Complex spatial navigation in animals, computational models and neuro-inspired robots
Complex spatial navigation in animals, computational models and neuro-inspired robots
复制标题
动物、计算模型和神经机器人的复杂空间导航
DOI：
10.1007/s00422-020-00832-y
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发表时间：
2020
影响因子：
1.9
通讯作者：
Weitzenfeld, Alfredo
中科院分区：
文献类型：
--
作者：
Fellous, Jean-Marc;Dominey, Peter;Weitzenfeld, Alfredo
本期关于“动物、计算模型和神经启发机器人的复杂空间导航”的特刊起源于 2018 年 9 月 28 日在法国里昂举行的研讨会，该研讨会由 Peter Ford Dominey、Jean-Marc Fellos 和 Alfredo Weitzenfeld 组织，由 NSF 和 ANR CRCNS 联合资助（奖项 1429937）赞助。研讨会的目标是讨论利用实验研究、计算建模和机器人评估理解复杂空间导航的神经机制的最新进展，重点关注与 3 种技术中至少 2 种（动物、计算神经科学和神经机器人）相关的研究。本期汇集了研讨会参与者以及其他研究人员的贡献，讨论复杂环境中的空间认知研究。人脑是最复杂的生物计算设备之一，拥有超过 3000 亿个并行处理器，与超过 30 万亿个连接相连。如何研究这样一台非凡的机器？经典的科学方法始终是将生物复杂性置于一个简单的环境中，在该环境中，大多数特征都受到控制并易于操纵。然后，对于每组环境参数，反复地、煞费苦心地呈现精心设计的感官输入并测量相关的行为输出。希望平均而言，输入和输出之间会出现一些有趣的关系，并且这些关系将深入了解底层的神经计算。这种方法必然会做出许多强有力的假设，其中最重要的是可以通过收集数据来理解系统的复杂性
This special issue on “Complex Spatial Navigation in Animals, Computational Models and Neuro-inspired Robots” has its origins in a workshop held Sept 28, 2018, in Lyon, France, organized by Peter Ford Dominey, Jean-Marc Fellous, and Alfredo Weitzenfeld sponsored by a joint NSF and ANR CRCNS grant (award 1429937). The goal of the workshop was to discuss the latest advances in understanding the neural mechanisms of complex spatial navigation using experimental studies, computational modeling and robotics evaluations, giving emphasis on studies that relate at least 2 of the 3 techniques (animals, computational neuroscience and neuro-robotics). This issue brings together contributions from workshop participants as well as from additional researchers to discuss the study of spatial cognition in complex environments.The human brain is one of the most complex biological computing device, with over 300 billion parallel processors, linked with over 30 trillion connections. How does one study such a phenomenal machine? The classic scientific approach has always been to place biological complexity in a simple environment where most of the features are controlled and easily manipulated. Then, for each set of environmental parameters, repeatedly, painstakingly, present well-designed sensory inputs and measure relevant behavioral outputs. The hope is that, on average, some interesting relationships between inputs and outputs will emerge, and that these relationships will give insights into the underlying neural computations. This method necessarily makes a number of strong assumptions, not the least of which is that the complexity of a system can be understood by collecting data
影响因子：
1.9
作者：
Cazin, Nicolas;Scleidorovich, Pablo;Dominey, Peter Ford
通讯作者：
Dominey, Peter Ford
压后皮质的路径整合和空间背景表示模型
影响因子：
1.9
作者：
Mingda Ju;P. Gaussier
通讯作者：
P. Gaussier