Bayesian inference with probabilistic population codes

By Wei Ji Ma, Jeffrey M. Beck, Peter E. Latham, Alexandre Pouget. Nature Neuroscience, 2006

738 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/462ebec7d0865d79a74e2b61b5758a4d51b96fbf

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Citations in the corpus

Improved probabilistic inference as a general learning mechanism with action video games (2010, background): “This interpretation is based on recent theories in which neural patterns of activity represent probability distributions over the encoded variables [28–31].”
Multisensory integration in macaque visual cortex depends on cue reliability (2008, background): “Our findings appear contrary to the assumption of fixed weights in the theory of Ma et al. (2006) .”
Deep neural networks: a new framework for modelling biological vision and brain information processing (2015, background): “Another deterministic proposal is based on probabilistic population codes (Ma et al. 2006).”
Neural Implementation of Hierarchical Bayesian Inference by Importance Sampling (2009, background): “Several studies have suggested that this form of cue combination could be implemented by population coding [2] [8].”
Synaptic sampling: A connection between PSP variability and uncertainty explains neurophysiological observations (2015, background): “about the state of the world and the synaptic weights) by combining samples is trivial, while there is currently no mechanism that can be used to combine sampling with, for instance, a probabilistic population code [27, 28, 29].”
A Neural Network Model of Ventriloquism Effect and Aftereffect (2012, positive): “Some theoretical studies [48–50] overcame previous limitations proposing a neural implementation of optimal Bayesian integration (gain-encoding scheme [49]).”
Neurons as Monte Carlo Samplers: Bayesian Inference and Learning in Spiking Networks (2014, background): “Several models have been proposed based on populations of neurons to represent probability distribution [5, 6, 7, 8].”
Cardinal rules: Visual orientation perception reflects knowledge of environmental statistics (2011, neutral): “Finally, eliminating both non-uniformities (that is, an equal-spaced population of neurons with identical tuning, as is commonly assumed in the population coding literatur”
A Normalization Model of Multisensory Integration (2011, positive): “A recent computational theory 15 has demonstrated that populations of neurons with Poisson-like spiking statistics can achieve Bayes-optimal cue integration if each multi-sensory neuron simply sums its inputs.”
Navigating the Neural Space in Search of the Neural Code. (2017, neutral): “…1994; Hanks et al., 2015; Harvey et al., 2012; Jazayeri and Movshon, 2006; Laurent et al., 2001; Lo and Wang, 2006; Mante et al., 2013; Ma et al., 2006; Moore and Armstrong, 2003; Raposo et al., 2014; Rigotti et al., 2013; Siegel et al., 2015; Znamenskiy and Zador, 2013), and they are…”
Dynamic models of large-scale brain activity (2017, background): “The FPE also describes the dynamics of the population variance, corresponding to the precision with which the ensemble response is represented 39,40 .”
Probabilistic brains: knowns and unknowns (2013, background): “This implies that the code for the posterior distribution, p ( s | r ), can be deduced from the form of the neural variability, p ( r | s ) 47 .”