BABEL

在行为和计算神经科学、认知机器人和大规模神经网络硬件实现方面的最新进展，为加速理解大脑功能和基于大脑控制系统的交互式机器人系统的设计提供了机会。在行动和语言学习领域尤其如此，因为这一领域的科学和技术取得了重大发展。该项目旨在促进对单词学习中的神经和行为机制的理解，在神经解剖学基础模型中验证这些原理，并在SpiNNaker神经形态架构中实时实现大脑语言模型，以支持与神经成像实验的比较。科学假设和皮质语言模型也将通过在仿人机器人iCub上实施具身主动语言学习模型来验证。具体来说，在这个项目中，我们将基于神经科学原理，在神经回路水平上发展语言学习理论，并建立语言皮层的神经计算模型，该模型实现了在大规模神经回路中用于谈论物体和动作的单词的学习。这项理论工作将得到新的、假设驱动的脑成像研究的支持，这些研究使用MEG、EEG和fMRI来识别物体和动作的单词学习的神经关联和机制。成像结果将为大规模神经解剖模型的改进提供信息。这些模型将在SpiNNaker软件和硬件基础设施上实现，使用更真实的尖峰活动来实现语言皮层的缩放实时模型。最后，该项目将翻译应用这些神经解剖模型和SpiNNaker系统作为控制器，用于类人机器人iCub的语言和动作学习模拟，在具体和主动的学习环境中，语言的语义直接由对象操作任务的环境驱动。这是一个高度跨学科的项目，整合了神经形态工程、计算和实验神经科学以及认知机器人的基本专业知识和方法。该项目是基于申请人在这些专业领域的国际记录和以前的合作经验的独特和战略合作伙伴关系。此外，该项目将受益于由学术和工业顾问组成的国际咨询委员会，以促进项目的国际层面和影响。

Recent advances in behavioural and computational neuroscience, in cognitive robotics, and in the hardware implementation of large-scale neural networks, provide the opportunity for an accelerated understanding of brain functions and for the design of interactive robotic systems based on brain-inspired control systems. This is especially the case in the domain of action and language learning, given the significant scientific and technological developments in this field. This project aims at advancing the understanding of neural and behavioural mechanisms in word learning, the validation of these principles in neuroanatomically grounded models, and real-time implementations of brain language models within the SpiNNaker neuromorphic architecture that will support comparisons with neuroimaging experiments. The scientific hypotheses and cortical language model will also be validated by implementing a model of embodied active language learning on the humanoid robot iCub. Specifically, in the project we will develop, based on neuroscientific principles, a theory of language learning at the neural circuit level and build a neurocomputational model of the language cortex that implements the learning of words used to speak about objects and actions in large-scale neuronal circuits. This theoretical work will be supported by novel, hypothesis-driven brain imaging investigations using MEG, EEG and fMRI to identify the neural correlates and mechanisms of the learning of words for objects and actions. Imaging results will inform the improvement of the large-scale neuroanatomical models. These models will be implemented on the SpiNNaker software and hardware infrastructure, to implement a scaled-up real-time model of the language cortex using more realistic spiking activity. Finally, the project will translationally apply these neuro-anatomical models and SpiNNaker system as controllers for language and action learning simulations with the humanoid robot iCub, within the embodied and active learning context where the semantics of the language is directly driven by the context of object manipulation tasks.This is a highly interdisciplinary project that integrates essential expertise and methodologies from neuromorphic engineering, computational and experimental neuroscience, and cognitive robotics. The project is based around the unique and strategic partnership of applicants with an international track record in these areas of expertise and with previous collaborative experience. Furthermore, the project will benefit from an International Advisory Board, with both academic and industrial advisors, to foster the international dimension and impact of the project.