Abstract

Please join us for our fortnightly journal club online via zoom where two presenters will jointly present a topic together. The next topic is ‘Brain-computer interfaces’ presented by Marine Schimel and David Liu.

Zoom information: https://us02web.zoom.us/j/84958321096?pwd=dFpsYnpJYWVNeHlJbEFKbW1OTzFiQT09 Meeting ID: 841 9788 6178 Passcode: 659046

Summary:

Ever since the first recordings of human brain activity around a century ago, developments in recording techniques have greatly improved the quality, variety and scale of neural data collected in neuroscience experiments. To test and expand our understanding of the brain, one has to perform experiments that involve recording and potentially manipulating activity in live neural populations simultaneously with behaviour and/or sensory input. The relation between the observed activity and the external behaviour or input allows one to characterize the meaning and function of such neural signals. Due to the complexity of neural activity, the analysis of neural data relies on powerful statistical tools and computational methods that run on silicon hardware. When combining all these aspects into a single framework, one naturally arrives at the idea of brain-machine interfaces (BMIs) or brain-computer interfaces (BCIs). Such setups aim to provide, as the name suggests, a direct interface to observe and potentially control neural activity through software. If successful, such constructions open up many new avenues for testing neuroscience theories, exploring neural activity, and more practical applications like reading intended behaviour. We present three papers, covering both theory as well as practical aspects of BMIs, showing their importance in both basic scientific study of the brain as well as potential for medical applications in neurological disorders. As improvements in bio-engineering and neural recording technologies are converging with recent strides in signal processing and machine learning, BMIs are becoming an exciting topic for both science and real-world applications.

Learning by neural reassociation, Golub, M.D., Sadtler, P.T., Oby, E.R. et al., Nature Neuroscience (2018)

Cortical control of virtual self-motion using task-specific subspaces, Schroeder, K.E., Perkins, S.M., Wang, Q., Churchland, M.M., bioRxiv (2020)

High-performance brain-to-text communication via handwriting, Willett, F.R., Avansino, D.T., Hochberg, L.R. et al., Nature (2021)