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SUMMARY:Sensory prediction errors in thalamocortical circuits - Daniel Kor
 nai\; Máté Lengyel
DTSTART:20241029T133000Z
DTEND:20241029T150000Z
UID:TALK223936@talks.cam.ac.uk
CONTACT:124819
DESCRIPTION:A central feature of perception is that our internal expectati
 ons to a large degree shape how we perceive the world. Predictive coding (
 PC) is a popular framework to explain cortical responses to the violation 
 of our sensory expectations.\nIn the first part of the talk\, Prof. Máté
  Lengyel will introduce the theory of predictive coding\, and clarify how 
 PC and Bayesian inference can be distinguished\, despite sharing core comp
 utational concepts and addressing an overlapping set of empirical phenomen
 a. He will argue that predictive coding is an algorithmic/representational
  motif that can serve several different computational goals of which Bayes
 ian inference is but one.\nIn the second half of the talk\, Daniel Kornai 
 (1st year CBL PhD) will discuss results from three recent papers on the to
 pic of predictive coding:\nJiang and Rao (PLOS Comp. Biol\, 2024) show how
  their implementation of a Dynamical Predictive Coding model qualitatively
  reproduces several phenomena in visual processing\, including the emergen
 ce of spatio-temporal receptive fields\, a temporal and representational h
 ierarchy\, and predictive/postdictive artefacts in human motion perception
 .\nhttps://doi.org/10.1371/journal.pcbi.1011801\nA core hypothesis of pred
 ictive coding is that the content of error signals should reflect the diff
 erence between predicted and observed sensory input. Furutachi et al. (Nat
 ure\, 2024) use calcium imaging data from layer 2/3 neurons in V1 to infer
  the content of the error signal induced by a violation of a learned seque
 nce to test this claim.\nhttps://doi.org/10.1038/s41586-024-07851-w\nPredi
 ctive coding also implies that violations of learned sequences should indu
 ce a robust feedforward error signal that propagates up the visual process
 ing hierarchy. Westerberg et al (preprint\, 2024) use spiking data from mi
 ce and monkeys collected during an oddball task to investigate if the path
  of the error signal through various neuronal subpopulations involved in v
 isual processing match the expectations of PC.\nhttps://doi.org/10.1101/20
 24.10.02.616378  
LOCATION:CBL Seminar Room\, Engineering Department\, 4th floor Baker build
 ing
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