Large scale spatio-temporal point processes: novel machine learning methodologies and application to neural multi-electrode arrays.

由于数据收集技术的进步，大尺度时空数据集的可用性越来越高。在这个建议中，我们关注基于事件的数据：数据点是事件的空间和时间坐标，而不是变量的模拟测量。这些数据在从流行病学到社会科学的许多应用中都很普遍，并且带来了相当大的计算问题：数据本质上是高维的（如果在连续时间框架中工作，实际上是无限维的）和非线性的，并且它通常是对复杂动态过程的嘈杂观察。这种数据类型迫切需要可扩展的数据建模解决方案，并且需要在计算统计学和机器学习方面进行新颖的研究。在本提案中，我们将解决基本的方法问题，这些问题是由高密度多电极阵列（HD-MEA）记录神经电活动在生物医学场景中的应用所激发的。这些电子芯片具有许多（bbb1000）记录通道，用于测量一系列体外制剂中的电活动，并能够同时测量数千个神经元的尖峰活动。这项新技术（在过去的五年里进行了商业化开发）有可能使科学家们能够回答有关神经元如何相互交流的基本问题，同时作为一种有效的工具，它具有直接转化的潜力，可以在体外测试药物治疗对神经元功能的影响。为这些数据提供数据建模工具是具有挑战性的：HD-MEA记录是大数据（数据速率约3GB/分钟）的主要示例，其中复杂的行为排除了简单的可扩展模型。在这个令人兴奋的多学科项目中，我们建议将HD-MEA数据集视为时空点过程（随机点集，即神经尖峰）的实现，并使用和开发贝叶斯统计和机器学习技术来推断数据背后生物物理过程的显著动态特性。将要解决的主要挑战是设计统计机器学习方法，该方法可以适应非线性，并且可以扩展到HD-MEA数据的大尺寸，同时仍然提供生物学上有意义的见解。特别是，我们将重点关注从数据中确定神经元网络的连通性，神经元内在动力学，以及化学（例如给药于培养物）和其他刺激如何影响培养物的电反应和网络特性。解决这些挑战将需要对大规模时空点过程的近似贝叶斯推理进行大量工作，生成将普遍适用于许多其他科学和工程领域的方法。该项目汇集了PI和RA的机器学习和系统生物学专业知识，coI的神经科学专业知识以及与国际实验组和行业参与者的紧密合作关系，使研究团队非常适合解决这个具有挑战性的项目。

Large scale spatio-temporal data sets are becoming increasingly available due to progress in data gathering technology. In this proposal we are concerned with event-based data: data points are spatial and temporal coordinates of an event, as opposed to analogue measurements of a variable. Such data is pervasive in a number of applications, ranging from epidemiology to social sciences, and poses considerable computational issues: the data is intrinsically high dimensional (indeed infinite dimensional if working in a continuous time framework) and nonlinear, and it is often a noisy observation of complex dynamical processes. Scalable data modelling solutions for this data type are urgently needed, and will require novel research in computational statistics and machine learning. In this proposal, we will address fundamental methodological problems motivated by an application of great relevance in a biomedical scenario: recordings of neural electrical activity by High Density Multi Electrode Arrays (HD-MEA). These are electronic chips with many (>1000) recording channels, which are used to measure electrical activity in a range of in vitro preparations, and enable simultaneous measurement of the spiking activity of thousands of neurons. This novel technology (commercially developed within the last five years) has the potential to enable scientists to answer fundamental questions on how neurons communicate between each other, as well as having direct translational potential as an effective tool to test in vitro the impact of drug treatment over neuronal function. Providing data modelling tools for such data is challenging: HD-MEA recordings are a prime example of big data (data rates of ~3GB/minute) where complex behaviours rule out simple scalable models.In this exciting multi-disciplinary project, we propose to treat an HD-MEA data set as a realisation of a spatio-temporal point-process (a random set of points, i.e. neural spikes), and use and develop techniques from Bayesian statistics and machine learning to infer salient dynamical properties of the biophysical process underlying the data. The major challenges which will be addressed are concerned with devising statistical machine learning methods which can accommodate non-linearities and that can scale to the large size of HD-MEA data, while still giving biologically meaningful insights. In particular, we will focus on determining from data the connectivity of the network of neurons, neuron-intrinsic dynamics, and how chemical (e.g. drugs administered to the culture) and other stimuli influence the electrical response and network properties of the culture. Addressing these challenges will entail considerable work on approximate Bayesian inference for large-scale spatio-temporal point processes, generating methodologies which will be general and applicable to many other domains of science and engineering.The project brings together the machine learning and systems biology expertise of the PI and named RA, the neuroscience expertise of the coI as well as strong collaborative ties with international experimental groups and industrial players, making the team of researchers ideally suited to tackle this challenging project.