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

人工智能存在于我们的日常生活中，并具有全球影响力。机器学习中常见的方法是使用人工神经网络，与监督学习一起使用。它们在分类任务中取得了最先进的性能。这些网络使用模拟神经元模型，与更精确的生物物理神经元模型相比，模拟神经元模型简化了动力学。这些更精确的模型被称为尖峰神经元模型，这些模型的网络被称为尖峰神经网络（snn）。snn领域是当前研究兴趣的一个领域，有许多关于神经元动力学、网络架构和学习规则的开放性问题，以最大限度地提高这些更具生物学真实性的网络模型的性能。该项目将考虑在基准问题中训练尖峰神经网络的不同方法，重点是自主导航。该项目的目的是探索在snn中使用监督、无监督和半监督学习。目标是：-回顾当前SNN动态建模和定义网络架构的方法。-确定候选学习算法，重点关注snn中的无监督和半监督方法。-定义自主导航中的基准问题，并考虑SNN控制系统最合适的输入和输出映射。-计划、承担和分析来自一系列实验的数据，探索所选SNN学习算法和输入输出映射的行为。-在博士论文中报告结果。研究方法将采用生物启发的方法，将神经科学的信息映射到可以应用于现实世界问题的基于软件的实现中。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方面的培训。软件将使用第三方软件和定制开发的软件（可能是Python和/或MATLAB）构建。这项工作将有助于开发新的人工智能技术，以补充现有的人工神经网络方法。该项目与EPSRC的研究主题：信息通信技术和人工智能技术保持一致。

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.