Context dependent and multimodal learning: from insect brains to robot controllers

上下文相关和多模态学习：从昆虫大脑到机器人控制器

• 批准号: EP/F030673/1
• 负责人: Barbara Webb
• 金额: $ 77.66万
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2008
• 资助国家: 英国
• 起止时间: 2008 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FF030673%2F1
• 关键词: Context; dependent; multimodal; learning; insect

A central issue in current robotics is how to scale up to more complex cognitive abilities, such as context dependent learning and prediction. Although insects are often viewed as simple reflexive systems, they are in fact more competent than any existing autonomous robots. They are capable of learning, integration of multisensory cues, real-world navigation, and flexible behavioural choice. As they obtain such competences with relatively small brains, understanding these mechanisms should lead to efficient robot applications. Within biology, there has recently been great interest and substantial advance in understanding the insect brain. So far, modelling of these systems has lagged behind, but it is essential for many reasons. By building models of the insect brain we can evaluate precisely expressed hypotheses about its function, and test which elements are crucial for complex behaviour. Moreover by implementing these hypotheses in hardware on robots we can understand the systems in real behavioural contexts. Thus there is a real opportunity to contribute to biological knowledge at the same time as developing systems that have useful application as robot controllers. Our intention in this project is to develop and evaluate models of learning in insect brains, using a combination of biological experiments, computational modelling, and hardware implementations. In particular we want to examine the neural mechanisms that support forms of learning more complex than simple association. These include context dependence, generalisation, and expectation-based expression of responses. Insight into these capabilities requires closer attention to the details of the mechanisms in the insect. For example, it may be important to understand the different stages and time-scales of learning and how these are supported by different biochemical processes. We can exploit an ideal combination of circumstances to make substantial advances in this area. The PI (Webb) has extensive experience in building robot models of insect behaviour, including implementing sensory and neural processing mechanisms in hardware. Along with the researcher-CI (Wessnitzer) she has developed initial models of the relevant insect brain mechanisms, and has strong connections to the leading biologists working in this area. One of these is the CI (Armstrong) who is using advanced genetic techniques to determine the roles of different structures and signalling pathways in the insect brain. Thus we intend to develop a tightly linked paradigm in which: - behavioural experiments suggested by the models provide data for model evaluation; - hardware implementation of the models provides real world evaluation and motivates abstraction; - abstracted models suggest key functional roles that can be tested using genetic manipulations on the insects. The outcome will be both significantly improved understanding of insect brains and a substantial step towards cognitive controllers in robots.

当前机器人技术的一个核心问题是如何扩展到更复杂的认知能力，比如依赖上下文的学习和预测。虽然昆虫通常被视为简单的反射系统，但实际上它们比任何现有的自主机器人都更有能力。他们能够学习，整合多感官线索，现实世界的导航和灵活的行为选择。当它们用相对较小的大脑获得这样的能力时，理解这些机制应该会导致高效的机器人应用。在生物学领域，最近人们对了解昆虫的大脑产生了极大的兴趣并取得了实质性进展。到目前为止，这些系统的建模已经落后，但由于许多原因，它是必不可少的。通过建立昆虫大脑的模型，我们可以精确地评估关于其功能的假设，并测试哪些元素对复杂行为至关重要。此外，通过在机器人硬件上实现这些假设，我们可以在真实的行为环境中理解系统。因此，在开发作为机器人控制器的有用应用系统的同时，为生物学知识做出贡献是一个真正的机会。在这个项目中，我们的目的是利用生物实验、计算建模和硬件实现的结合，开发和评估昆虫大脑的学习模型。我们特别想研究支持比简单联想更复杂的学习形式的神经机制。这些包括上下文依赖、泛化和基于期望的反应表达。深入了解这些能力需要更密切地关注昆虫机制的细节。例如，了解学习的不同阶段和时间尺度以及这些是如何由不同的生化过程支持的可能很重要。我们可以利用各种情况的理想组合，在这一领域取得实质性进展。PI （Webb）在构建昆虫行为的机器人模型方面拥有丰富的经验，包括在硬件中实现感觉和神经处理机制。与研究人员ci （Wessnitzer）一起，她开发了相关昆虫大脑机制的初始模型，并与该领域的主要生物学家建立了密切的联系。其中之一是CI (Armstrong)，他正在使用先进的遗传技术来确定昆虫大脑中不同结构和信号通路的作用。因此，我们打算开发一个紧密联系的范式：-模型提出的行为实验为模型评估提供数据；模型的硬件实现提供了真实世界的评估并激发了抽象；抽象的模型提出了关键的功能作用，可以通过对昆虫的基因操作来测试。结果将大大提高对昆虫大脑的理解，并在机器人认知控制器方面迈出实质性的一步。

项目成果

Cognition in insects.

昆虫的认知。

• DOI: 10.1098/rstb.2012.0218
• 发表时间: 2012
• 期刊: Philosophical transactions of the Royal Society of London. Series B, Biological sciences
• 作者: Webb B

Building the central complex in Drosophila: the generation and development of distinct neural subsets.

构建果蝇的中央复合体：不同神经亚群的产生和发展。

• DOI: 10.1002/cne.22285
• 发表时间: 2010
• 期刊: The Journal of comparative neurology
• 作者: Young JM

An incentive circuit for memory dynamics in the mushroom body of Drosophila melanogaster.

• DOI: 10.7554/elife.75611
• 发表时间: 2022-04-01
• 期刊: eLife
• 影响因子: 7.7
• 作者: Gkanias E;McCurdy LY;Nitabach MN;Webb B
• 通讯作者: Webb B

Open source tracking and analysis of adult Drosophila locomotion in Buridan's paradigm with and without visual targets.

• DOI: 10.1371/journal.pone.0042247
• 发表时间: 2012
• 期刊: PloS one
• 影响因子: 3.7
• 作者: Colomb J;Reiter L;Blaszkiewicz J;Wessnitzer J;Brembs B
• 通讯作者: Brembs B

Dietary salt levels affect salt preference and learning in larval Drosophila.

• DOI: 10.1371/journal.pone.0020100
• 发表时间: 2011
• 期刊: PloS one
• 影响因子: 3.7
• 作者: Russell C;Wessnitzer J;Young JM;Armstrong JD;Webb B
• 通讯作者: Webb B