A computational framework for topographies of cortical areas

Self-organizing feature maps (SOFMs) represent a dimensionality-reduction algorithm that has been used to replicate feature topographies observed experimentally in primary visual cortex (V1). We used the SOFM algorithm to model possible topographies of generic sensory cortical areas containing up to five arbitrary physiological features. This study explored the conditions under which these multi-feature SOFMs contained two features that were mapped monotonically and aligned orthogonally with one another (i.e., “globally orthogonal”), as well as the conditions under which the map of one feature aligned with the longest anatomical dimension of the modeled cortical area (i.e., “dominant”). In a single SOFM with more than two features, we never observed more than one dominant feature, nor did we observe two globally orthogonal features in the same map in which a dominant feature occurred. Whether dominance or global orthogonality occurred depended upon how heavily weighted the features were relative to one another. The most heavily weighted features are likely to correspond to those physical stimulus properties transduced directly by the sensory epithelium of a particular sensory modality. Our results imply, therefore, that in the primary cortical area of sensory modalities with a two-dimensional sensory epithelium, these two features are likely to be organized globally orthogonally to one another, and neither feature is likely to be dominant. In the primary cortical area of sensory modalities with a one-dimensional sensory epithelium, however, this feature is likely to be dominant, and no two features are likely to be organized globally orthogonally to one another. Because the auditory system transduces a single stimulus feature (i.e., frequency) along the entire length of the cochlea, these findings may have particular relevance for topographic maps of primary auditory cortex. This research was supported by The McDonnell Center for Higher Brain Function, The Wallace H. Coulter Foundation and NIH grant DC008880.     

A computational model of monkey cortical grating cells

Grating cells were discovered in the V1 and V2 areas of the monkey visual cortex by von der Heydt et al. (1992). These cells responded vigorously to grating patterns of appropriate orientation and periodicity. Computational models inspired by these findings were used as texture operator (Kruzinga and Petkov 1995, 1999; Petkov and Kruzinga 1997) and for the emergence and self-organization of grating cells (Brunner et al. 1998; Bauer et al. 1999). The aim of this paper is to create a grating cell operator that demonstrates similar responses to monkey grating cells by applying operator to the same stimuli as in the experiments carried out by von der Heydt et al. (1992). Operator will be tested on images that contain periodic patterns as suggested by De Valois (1988). In order to learn more about the role of grating cells in natural vision, operator is applied to 338 real-world images of textures obtained from three different databases. The results suggest that grating cells respond strongly to regular alternating periodic patterns of a certain orientation. Such patterns are common in images of human-made structures, like buildings, fabrics, and tiles, and to regular natural periodic patterns, which are relatively rare in nature.     

A computational model of hemodynamic parameters in cortical capillary networks

The analysis of hemodynamic parameters and functional reactivity of cerebral capillaries is still controversial. To assess the hemodynamic parameters in the cortical capillary network, a generic model was created using 2D voronoi tessellation in which each edge represents a capillary segment. This method is capable of creating an appropriate generic model of cerebral capillary network relating to each part of the brain cortex because the geometric model is able to vary the capillary density. The modeling presented here is based on morphometric parameters extracted from physiological data of the human cortex. The pertinent hemodynamic parameters were obtained by numerical simulation based on effective blood viscosity as a function of hematocrit and microvessel diameter, phase separation and plasma skimming effects. The hemodynamic parameters of capillary networks with two different densities (consistent with the variation of the morphometric data in the human cortical capillary network) were analyzed. The results show pertinent hemodynamic parameters for each model. The heterogeneity (coefficient variation) and the mean value of hematocrits, flow rates and velocities of the both network models were specified. The distributions of blood flow throughout the both models seem to confirm the hypothesis in which all capillaries in a cortical network are recruited at rest (normal condition). The results also demonstrate a discrepancy of the network resistance between two models, which are derived from the difference in the number density of capillary segments between the models.     

