Hierarchical Bayesian inference in the visual cortex

Hierarchical Bayesian inference in the visual cortex.

Traditional views of visual processing suggest that early visual neurons in areas V1 and V2 are static spatiotemporal filters that extract local features from a visual scene. The extracted information is then channeled through a feedforward chain of modules in successively higher visual areas for further analysis. Recent electrophysiological recordings from early visual neurons in awake behavin...

Bayesian inference and attentional modulation in the visual cortex.

The responses of neurons in cortical areas V2 and V4 can be significantly modulated by attention to particular locations within an input image. We show that such effects emerge naturally when perception is viewed as a probabilistic inference process governed by Bayesian principles and implemented in hierarchical cortical networks. The proposed model can explain a rich variety of attention-relat...

A hierarchical Bayesian model of the early visual cortex

Recent studies have proposed hierarchical Bayesian model of visual, or other multidimensional signal processing the scaling factors of linear components in which are represented by higherorder components. Here we constructed a two-layer Bayesian model of the early visual cortex and investigated the model properties to provide a explanation of how visual system acquires the even-symmetric respon...

Hierarchical Bayesian ARX models for robust inference

Gaussian innovations are the typical choice in most ARX models but using other distributions such as the Student’s t could be useful. We demonstrate that this choice of distribution for the innovations provides an increased robustness to data anomalies, such as outliers and missing observations. We consider these models in a Bayesian setting and perform inference using numerical procedures base...

The Visual Cortex as a Hierarchical Predictor