Brain inspired machine learning methods for analysis of neural data

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.