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SUMMARY:Computational Neuroscience Journal Club - Zahara Girones and Ivan 
 Tomic
DTSTART:20210504T140000Z
DTEND:20210504T153000Z
UID:TALK160165@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 'Efficient coding and natural scenes representation’ presented by 
 Zahara Girones and Ivan Tomic.\n\nZoom information: https://us02web.zoom.u
 s/j/84958321096?pwd=dFpsYnpJYWVNeHlJbEFKbW1OTzFiQT09\n\nThe efficient codi
 ng hypothesis has been successful in explaining various aspects of sensory
  neural coding. According to this hypothesis\, sensory systems are optimiz
 ed to maximize the transmitted information (or some other measure of codin
 g fidelity) about the natural environment\, under biological resource cons
 traints. Bayesian models of perception\, which view perception as inferenc
 e from noisy sensory evidence merged with prior expectations\, have also b
 een successful in accounting for various perceptual biases and illusions. 
 However\, some perceptual biases appear to have an anti-Bayesian character
 . We will review a series of papers that explain this and other perceptual
  phenomena by combining Bayesian decoding with efficient sensory encoding.
  In these models both the encoder (via efficient coding) and the Bayesian 
 decoder (via the prior distribution) are adapted to natural stimulus stati
 stics. We will discuss how these theories relate the variations of percept
 ual bias and psychophysical discriminability over stimulus space to each o
 ther and to the distribution of stimuli in the natural environment\, and w
 ill discuss how different formulations of efficient coding lead to quantit
 atively different predictions for these relationships. At the end we will 
 look at whether the same principles can be applied to domains beyond low-l
 evel perceptual processing\, such as subjective valuations.\n\nList of ref
 erences:\n\nD. Ganguli\, E. P. Simoncelli. Efficient Sensory Encoding and 
 Bayesian Inference with Heterogeneous Neural Populations. Neural Comput 20
 14\; 26 (10): 2103–2134. \nhttps://doi.org/10.1162/NECO_a_00638\n\nX. We
 i\, A. Stocker. A Bayesian observer model constrained by efficient coding 
 can explain 'anti-Bayesian' percepts. Nat Neurosci 18\, 1509–1517 (2015)
 . \nhttps://doi.org/10.1038/nn.4105\n\nX. Wei & A.Stocker. Lawful relation
  between perceptual bias and discriminability. PNAS (2017).\nhttps://doi.o
 rg/10.1073/pnas.1619153114\n\nM. Morais\, J. W. Pillow. Power-law efficien
 t neural codes provide general link between perceptual bias and discrimina
 bility. NeurIPS (2018).\nhttp://pillowlab.princeton.edu/pubs/Morais18_Neur
 IPS_powerlawefficientcoding.pdf\n\nR. Polanía\, M. Woodford & C.C. Ruff. 
 Efficient coding of subjective value. Nat Neurosci 22\, 134–142 (2019).\
 nhttps://doi.org/10.1038/s41593-018-0292-0\n
LOCATION:Online on Zoom
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