Brain-Inspired Neuronal Model of Attention and Memory

受大脑启发的注意力和记忆神经元模型

• 批准号: EP/D036364/1
• 负责人: Roman Borisyuk
• 金额: $ 19.77万
• 依托单位: Plymouth University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2006
• 资助国家: 英国
• 起止时间: 2006 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FD036364%2F1
• 关键词: Brain; Inspired; Neuronal; Model; Attention

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.

由于视觉系统有限的处理能力，排除了对整个视觉场景的快速分析，因此注意力对于生物体是必要的和至关重要的。选择性视觉注意是一种认知过程，允许生物体从传入的视觉信息中提取在给定时刻最重要的部分，并且应该更详细地处理。例如，提取的信息的详细处理可以包括新奇检测和分配一个新的对象到memory.In这个项目将开发一个大规模的大脑启发模型的分层组织尖峰神经元，解决了对象的连续选择问题相结合的面向对象的注意，记忆，和新奇检测。由于我们相信大脑不会为每种认知功能发明一种特殊的处理机制，而是为特定类型的处理适应类似的机制，因此开发一个基于信息处理的一小部分一般原则（例如同步，自然频率的适应，共振幅度增加）的模型是一个挑战。我们相信，这些理论原则是生物大脑性能的关键，并且在拟议的研究中将首次在注意力和记忆力的组合模型中实施。这些发展提供了巨大的潜力，无论是在揭示大脑信息处理的基本机制，还是在设计新一代计算设备，认知机器人等方面。

项目成果

Forecasting the 2005 General Election: A Neural Network Approach

预测 2005 年大选：神经网络方法

• DOI: 10.1111/j.1467-856x.2005.00182.x
• 发表时间: 2016
• 期刊: The British Journal of Politics and International Relations
• 作者: Borisyuk R

International symposium: theory and neuroinformatics in research related to deep brain stimulation.

国际研讨会：脑深部刺激相关研究的理论和神经信息学。

• DOI: 10.1586/17434440.4.5.587
• 发表时间: 2007
• 期刊: Expert review of medical devices
• 影响因子: 3.1
• 作者: Borisyuk R

Selective Attention Model of Moving Objects

• DOI: 10.1007/978-3-540-87559-8_37
• 发表时间: 2008-09
• 作者: R. Borisyuk;D. Chik;Y. Kazanovich

Stochasticity and functionality of neural systems: Mathematical modelling of axon growth in the spinal cord of tadpole

• DOI: 10.1016/j.biosystems.2008.03.012
• 发表时间: 2008-07-01
• 期刊: BIOSYSTEMS
• 影响因子: 1.6
• 作者: Borisyuk, Roman;Cooke, Tom;Roberts, Alan
• 通讯作者: Roberts, Alan

Model of the tadpole spinal cord: The interplay of deterministic and stochastic processes in development of specialised neural circuit

蝌蚪脊髓模型：特定神经回路发育中确定性过程和随机过程的相互作用

• DOI: 10.3182/20090622-3-uk-3004.00006
• 发表时间: 2009
• 期刊: IFAC Proceedings Volumes
• 作者: Borisyuk R