The nature of statistical learning theory
The nature of statistical learning theory
Probabilistic interpretation of population codes
Neural Computation
Neuronal tuning: to sharpen or broaden
Neural Computation
Narrow versus wide turning curves: what's best for a population code?
Neural Computation
Distributional population codes and multiple motion models
Proceedings of the 1998 conference on Advances in neural information processing systems II
Representational accuracy of stochastic neural populations
Neural Computation
Slow and Smooth: A Bayesian theory for the combination of local motion signals in human vision
Slow and Smooth: A Bayesian theory for the combination of local motion signals in human vision
Multidimensional Encoding Strategy of Spiking Neurons
Neural Computation
Difficulty of Singularity in Population Coding
Neural Computation
Exact Inferences in a Neural Implementation of a Hidden Markov Model
Neural Computation
Neural Computation
Bayesian spiking neurons i: Inference
Neural Computation
Cortical circuitry implementing graphical models
Neural Computation
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Perceptual inference fundamentally involves uncertainty, arising from noise in sensation and the ill-posed nature of many perceptual problems. Accurate perception requires that this uncertainty be correctly represented, manipulated, and learned about. The choices subjects make in various psychophysical experiments suggest that they do indeed take such uncertainty into account when making perceptual inferences, posing the question as to how uncertainty is represented in the activities of neuronal populations. Most theoretical investigations of population coding have ignored this issue altogether; the few existing proposals that address it do so in such a way that it is fatally conflated with another facet of perceptual problems that also needs correct handling: multiplicity (that is, the simultaneous presence of multiple distinct stimuli). We present and validate a more powerful proposal for the way that population activity may encode uncertainty, both distinctly from and simultaneously with multiplicity.