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SUMMARY:Computational Neuroscience Journal Club - Marine Schimel and David
  Liu
DTSTART:20210928T130000Z
DTEND:20210928T143000Z
UID:TALK162556@talks.cam.ac.uk
CONTACT:Jake Stroud
DESCRIPTION:Please join us for our fortnightly journal club online via zoo
 m where two presenters will jointly present a topic together. The next top
 ic is ‘Brain-computer interfaces' presented by Marine Schimel and David 
 Liu.\n\nZoom information: https://us02web.zoom.us/j/84958321096?pwd=dFpsYn
 pJYWVNeHlJbEFKbW1OTzFiQT09 \nMeeting ID: 841 9788 6178 \nPasscode: 659046\
 n\nSummary:\n\nEver since the first recordings of human brain activity aro
 und a century ago\, developments in recording techniques have greatly impr
 oved the quality\, variety and scale of neural data collected in neuroscie
 nce experiments. To test and expand our understanding of the brain\, one h
 as to perform experiments that involve recording and potentially manipulat
 ing activity in live neural populations simultaneously with behaviour and/
 or sensory input. The relation between the observed activity and the exter
 nal behaviour or input allows one to characterize the meaning and function
  of such neural signals. Due to the complexity of neural activity\, the an
 alysis of neural data relies on powerful statistical tools and computation
 al methods that run on silicon hardware. When combining all these aspects 
 into a single framework\, one naturally arrives at the idea of brain-machi
 ne interfaces (BMIs) or brain-computer interfaces (BCIs). Such setups aim 
 to provide\, as the name suggests\, a direct interface to observe and pote
 ntially control neural activity through software. If successful\, such con
 structions open up many new avenues for testing neuroscience theories\, ex
 ploring neural activity\, and more practical applications like reading int
 ended behaviour.\nWe present three papers\, covering both theory as well a
 s practical aspects of BMIs\, showing their importance in both basic scien
 tific study of the brain as well as potential for medical applications in 
 neurological disorders. As improvements in bio-engineering and neural reco
 rding technologies are converging with recent strides in signal processing
  and machine learning\, BMIs are becoming an exciting topic for both scien
 ce and real-world applications.\n\nLearning by neural reassociation\,\nGol
 ub\, M.D.\, Sadtler\, P.T.\, Oby\, E.R. et al.\, Nature Neuroscience (2018
 )\n\nCortical control of virtual self-motion using task-specific subspaces
 \,\nSchroeder\, K.E.\, Perkins\, S.M.\, Wang\, Q.\, Churchland\, M.M.\, bi
 oRxiv (2020)\n\nHigh-performance brain-to-text communication via handwriti
 ng\,\nWillett\, F.R.\, Avansino\, D.T.\, Hochberg\, L.R. et al.\, Nature (
 2021)
LOCATION:Online on Zoom
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