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Optimal representation of sensory information by neural populations
 

Summary: Optimal representation of sensory information by
neural populations
Mehrdad Jazayeri & J Anthony Movshon
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.
The ability to detect, discriminate and identify sensory signals is limited
by how efficiently information in sensory representations is put to use
in the control of behavior. A stimulus activates a population of neurons
in various areas of the brain. To guide behavior, the brain must correctly
decode this population response and extract the sensory information as
reliably as possible. Two important factors make this problem a
challenging one. First, each neuron's response is inherently variable:
repeated presentations of the same stimulus elicit different responses.
Second, sensory neurons represent moment-to-moment changes in
sensory input by rapidly changing their firing patterns. The neural

  

Source: Andrzejak, Ralph Gregor - Departament de Tecnologia, Universitat Pompeu Fabra
Movshon, Joseph Anthony - Center for Neural Science, New York University

 

Collections: Biology and Medicine; Computer Technologies and Information Sciences