Communications Biophysics

Contains research objectives and reports on one research project.U. S. Air Force under Contract AF19(604)-411

Underestimation of Visual Texture Slant by Human Observers: A Model

The perspective image of an obliquely inclined textured surface exhibits shape and density distortions of texture elements which allow a human observer to estimate the inclination angle of the surface. However, since the work of Gibson (1950) it has been known that, in the absence of other cues, humans tend to underestimate the slant angle of the surface, particularly when the texture is perceived as being irregular. The perspective distortions which affect texture elements also shift the projected spatial frequencies of the texture in systematic ways. Using a suitable local spectral filter to measure these frequency gradients, the inclination angle of the surface may be estimated. A computational model has been developed which performs this task using distributions of outputs from filters found to be a good description of simple cell receptive fields. However, for irregular textures the filter output distributions are more like those of regular textures at shallower angles of slant, leading the computational algorithm to underestimate the slant angle. This behavioral similarity between human and algorithm suggests the possibility that a similar visual computation is performed in cortex

Estimation of Textured Surface Inclination by Parallel Local Spectral Analysis

When an inclined, uniformly textured surface is viewed by an observer or imaged by a camera, the systematic distortions of the perspective transformation will induce a predictable distribution of shifts in the projected spatial frequencies which compose the texture. By measuring these shifts using a set of filters having suitable spatial, frequency, and orientation resolution, the inclination angles of the original textured surface may be estimated. An algorithm is presented which uses the amplitude distributions of 2D Gabor filters to perform such a calculation. Central to the algorithm is a pair of iteratively executed routines. The fist adjusts local sets of parameters to reduce the error between predicted and measured filter amplitudes. The second propagates the local parameters to neighboring regions to consolidate the estimates of inclination. The algorithm is capable of operating in parallel on any number of regions in the image and with a diverse set of filter inputs

Receptive Fields for the Determination of Textured Surface Inclination

The image of a uniformly textured inclined surface exhibits systematic distortions which affect the projection of the spatial frequencies of which the texture is composed. Using a set of filters having suitable spatial, frequency and orientation resolution, the inclination angle of the textured surface may be estimated from the resulting spatial frequency gradients. Psychophysical experiments suggest that, in absence of other cues, humans perceive surface inclination from perspective distortions, suggesting the possibility of a specific neuronal mechanism in the visual system. Beginning with a low level filter model found to be an accurate and economical model for simple cell receptive fields, we have developed both algorithmic machine vision and neural network models to investigate physiologically plausible mechanisms for this behavior. The two models are related through a new class of receptive field formed in the hidden layer of a neural network which learned to solve the problem. This receptive field can also be described analytically from the analysis developed for the algorithmic study. This paper, then, offers a prediction for a new type of receptive field in cortex which may be involved in the perception of inclined textured surfaces

Communications Biophysics

Contains reports on three research projects.United States Air Force (Contract AF19(604)-4112

First Passage Time Densities in Non-Markovian Models with Subthreshold Oscillations

Motivated by the dynamics of resonant neurons we consider a differentiable, non-Markovian random process $x(t)$ and particularly the time after which it will reach a certain level $x_b$. The probability density of this first passage time is expressed as infinite series of integrals over joint probability densities of $x$ and its velocity $\dot{x}$. Approximating higher order terms of this series through the lower order ones leads to closed expressions in the cases of vanishing and moderate correlations between subsequent crossings of $x_b$. For a linear oscillator driven by white or coloured Gaussian noise, which models a resonant neuron, we show that these approximations reproduce the complex structures of the first passage time densities characteristic for the underdamped dynamics, where Markovian approximations (giving monotonous first passage time distribution) fail

Surrogate Spike Train Generation Through Dithering in Operational Time

Detecting the excess of spike synchrony and testing its significance can not be done analytically for many types of spike trains and relies on adequate surrogate methods. The main challenge for these methods is to conserve certain features of the spike trains, the two most important being the firing rate and the inter-spike interval statistics. In this study we make use of operational time to introduce generalizations to spike dithering and propose two novel surrogate methods which conserve both features with high accuracy. Compared to earlier approaches, the methods show an improved robustness in detecting excess synchrony between spike trains

Communications Biophysics

Contains reports on three research projects.United States Air Force (Contract AF19(602)-4112

Risk of new onset diabetes mellitus in patients with asthma or COPD taking inhaled corticosteroids

SummaryBackgroundA recent case-controlled study reported an increased risk of diabetes mellitus in patients treated with inhaled corticosteroids for asthma or COPD, versus age-matched controls.ObjectiveThe purpose of the current study was to evaluate whether there was an increased risk of new onset diabetes mellitus or hyperglycaemia among patients with asthma or COPD treated with inhaled corticosteroids.MethodsA retrospective analysis evaluated all double-blind, placebo-controlled, trials in patients ≥4 years of age involving budesonide or budesonide/formoterol in asthma (26 trials; budesonide: n = 9067; placebo: n = 5926), and in COPD (8 trials; budesonide: n = 4616; non-ICS: n = 3643). A secondary dataset evaluated all double-blind, controlled trials in asthma involving the use of inhaled corticosteroids (60 trials; budesonide: n = 33,496; fluticasone: n = 2773).ResultsIn the primary asthma dataset, the occurrence of diabetes mellitus/hyperglycaemia adverse events (AEs) was 0.13% for budesonide and 0.13% for placebo (HR 0.98 [95% CI: 0.38–2.50], p = 0.96) and serious adverse events (SAEs) was 0% for budesonide and 0.05% for placebo. In the secondary dataset, the occurrence of diabetes/hyperglycaemia as AE and SAE was 0.19% and 0.03%, respectively. In the COPD dataset, the occurrence of diabetes mellitus/hyperglycaemia AEs was 1.3% for budesonide and 1.2% for non-ICS (HR 0.99 [95% CI: 0.67–1.46], p = 0.96) and SAEs was 0.1% for budesonide and 0.03% for non-ICS.Conclusion and clinical relevanceTreatment with inhaled corticosteroids in patients with asthma or COPD was not associated with increased risk of new onset diabetes mellitus or hyperglycaemia