Underlying neural computations for some visual phenomena.

M N Oğuztöreli; G M Steil; T M Caelli

doi:10.1007/bf00202896

Home/Archive/PubMed

PubMed · 3228557

Underlying neural computations for some visual phenomena.

M N Oğuztöreli·G M Steil·T M Caelli

View original source ↗Export metadata ↓

Abstract

In this paper we examine how a large array of neurons, and their associated neural circuitry, may determine known receptive field profile types and some well-known visual phenomena including Mach bands, edge enhancement, and visual masking of one signal by another. The neural model has a spatio-temporal structure and is described by a nonlinear integropartial differential difference equation with an isotropic Gabor kernel - a Gaussian apertured cosine modulation. Several simulations are presented.

Explore related subjects

Computer Simulation Humans Models, Neurological Neurons Perceptual Masking Visual Perception Journal Article Research Support, Non-U.S. Gov't

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

M N Oğuztöreli, G M Steil, T M Caelli. 1988. Underlying neural computations for some visual phenomena.. https://doi.org/10.1007/bf00202896

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2025

Generating correlated data for omics simulation.

Simulation of realistic omics data is a key input for benchmarking studies that help users obtain optimal computational pipelines. Omics data involves large numbers of measured features on each sample and these measures are generally correlated with each other. However, simulation too often ignores these correlations, perhaps due to computational and statistical hurdles of doing so. To alleviate this, we describe three approaches for generating omics-scale data with correlated measures which mimic real datasets. These approaches are all based on a Gaussian copula approach with a covariance matrix that decomposes into a diagonal part and a low-rank part. This decomposition allows for extremely efficient simulation, overcoming a hurdle for adoption of past methods. We use these approaches to demonstrate the importance of including correlation in two benchmarking applications. First, we show that variance of results from the popular DESeq2 method increases when dependence is included. Second, we demonstrate that CYCLOPS, a method for inferring circadian time of collection from transcriptomics, improves in performance when given gene-gene dependencies in some circumstances. We provide an R package, dependentsimr, that has efficient implementations of these methods and can generate dependent data with arbitrary marginal distributions, including discrete (binary, ordered categorical, Poisson, negative binomial), continuous (normal), or with an empirical distribution.

Computer Simulation↗

PubMed2017

Addressing current challenges in cancer immunotherapy with mathematical and computational modelling.

The goal of cancer immunotherapy is to boost a patient's immune response to a tumour. Yet, the design of an effective immunotherapy is complicated by various factors, including a potentially immunosuppressive tumour microenvironment, immune-modulating effects of conventional treatments and therapy-related toxicities. These complexities can be incorporated into mathematical and computational models of cancer immunotherapy that can then be used to aid in rational therapy design. In this review, we survey modelling approaches under the umbrella of the major challenges facing immunotherapy development, which encompass tumour classification, optimal treatment scheduling and combination therapy design. Although overlapping, each challenge has presented unique opportunities for modellers to make contributions using analytical and numerical analysis of model outcomes, as well as optimization algorithms. We discuss several examples of models that have grown in complexity as more biological information has become available, showcasing how model development is a dynamic process interlinked with the rapid advances in tumour-immune biology. We conclude the review with recommendations for modellers both with respect to methodology and biological direction that might help keep modellers at the forefront of cancer immunotherapy development.

Computer Simulation↗

PubMed1992

Compositional mapping of the human dystrophin-encoding gene.

The genomes of warm-blooded vertebrates are mosaics of long DNA segments (> 300 kb, on the average), the isochores, homogeneous in GC levels, which belong to a small number of compositional families. In the present work, the human dystrophin-encoding gene, spanning more than 2.3 Mb in Giemsa band Xp21 (on the short arm of the X chromosome), was analyzed in its isochore organization by hybridizing cDNA probes, corresponding to eight contiguous segments of the coding sequence, on compositional fractions from human DNA. Five DNA regions of uniform (+/- 0.5%) GC content, separated by compositional discontinuities of about 2% GC, were found, so providing the first high-resolution compositional map obtained for a human genome locus and the first direct estimate of isochore size (360 kb to more than 770 kb, in the locus under consideration). One of the isochores contains 71% and another one 21% of deletion breakpoints found in patients suffering from Duchenne's and Becker's muscular dystrophies.

Computer Simulation↗