Neural and robotic correlates of predictive coding and selective attention

预测编码和选择性注意的神经和机器人关联

• 批准号: DE150100548
• 负责人: A/Prof Steven Wiederman
• 金额: $ 24.77万
• 依托单位: The University of Adelaide
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Early Career Researcher Award
• 财政年份: 2015
• 资助国家: 澳大利亚
• 起止时间: 2015-01-01 至 2018-03-31
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DE150100548
• 关键词: Neural; robotic; correlates; predictive; coding

Whether a human catching a ball, a dog leaping at a frisbee or a dragonfly hunting prey amidst a swarm, brains both large and small have evolved the ability to focus attention on one moving target, even in the presence of distracters. This project aims to investigate how brains solve this challenging problem by recording the activity of dragonfly neurons that selectively attend to one target whilst ignoring others. The project aims to examine how expectation and attention are encoded in the brain and will build an autonomous robot using computational models bio-inspired from this neuronal processing. Robots capable of visually perceiving and interacting with targets in natural environments have applications in health, surveillance and defence.

无论是人类接球、狗跳跃飞盘还是蜻蜓在蜂群中捕食猎物，无论大小的大脑都进化出了将注意力集中在一个移动目标上的能力，即使存在干扰物。该项目旨在通过记录蜻蜓神经元的活动来研究大脑如何解决这一具有挑战性的问题，这些神经元选择性地关注一个目标而忽略其他目标。该项目旨在研究期望和注意力如何在大脑中编码，并将使用受这种神经元处理启发的生物计算模型来构建一个自主机器人。能够在自然环境中视觉感知目标并与目标交互的机器人可在健康、监视和国防领域得到应用。