What does the retina know about natural scenes?
Neural Computation
What is the goal of sensory coding?
Neural Computation
Bayesian decision theory and psychophysics
Perception as Bayesian inference
Implications of a Bayesian formulation of visual information for processing for psychophysics
Perception as Bayesian inference
Introduction: a Bayesian formulation of visual perception
Perception as Bayesian inference
Prior Learning and Gibbs Reaction-Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Minimal Local-Asperity Hypothesis of Early Retinal Lateral Inhibition
Neural Computation
Statistical Edge Detection: Learning and Evaluating Edge Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
| Hi-index | 0.00 |
Adaptation allows biological sensory systems to adjust to variations in the environment and thus to deal better with them. In this article, we propose a general framework of sensory adaptation. The underlying principle of this framework is the setting of internal parameters of the system such that certain prespecified tasks can be performed optimally. Because sensorial inputs vary probabilistically with time and biological mechanisms have noise, the tasks could be performed incorrectly. We postulate that the goal of adaptation is to minimize the number of task errors. This minimization requires prior knowledge of the environment and of the limitations of the mechanisms processing the information. Because these processes are probabilistic, we formulate the minimization with a Bayesian approach. Application of this Bayesian framework to the retina is successful in accounting for a host of experimental findings.