Biomedical subjects

Nestor Caticha

Publications and source records attributed to Nestor Caticha.

4 recordsLinked to original sources

PubMed2005

Computational neurobiology of the flash-lag effect.

In the flash-lag effect (FLE) a moving object is perceived ahead of a stationary stimulus flashed in spatial alignment. Several explanations have been proposed to account for the FLE and its dependence on a variety of psychophysical attributes. Here, we show that a simple feed-forward network reproduces the standard FLE and several related manifestations, such as its modulation by stimulus luminance, trajectory, priming, and spatial predictability. A minimal set of elements, based on plausible neuronal mechanisms, yields a unified account of these visual illusions and possibly other perceptual phenomena.

Humans↗

PubMed2004

Multigrid priors for a Bayesian approach to fMRI.

We introduce multigrid priors to construct a Bayesian-inspired method to asses brain activity in functional magnetic resonance imaging (fMRI). A sequence of different scale grids is constructed over the image. Starting from the finest scale, coarse grain data variables are sequentially defined for each scale. Then we move back to finer scales, determining for each coarse scale a set of posterior probabilities. The posterior on a coarse scale is used as the prior for activity at the next finer scale. To test the method, we use a linear model with a given hemodynamic response function to construct the likelihood. We apply the method both to real and simulated data of a boxcar experiment. To measure the number of errors, we impose a decision to determine activity by setting a threshold on the posterior. Receiver operating characteristic (ROC) curves are used to study the dependence on threshold and on a few hyperparameters in the relation between specificity and sensitivity. We also study the deterioration of the results for real data, under information loss. This is done by decreasing the number of images in each period and also by decreasing the signal to noise ratio and compare the robustness to other methods.

Algorithms↗

PubMed2003

Superparamagnetic segmentation by excitable neural systems.

Magnetic modeling for clustering or segmentation purposes can either associate the image data to external quenched fields or to the interactions among a set of auxiliary variables. The latter gives rise to superparamagnetic segmentation and is usually done with Potts systems. We have used the superparamagnetic clustering technique to segment images, with the aid of different associated systems. Results using Potts model are comparable to those obtained using excitable FitzHugh-Nagumo and Morris-Lecar model neurons. Interactions between the associated system components are a function of the difference of luminosity on a gray scale of neighbor pixels and the difference of membrane potential.

Animals↗

PubMed2003

Dynamics of the evolution of learning algorithms by selection.

We study the evolution of artificial learning systems by means of selection. Genetic programming is used to generate populations of programs that implement algorithms used by neural network classifiers to learn a rule in a supervised learning scenario. In contrast to concentrating on final results, which would be the natural aim while designing good learning algorithms, we study the evolution process. Phenotypic and genotypic entropies, which describe the distribution of fitness and of symbols, respectively, are used to monitor the dynamics. We identify significant functional structures responsible for the improvements in the learning process. In particular, some combinations of variables and operators are useful in assessing performance in rule extraction and can thus implement annealing of the learning schedule. We also find combinations that can signal surprise, measured on a single example, by the difference between predicted and correct classification. When such favorable structures appear, they are disseminated on very short time scales throughout the population. Due to such abruptness they can be thought of as dynamical transitions. But foremost, we find a strict temporal order of such discoveries. Structures that measure performance are never useful before those for measuring surprise. Invasions of the population by such structures in the reverse order were never observed. Asymptotically, the generalization ability approaches Bayesian results.

Algorithms↗