BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Talks.cam//talks.cam.ac.uk//
X-WR-CALNAME:Talks.cam
BEGIN:VEVENT
SUMMARY:Computational Neuroscience Journal Club - Jean-Pascal Pfister and 
 Xizi Li
DTSTART:20210518T140000Z
DTEND:20210518T153000Z
UID:TALK160606@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 ‘Nonlinear filtering in neuroscience’ presented by Jean-Pascal P
 fister and Xizi Li.\n\nZoom information: https://us02web.zoom.us/j/8495832
 1096?pwd=dFpsYnpJYWVNeHlJbEFKbW1OTzFiQT09\n\nContinuously extracting relev
 ant information from a stream of inputs is a key machine learning problem 
 with a wide range of applications in neuroscience. Formally\, this problem
  - also known as nonlinear (Bayesian) filtering - aims at estimating the p
 osterior distribution (or filtering distribution) at time t of some latent
  variable given all the inputs up to time t.\n\nThis nonlinear filtering t
 heory can be applied in neuroscience from two different perspectives. Firs
 tly\, nonlinear filtering can be seen as a data analysis method where the 
 task is to extract relevant information from continuous neural recording (
 such as continuously estimating the position of a rat based on place cells
  activity or continuously estimating the intention of a patient in order t
 o control a neuroprothesis). Secondly\, nonlinear filtering can be seen as
  a computational principle that can be applied at different levels such as
  the behavioural level (e.g. continuously tracking the position of a prey)
 \, neuronal level (estimating the causes of the inputs to a neural network
 ) or even single synapse level (e.g estimating the presynaptic membrane po
 tential).\n\nIn the first part of this journal club\, we will review the t
 heory of nonlinear filtering with the formal solution given by the Kushner
 -Stratonovic equation. For a tutorial see [1].  We will highlight the limi
 tation of this formal solution in terms of practical applicability and des
 cribe the possible approximate solutions. In the second part of the journa
 l club we will discuss one specific application of nonlinear filtering in 
 the context of learning [2\,3] and highlight the specific predictions of a
  synaptic learning rule derived from this nonlinear filtering approach.\n\
 nRefs:\n\n[1] Kutschireiter\, A.\, Surace\, S. C.\, & Pfister\, J.-P. (202
 0). The Hitchhiker’s guide to nonlinear filtering. Journal of Mathematic
 al Psychology\, 94\, 102307. http://doi.org/10.1016/j.jmp.2019.102307\n\n[
 2] Aitchison\, L.\, Jegminat\, J.\, Menendez\, J. A.\, Pfister\, J.-P.\, P
 ouget\, A.\, & Latham\, P. E. (2021). Synaptic plasticity as Bayesian infe
 rence. Nature Neuroscience\, 24\, 565–571. \nhttp://doi.org/10.1038/s415
 93-021-00809-5\n\n[3] Jegminat\, J.\, & Pfister\, J.-P. (2020). Learning a
 s filtering: \nimplications for spike-based plasticity. arXiv:2008.03198\n
LOCATION:Online on Zoom
END:VEVENT
END:VCALENDAR