Mapping information flow in sensorimotor networks.
Mapping information flow in sensorimotor networks.
复制标题
DOI：
10.1371/journal.pcbi.0020144
复制
发表时间：
2006-10-27
影响因子：
4.3
通讯作者：
Sporns O
中科院分区：
文献类型：
--
作者：
Lungarella M;Sporns O
生物有机体不断地选择和采样信息，这些信息被它们的神经结构用于感知和行动，并用于创建指导它们自主行为的连贯认知状态。然而，信息处理不仅仅是神经系统的内部功能。相反，在这里，我们展示了感觉运动相互作用和身体形态如何在感觉输入和神经控制架构中诱导统计规律和信息结构，以及传感器、神经单元和效应器之间的信息流动如何通过与环境的相互作用而积极塑造。我们分析了从真实的和模拟机器人收集的感觉和运动数据，揭示了动态耦合感觉运动活动引起的信息结构和定向信息流的存在，包括运动输出对感觉输入的影响。我们发现感觉运动网络中的信息结构和信息流（a）是空间和时间特异性的;（B）可以受到学习的影响;（c）可以受到身体形态变化的影响。我们的研究结果表明，物理嵌入和信息之间的基本联系，突出体现内部（神经）信息处理的相互作用的影响，并阐明了各种系统组件的作用，行为的产生。神经元如何编码和处理信息是计算生物学和神经科学中的一个关键问题。在本文中，Lunarylla和Sporns提出了一种新的应用程序的计算方法，在系统级尺度的神经和感觉运动过程的整合。他们研究的主要结果是，感觉运动相互作用和身体形态可以在感觉输入和神经控制结构中诱导统计学意义和信息结构。因此，输入的信息内容并不独立于输出，作者认为，神经编码需要在其生态位内的生物体的“嵌入性”的背景下考虑。使用机器人和非线性时间序列分析技术，他们研究了传感器，神经单元和效应器之间的信息流如何通过与环境的相互作用而积极塑造。这项研究代表了发展一个明确的定量框架，统一神经和行为过程的第一步。这样的框架也可以为塑造神经系统及其行为和认知能力的进化和发展的关键制约因素提供重要的新见解。此外，它可以提供一个重要的设计原则，以指导更有效的人工认知系统的建设。
Biological organisms continuously select and sample information used by their neural structures for perception and action, and for creating coherent cognitive states guiding their autonomous behavior. Information processing, however, is not solely an internal function of the nervous system. Here we show, instead, how sensorimotor interaction and body morphology can induce statistical regularities and information structure in sensory inputs and within the neural control architecture, and how the flow of information between sensors, neural units, and effectors is actively shaped by the interaction with the environment. We analyze sensory and motor data collected from real and simulated robots and reveal the presence of information structure and directed information flow induced by dynamically coupled sensorimotor activity, including effects of motor outputs on sensory inputs. We find that information structure and information flow in sensorimotor networks (a) is spatially and temporally specific; (b) can be affected by learning, and (c) can be affected by changes in body morphology. Our results suggest a fundamental link between physical embeddedness and information, highlighting the effects of embodied interactions on internal (neural) information processing, and illuminating the role of various system components on the generation of behavior. How neurons encode and process information is a key problem in computational biology and neuroscience. In this paper, Lungarella and Sporns present a novel application of computational methods to the integration of neural and sensorimotor processes at the systems-level scale. The central result of their study is that sensorimotor interaction and body morphology can induce statistical regularities and information structure in sensory inputs and within the neural control architecture. The informational content of inputs is thus not independent of output, and the authors suggest that neural coding needs to be considered in the context of the “embeddedness” of the organism within its eco-niche. Using robots and nonlinear time-series analysis techniques, they investigate how the flow of information between sensors, neural units, and effectors is actively shaped by interaction with the environment. This study represents a first step towards the development of an explicit quantitative framework that unifies neural and behavioral processes. Such a framework could also shed significant new light on key constraints shaping the evolution and development of nervous systems and their behavioral and cognitive capacities. In addition, it could provide an important design principle to guide the construction of more efficient artificial cognitive systems.
登录
查看更多内容
DOI：
10.1073/pnas.0308538101
发表时间：
2004-06-29
影响因子：
11.1
作者：
Brovelli, A;Ding, MZ;Bressler, SL
通讯作者：
Bressler, SL
影响因子：
20.6
作者：
BAJCSY, R
通讯作者：
BAJCSY, R
DOI：
10.1109/34.730558
发表时间：
1998-11-01
影响因子：
23.6
作者：
Itti, L;Koch, C;Niebur, E
通讯作者：
Niebur, E
影响因子：
64.8
作者：
BECKER, S;HINTON, GE
通讯作者：
HINTON, GE
影响因子：
2.2
作者：
LINSKER, R
通讯作者：
LINSKER, R