A computational model of rapid task-related plasticity of auditory cortical receptive fields

Receptive field properties of neurons in A1 can rapidly adapt their shapes during task performance in accord with specific task demands and salient sensory cues (Fritz et al., Hearing Research, 206:159–176, 2005a, Nature Neuroscience, 6: 1216–1223, 2003). Such modulatory changes selectively enhance overall cortical responsiveness to target (foreground) sounds and thus increase the likelihood of detection against the background of reference sounds. In this study, we develop a mathematical model to describe how enhancing discrimination between two arbitrary classes of sounds can lead to the observed receptive field changes in a variety of spectral and temporal discrimination tasks. Cortical receptive fields are modeled as filters that change their spectro-temporal tuning properties so as to respond best to the discriminatory acoustic features between foreground and background stimuli. We also illustrate how biologically plausible constraints on the spectro-temporal tuning of the receptive fields can be used to optimize the plasticity. Results of the model simulations are compared to published data from a variety of experimental paradigms.     

A computational framework for cortical learning

Recent physiological findings have revealed that long-term adaptation of the synaptic strengths between cortical pyramidal neurons depends on the temporal order of presynaptic and postsynaptic spikes, which is called spike-timing-dependent plasticity (STDP) or temporally asymmetric Hebbian (TAH) learning. Here I prove by analytical means that a physiologically plausible variant of STDP adapts synaptic strengths such that the presynaptic spikes predict the postsynaptic spikes with minimal error. This prediction error model of STDP implies a mechanism for cortical memory: cortical tissue learns temporal spike patterns if these spike patterns are repeatedly elicited in a set of pyramidal neurons. The trained network finishes these patterns if their beginnings are presented, thereby recalling the memory. Implementations of the proposed algorithms may be useful for applications in voice recognition and computer vision.     

A semianalytical model to study the effect of cortical tension on cell rolling

Cell rolling on the vascular endothelium plays an important role in trafficking of leukocytes, stem cells, and cancer cells. We describe a semianalytical model of cell rolling that focuses on the microvillus as the unit of cell-substrate interaction and integrates microvillus mechanics, receptor clustering, force-dependent receptor-ligand kinetics, and cortical tension that enables incorporation of cell body deformation. Using parameters obtained from independent experiments, the model showed excellent agreement with experimental studies of neutrophil rolling on P-selectin and predicted different regimes of cell rolling, including spreading of the cells on the substrate under high shear. The cortical tension affected the cell-surface contact area and influenced the rolling velocity, and modulated the dependence of rolling velocity on microvillus stiffness. Moreover, at the same shear stress, microvilli of cells with higher cortical tension carried a greater load compared to those with lower cortical tension. We also used the model to obtain a scaling dependence of the contact radius and cell rolling velocity under different conditions of shear stress, cortical tension, and ligand density. This model advances theoretical understanding of cell rolling by incorporating cortical tension and microvillus extension into a versatile, semianalytical framework.     

A misexpression screen to identify regulators of Drosophila larval hemocyte development

In Drosophila, defense against foreign pathogens is mediated by an effective innate immune system, the cellular arm of which is composed of circulating hemocytes that engulf bacteria and encapsulate larger foreign particles. Three hemocyte types occur: plasmatocytes, crystal cells, and lamellocytes. The most abundant larval hemocyte type is the plasmatocyte, which is responsible for phagocytosis and is present either in circulation or in adherent sessile domains under the larval cuticle. The mechanisms controlling differentiation of plasmatocytes and their migration toward these sessile compartments are unclear. To address these questions we have conducted a misexpression screen using the plasmatocyte-expressed GAL4 driver Peroxidasin-GAL4 (Pxn-GAL4) and existing enhancer-promoter (EP) and EP yellow (EY) transposon libraries to systematically misexpress approximately 20% of Drosophila genes in larval hemocytes. The Pxn-GAL4 strain also contains a UAS-GFP reporter enabling hemocyte phenotypes to be visualized in the semitransparent larvae. Among 3412 insertions screened we uncovered 101 candidate hemocyte regulators. Some of these are known to control hemocyte development, but the majority either have no characterized function or are proteins of known function not previously implicated in hemocyte development. We have further analyzed three candidate genes for changes in hemocyte morphology, cell-cell adhesion properties, phagocytosis activity, and melanotic tumor formation.     

A computational model of the human thyroid

The thyroid, the largest gland in the endocrine system, secretes hormones that help promote bodily growth and development. This gland regulates hormonal secretion rate in spite of changes in dietary iodine which is a key ingredient in the hormone's biosynthesis. The thyroid relies on several feedback mechanisms for this regulation, and in this paper we use recent molecular-level and clinical observations to engineer a computational thyroid model. We use simulation and analysis to show that this models captures known aspects of thyroid physiology. We identify features in the model that are responsible for hormonal regulation, and use the model to identify and evaluate competing hypotheses associated with Wolff-Chaikoff escape.     

