Inference and computation with population codes.

By Alexandre Pouget, Peter S. Dayan, Richard S. Zemel. Annual Review of Neuroscience, 2003

489 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/be561e5f1a315564ad0dc77d1f499d531768e900

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

Citations in the corpus

Emerging concepts for the dynamical organization of resting-state activity in the brain (2010, unknown): “It seems reasonable to link this behaviour to the notion from Bayes’ models of the brain 69,70 : by being in a constant state of exploration the brain generates predictions about likely future network configurations that will be appropriate for novel contexts.”
Uncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioral control (2005, background): “First, along with direct, population-code representations of uncertaint”
Value-based gain control: divisive normalization in parietal cortex (2011, background): “Cortical neurons exhibit considerable variability in their spiking activity, and information is likely represented by distributions of neurons and spiking rates (Tolhurst et al., 1983; Shadlen and Newsome, 1998; Pouget et al., 2003).”
Visual adaptation: physiology, mechanisms, and functional benefits. (2007, background): “Second, the neurons that contribute most to a particular task may be difficult to determine and may change when tuning or response strength is altered (Butts and Goldman 2006; Pouget et al. 2003).”
Primary Visual Cortex Represents the Difference Between Past and Present (2013, background): “This was in sharp contrast to more rapid sequences for which we found an ongoing representation of current input, consistent with earlier studies.”
Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging (2007, background): “Finally, spontaneous activity may represent a dynamic prediction about expected us”
Causal Inference in Multisensory Perception (2007, background): “It is wellknown that neural populations naturally encode probability distributions over stimuli through Bayes’ rule, a type of coding known as probabilistic population coding [33,34].”
Noise characteristics and prior expectations in human visual speed perception (2006, result): “If we associate the measurement ~ m with responses of MT neurons, the estimate must be computed in subsequent neural stages [38] and should be consistent with the prior, as well as the likelihood associated with the MT population response [39].”
Modeling development of natural multi-sensory integration using neural self-organisation and probabilistic population codes (2015, background): “Of particular interest are probabilistic population code (PPCs), and especially topographic PPCs, population codes in which each neuron has a preferred value of some variable and encodes in its activity the probability of this value given the input (Pouget, Dayan, & Zemel, 2003).”