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SUMMARY:Computational Neuroscience Journal Club - Calvin Kao (CBL)
DTSTART:20180206T160000Z
DTEND:20180206T170000Z
UID:TALK100879@talks.cam.ac.uk
CONTACT:Rodrigo Echeveste
DESCRIPTION:Calvin Kao will cover:\n\n• Single-trial dynamics of motor c
 ortex and their applications to brain-machine interfaces\n\n• J.C. Kao\,
  P. Nuyujukian\, S.I. Ryu\, M.M. Churchland\, J.P. Cunningham & K.V. Sheno
 y\n\n• Nature Communications (2015)\n\n• https://www.nature.com/articl
 es/ncomms8759\n\n\nAbstract: Increasing evidence suggests that neural popu
 lation responses have their own internal drive\, or dynamics\, that descri
 be how the neural population evolves through time. An important prediction
  of neural dynamical models is that previously observed neural activity is
  informative of noisy yet-to-be-observed activity on single-trials\, and m
 ay thus have a denoising effect. To investigate this prediction\, we built
  and characterized dynamical models of single-trial motor cortical activit
 y. We find these models capture salient dynamical features of the neural p
 opulation and are informative of future neural activity on single trials. 
 To assess how neural dynamics may beneficially denoise single-trial neural
  activity\, we incorporate neural dynamics into a brain–machine interfac
 e (BMI). In online experiments\, we find that a neural dynamical BMI achie
 ves substantially higher performance than its non-dynamical counterpart. T
 hese results provide evidence that neural dynamics beneficially inform the
  temporal evolution of neural activity on single trials and may directly i
 mpact the performance of BMIs.
LOCATION:Cambridge University Engineering Department\, CBL\, BE4-38 (http:
 //learning.eng.cam.ac.uk/Public/Directions)
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