Brain inspired machine learning methods for analysis of neural data

神经科学的最新发展已经产生了能够同时记录数百甚至数千个神经元(脑细胞)活动的电子探针。这为神经科学家发现新的大脑功能原理提供了可能性，而以前的技术只能同时从单个神经元或少数几个神经元进行记录，这是不可能的。不幸的是，尽管记录这些数据的技术非常先进，但我们分析数据的能力并没有跟上。已经提出了许多重要的数学方法。它们有一些希望，但有一个共同的问题：它们发现数据中的模式，但它们不告诉你大脑本身是否或如何利用这些模式。这项研究的目的是使用大脑启发的方法来分析数据，这样在数据中发现的模式也可以被大脑本身发现。这将导致与大脑如何运作的假设比现有方法更紧密地联系在一起。这项研究提出了将神经建模和机器学习(最近在解决以前被认为需要人类智力的任务方面产生了令人难以置信的结果的技术的集合)相结合的混合方法。通过将神经模型结合到分析中，我们确保结果本身就是大脑可能自己发现的东西。通过结合机器学习，我们使用了目前最知名的、最先进的方法来检测模式。这项研究有可能在未来发现大脑是如何工作的，以及它如何在许多重要任务中超越计算机。它属于EPSRC的以下研究领域：生物信息学；人工智能技术。

Recent developments in neuroscience have produced electrical probes capable of recording the activity of hundreds or even thousands of neurons (brain cells) simultaneously. This opens the possibility for neuroscientists to discover new principles of brain function that would not have been possible with previous technology that could only record from a single neuron or a handful of neurons simultaneously. Unfortunately, despite the technology to record this data being very advanced, our ability to analyse the data has not kept pace. A number of heavily mathematical methods have been proposed. These have some promise, but share a common problem: they discover patterns in the data but they do not tell you if or how the brain itself might make use of those patterns.The aim of this research is to use brain-inspired methods to analyse the data, so that patterns that are discovered in the data would also be discoverable by the brain itself. This will lead to hypotheses that are much more closely linked to how the brain functions than existing methods.This research proposes hybrid methods that combine neural modelling with machine learning (a collection of techniques that has recently produced incredible results in solving tasks that were previously thought to require human intelligence). By incorporating neural models into the analysis, we ensure that the results are themselves something that the brain could potentially discover itself. By incorporating machine learning, we use the best currently known, state of the art methods for detecting patterns.This research has the potential to enable significant future discoveries about how the brain works and how it outperforms computers at many important tasks. It falls under the remit of the following EPSRC research areas: biological informatics; artificial intelligence technologies.