文学位 >A model of microtubule based learning for perception-action behavior control.
【24h】

A model of microtubule based learning for perception-action behavior control.

机译:基于微管学习的感知行为控制模型。

获取原文
获取原文并翻译 | 示例

摘要

The eukaryotic cell is a computational device that performs perception-action behavior, which requires a long-range signaling mechanism. The micro-tubule network is the only intracellular structure that provides the structural characteristics needed for this type of intracellular signaling. From these characteristics, and preliminary experimental evidence, a variety of signaling mechanisms have been proposed in the literature. To explore this hypothesis, the microtubule learning model (MtLM) is presented that combines a biologically motivated a bridge between machine learning and mainstream cell biology.; The presented MtLM is shown to perform well within the context of two different perception-action frameworks. The first framework, the “center finding problem”, is a robot navigation task where the MtLM must find the center of a virtual two-dimensional space when given a random starting point. The second framework, the biot, is a novel biomimetic robot architecture that consists of several segments interconnected in a highly context-sensitive fashion (exhibiting some degree of randomness). This framework is designed to simulate the context-sensitivity that is typical of interactions inherent between different components of the eukaryotic cell, providing the MtLM with a biologically plausible learning task.; This work, by providing a functional example of intracellular long-range signaling through the MtLM, reinforces the hypothesis that the long-range signaling mechanism in the eukaryotic cell is the microtubule network. Additionally, application of the MtLM to these differing frameworks illustrates the importance of structure in any system constructed in a bottom-up fashion, and highlights the differences between information processing tasks typically performed at the cellular level and in higher-order cognitive tasks. Lastly, this work also illustrates the strength of the MtLM as a control mechanism for producing tuned oscillatory activity.
机译:真核细胞是执行感知动作行为的计算设备,这需要长距离的信号传导机制。微管网络是唯一提供此类细胞内信号传递所需结构特征的细胞内结构。从这些特征和初步的实验证据,文献中已经提出了多种信号传导机制。为了探索这一假设,提出了微管学习模型(MtLM),该模型结合了生物学动机在机器学习和主流细胞生物学之间架起了一座桥梁。所展示的MtLM在两个不同的感知动作框架的背景下表现良好。第一个框架是“中心发现问题”,它是机器人导航任务,其中,当给定随机起点时,MtLM必须找到虚拟二维空间的中心。第二个框架,即biot,是一种新颖的仿生机器人体系结构,由以高度上下文相关的方式互连(表现出一定程度的随机性)的多个段组成。该框架旨在模拟环境敏感度,这是真核细胞不同成分之间固有的相互作用所特有的,为MtLM提供生物学上合理的学习任务。通过提供通过MtLM进行细胞内远程信号传导的功能性实例,这项工作加强了以下假说:真核细胞中的远程信号传导机制是微管网络。此外,将MtLM应用于这些不同的框架说明了在以自下而上的方式构造的任何系统中结构的重要性,并强调了通常在细胞级别执行的信息处理任务与更高级别的认知任务之间的区别。最后,这项工作还说明了MtLM作为产生调谐振荡活动的控制机制的优势。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号