Brain-Inspired Neuronal Model of Attention and Memory

由于视觉系统的处理能力有限，无法对整个视觉场景进行快速分析，因此注意力对生物体来说是必要和至关重要的。选择性视觉注意是一个认知过程,它允许一个活的有机体从传入视觉信息提取的部分是最重要的在一个给定的时刻,应该详细处理。例如，对所提取信息的详细处理可以包括新颖性检测和将新对象分配到存储器。在这个项目中大规模brain-inspired模型开发的等级森严的神经元会飙升,连续选择的解决问题的对象通过结合面向对象的注意,内存和新奇检测。由于我们认为大脑并没有为每一种认知功能发明一种特殊的处理机制，而是为一种特定类型的处理适应类似的机制，因此基于一小组信息处理的一般原则（例如同步，自然频率的适应，共振振幅的增加）开发一个模型是一个挑战。我们认为，这些理论原理是生物大脑性能的关键，并且在提出的研究中将首次在注意力和记忆的组合模型中实施。这样的发展提供了巨大的潜力,在减少新鲜的基本机制支撑大脑的信息处理和设计新一代的计算设备,认知机器人等。

Attention is necessary and vital for living organisms due to the limited processing capability of the visual system which precludes the rapid analysis of the whole visual scene. Selective visual attention is a cognitive process that allows a living organism to extract from the incoming visual information the part that is most important at a given moment and that should be processed in more detail. For example, detailed processing of the extracted information can include novelty detection and allocation of a novel object to memory.In this project a large-scale brain-inspired model of hierarchically organised spiking neurons will be developed, that solves the problem of consecutive selection of objects by combining object oriented attention, memory, and novelty detection. Since we believe that the brain does not invent a special processing mechanism for each cognitive function but adapts similar mechanisms for a particular type of processing, it is a challenge to develop a model based on a small set of general principles of information processing (e.g. synchronisation, adaptation of natural frequencies, resonance amplitude increase). We believe that these theoretical principles are the key to the performance of the biological brain and within the proposed research will be implemented for the first time in combined model of attention and memory. Such developments offer great potential, both in shedding fresh light on the basic mechanisms underpinning information processing in the brain and in the design of a new generation of computational devices, cognitive robots, etc.