Rosario M Balboa | Search PubMed

PubMed2003

Power spectra and distribution of contrasts of natural images from different habitats.

Some theories for visual receptive fields postulate that they depend on the image statistics of the natural habitat. Consequently, different habitats may lead to different receptive fields. We thus decided to study how some of the most relevant statistics vary across habitats. In particular, atmospheric and underwater habitats were compared. For these habitats, we looked at two measures of the power spectrum and one of the distributions of contrasts. From power spectra, we analyzed the log-log slope of the fall and the degree of isotropy. From the distribution of contrasts, we analyzed the fall in a semi-log scale. Past studies found that the spatial power spectra of natural atmospheric images fall linearly in logarithmic axes with a slope of about -2 and that their distribution of contrasts shows an approximate linear fall in semi-logarithmic axes. Here, we show that the power spectrum of underwater images have statistically significantly steeper slopes ( approximately -2.5 in log-log axes) than atmospheric images. The vast majority of power spectra are non-isotropic, but their degree of anisotropy is extremely low, especially in atmospheric images. There are also statistical differences across habitats for the distribution of contrasts, with it falling faster for underwater images than for atmospheric ones. We will argue that these differences are due to the optical properties of water and that the differences have relevance for theories of visual receptive fields. These theories would predict larger receptive fields for aquatic animals compared to land animals.

Contrast Sensitivity↗

PubMed2002

A Bayesian framework for sensory adaptation.

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.

Adaptation, Physiological↗