The Bayesian brain¶
Knill, D. C., & Pouget, A. (2004). The Bayesian brain: the role of uncertainty in neural coding and computation. Trends in Neurosciences, 27(12), 712-719.
Cited by¶
2 citations across 2 artifacts.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Regularization
- Signal processing and inverse problems. Tikhonov regularization, total-variation denoising, and wavelet thresholding all penalize non-smooth or non-sparse reconstructions of an ill-posed inverse problem. Optimization and numerical analysis. Regularized formulations of ill-conditioned problems, barrier methods that penalize approach to constraint boundaries, and Tikhonov regularization of near-singular linear systems. Cognitive science and neuroscience. Bayesian-brain accounts treat perception and learning as regularized inference, with priors playing the role of penalty terms and the regularization weight mapping onto neural precision.
This sourceTreats perception and learning as regularized Bayesian inference, with priors playing the role of penalty terms and precision the regularization weight.
- Signal processing and inverse problems. Tikhonov regularization, total-variation denoising, and wavelet thresholding all penalize non-smooth or non-sparse reconstructions of an ill-posed inverse problem. Optimization and numerical analysis. Regularized formulations of ill-conditioned problems, barrier methods that penalize approach to constraint boundaries, and Tikhonov regularization of near-singular linear systems. Cognitive science and neuroscience. Bayesian-brain accounts treat perception and learning as regularized inference, with priors playing the role of penalty terms and the regularization weight mapping onto neural precision.
Mechanisms¶
- Bayesian Cue Integration Model
- … but miscalibrated likelihood behaves like an overconfident expert and hijacks the posterior; a poorly chosen prior silently tilts every result; and cues assumed independent that are not will make the posterior far too sharp — the same over-counting that bites simple averaging, now hidden inside the likelihoods.
This sourceBayesian perception combines prior knowledge with likelihood functions, so the specified prior and sensory uncertainty influence posterior estimates.
- … but miscalibrated likelihood behaves like an overconfident expert and hijacks the posterior; a poorly chosen prior silently tilts every result; and cues assumed independent that are not will make the posterior far too sharp — the same over-counting that bites simple averaging, now hidden inside the likelihoods.
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