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SUMMARY:Linking Bayesian models of perception and neural responses with sp
 ontaneous activity
DTSTART:20110128T121500
DTSTAMP:20260510T044913Z
UID:40fd70c34f8b14bbd10ccd7fd3bf9dcec6592941a64e55002483a069
CATEGORIES:Conferences - Seminars
DESCRIPTION:Pietro BERKES\, Volen Center for Complex Systems\, Brandeis Un
 iversity\nIn order to correctly interpret sensory stimuli and represent th
 em efficiently despite the ambiguities and noise that are pervasive in nat
 ural conditions\, the brain builds from experience internal models of its 
 environment. A number of recent behavioral studies suggest that the way th
 e brain combines evidence from sensation with the information captured by 
 internal models is well described by Bayesian statistics\, implying that n
 eurons must be able to represent and manipulate probability distributions.
  Uncovering the neural basis of such computations\, which remains largely 
 unknown\, is crucial to allow a fruitful exchange between computational an
 d experimental studies.\n\nWe recently proposed that spontaneous neural ac
 tivity in the visual cortex reveals an important functional aspect of inte
 rnal models\, namely their prior expectations about the environment. Since
  in an efficient model of vision the distribution of prior expectations sh
 ould match the distribution of the features inferred from natural images\,
  we predicted that the distribution of spontaneous neural responses should
  be identical to the one evoked by natural images\, but not by other stimu
 li. We analyzed the population activity of neurons in primary visual corte
 x of ferrets over development\, from eye-opening to maturity\, and found a
 n increasing similarity between the distribution of spontaneous activity a
 nd that of activity evoked by natural scenes but not by artificial stimuli
 \, revealing a gradual adaptation of the internal model to the statistics 
 of the visual environment. We confirmed the same hallmarks of internal mod
 els in the primary auditory cortex of adult ferrets\, suggesting that our 
 findings might uncover a general feature of representation and computation
  in sensory cortex. \n\nOur hypothesis provides a direct mapping of Bayesi
 an models of perception on neural activity. This link opens the opportunit
 y for a data-driven development and evaluation of models of visual process
 ing\, and the development of methods for the decoding of neural responses 
 that would take into account the information contained in ongoing cortical
  activity.
LOCATION:BC 01 https://plan.epfl.ch/?room==BC%2001
STATUS:CONFIRMED
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