@Article{Jazayeri2006,
  author   = {Mehrdad Jazayeri and J. Anthony Movshon},
  journal  = {Nat Neurosci},
  title    = {Optimal representation of sensory information by neural populations.},
  year     = {2006},
  number   = {5},
  pages    = {690-6},
  volume   = {9},
  abstract = {Sensory information is encoded by populations of neurons. The responses
	of individual neurons are inherently noisy, so the brain must interpret
	this information as reliably as possible. In most situations, the
	optimal strategy for decoding the population signal is to compute
	the likelihoods of the stimuli that are consistent with an observed
	neural response. But it has not been clear how the brain can directly
	compute likelihoods. Here we present a simple and biologically plausible
	model that can realize the likelihood function by computing a weighted
	sum of sensory neuron responses. The model provides the basis for
	an optimal decoding of sensory information. It explains a variety
	of psychophysical observations on detection, discrimination and identification,
	and it also directly predicts the relative contributions that different
	sensory neurons make to perceptual judgments.},
  doi      = {10.1038/nn1691},
  keywords = {Afferent, Animals, Automatic Data Processing, Brain, Discrimination (Psychology), Humans, Likelihood Functions, Models, Nerve Net, Neurological, Neurons, Stochastic Processes, Visual Fields, Visual Perception, 16617339},
}

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