An Investigation into Learning Methods for Spiking Neural Networks in the Context of Autonomous Navigation

自主导航背景下尖峰神经网络学习方法的研究

• 批准号: 2603096
• 金额: --
• 依托单位: University of York
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2603096
• 关键词: Investigation; into; Learning; Methods; Spiking; Investigation; into; Learning; Methods; Spiking

Artificial intelligence is present throughout our everyday lives and has a global reach. A common approach in machine learning is the use of artificial neural networks, used with supervised learning. These have achieved state-of-the-art performance in classification tasks. These networks use analogue neuron models which have simplified dynamics compared to more biophysically accurate neuron models. These more accurate models are known as spiking neuron models, and networks of these are referred to as spiking neural networks (SNNs). The field of SNNs is an area of current research interest with a number of open questions regarding neuronal dynamics, network architecture and learning rules to maximise the performance of these more biologically realistic network models. This project will consider different approach to training spiking neural networks in benchmark problems, with a focus on autonomous navigation. The aim of the project is to explore the use of supervised, unsupervised and semi-supervised learning in SNNs. The objectives are to:-Review current approaches to modelling SNN dynamics and defining network architecture. -Identify candidate learning algorithms to apply with a focus on unsupervised and semi-supervised approaches in SNNs.-To define a benchmark problem in autonomous navigation with consideration of the most appropriate input and output mappings for an SNN control system.-To plan, undertake and analyse data from a series of experiments exploring the behaviour of the chosen SNN learning algorithms and input-output mappings.-To report the results in a PhD thesis.The research methodology will take a bio-inspired approach that maps information from neuroscience into software based implementations that can be applied to real-world problems. The project will provide training in computational neuroscience, machine learning, software development and experimental data analysis. Software will be constructed using both third party software and custom developed software (likely to be in either Python and/or MATLAB). The work will contribute to the development of new AI technologies to complement existing artificial neural network approaches. The project is aligned with EPSRC research themes: ICT and Artificial Intelligence Technologies.

人工智能在我们的整个日常生活中都存在，并具有全球影响力。机器学习中的一种常见方法是使用人工神经网络，与监督学习一起使用。这些在分类任务中实现了最先进的表现。与更精确的神经元模型相比，这些网络使用模拟神经元模型，这些模型简化了动力学。这些更准确的模型称为尖峰神经元模型，它们的网络称为尖峰神经网络（SNN）。 SNN的领域是当前研究兴趣领域的领域，其中有许多有关神经元动态，网络体系结构和学习规则的开放问题，以最大程度地提高这些更现实的网络模型的性能。该项目将考虑在基准问题中培训尖峰神经网络的不同方法，重点是自动导航。该项目的目的是探索SNN中受监督，无监督和半监督的学习的使用。这些目标是： - 对建模SNN动力学和定义网络体系结构进行建模的当前方法。 - 识别候选人学习算法，以专注于SNN中的无监督和半监督的方法，以考虑自主导航中的基准问题，考虑到SNN控制系统的最合适的输入和输出映射。进行计划，进行计划，并从事一系列实验，以探索SNN的销量，并在SNN学习的行为中，并在Chorith学习中，并在Chorith中进行了研究。博士学位论文。研究方法将采用一种生物启发的方法，将神经科学的信息映射到基于软件的实现中，这些信息可用于现实世界中的问题。该项目将提供计算神经科学，机器学习，软件开发和实验数据分析的培训。软件将使用第三方软件和自定义开发的软件（可能位于Python和/或MATLAB）来构建。这项工作将有助于开发新的AI技术，以补充现有的人工神经网络方法。该项目与EPSRC研究主题一致：ICT和人工智能技术。