A computational model to link psychophysics and cortical cell activation patterns in human texture processing

The human visual system uses texture information to automatically, or pre-attentively, segregate parts of the visual scene. We investigate the neural substrate underlying human texture processing using a computational model that consists of a hierarchy of bi-directionally linked model areas. The model builds upon two key hypotheses, namely that (i) texture segregation is based on boundary detection--rather than clustering of homogeneous items--and (ii) texture boundaries are detected mainly on the basis of a large scenic context that is analyzed by higher cortical areas within the ventral visual pathway, such as area V4. Here, we focus on the interpretation of key results from psychophysical studies on human texture segmentation. In psychophysical studies, texture patterns were varied along several feature dimensions to systematically characterize human performance. We use simulations to demonstrate that the activation patterns of our model directly correlate with the psychophysical results. This allows us to identify the putative neural mechanisms and cortical key areas which underlie human behavior. In particular, we investigate (i) the effects of varying texture density on target saliency, and the impact of (ii) element alignment and (iii) orientation noise on the detectability of a pop-out bar. As a result, we demonstrate that the dependency of target saliency on texture density is linked to a putative receptive field organization of orientation-selective neurons in V4. The effect of texture element alignment is related to grouping mechanisms in early visual areas. Finally, the modulation of cell activity by feedback activation from higher model areas, interacting with mechanisms of intra-areal center-surround competition, is shown to result in the specific suppression of noise-related cell activities and to improve the overall model capabilities in texture segmentation. In particular, feedback interaction is crucial to raise the model performance to the level of human observers.     

Inhibitory synapses between striatal projection neurons support efficient enhancement of cortical signals: A computational model

The function of lateral inhibitory synapses between striatal projection neurons is currently poorly understood. This paper puts forward a model suggesting that inhibitory collaterals can be used to enhance the incoming cortical signals. In particular, we propose that lateral inhibition between projection neurons performs a signal-enhancing process that resembles the image processing technique of “unsharp masking”, where a blurred copy is used to enhance and sharpen an input image. The paper also presents the results of computer simulations deomsntrating that the proposed mechanisms is compatible with known properties of striatal projection neurons, and outperforms alternative models of lateral inhibition. Finally, this paper illustrates the advantages of the proposed model and discusses the relevance of these conclusions for existing computational models of the basal ganglia and their role in cognition.     

A computational model of blast loading on the human eye

Ocular injuries from blast have increased in recent wars, but the injury mechanism associated with the primary blast wave is unknown. We employ a three-dimensional fluid–structure interaction computational model to understand the stresses and deformations incurred by the globe due to blast overpressure. Our numerical results demonstrate that the blast wave reflections off the facial features around the eye increase the pressure loading on and around the eye. The blast wave produces asymmetric loading on the eye, which causes globe distortion. The deformation response of the globe under blast loading was evaluated, and regions of high stresses and strains inside the globe were identified. Our numerical results show that the blast loading results in globe distortion and large deviatoric stresses in the sclera. These large deviatoric stresses may be indicator for the risk of interfacial failure between the tissues of the sclera and the orbit.     

A biological and computational model of megakaryocyte development as a stochastic branching process

The purpose of this paper is to describe a model of megakaryocytopoiesis as a branching process with stochastic processes regulating critical control points of differentiation along the stem cell megakaryocyte platelet axis. Progress of cells through these critical control points are regulated by transitional probabilities, which in turn are regulated by influences such as growth factors. The critical control points include transition of resting megakaryocytic stem cells (CFU-meg) into proliferating stem cells, the cessation of cytokinesis, and the cessation of DNA synthesis. A computerized computational method has been developed for directly fitting the stochastic branching model to colony growth data. The computational model has allowed transitional probabilities to be derived from colony size data. The model provides a unifying explanation for much of the heterogeneity of stages of maturation within populations of megakaryocytes and is fully compatible with historical data supporting the stochastic nature of hematopoietic stem cell regulation and with modern molecular concepts about control of the cell cycle.     

A computational model of visual anisotropy

Visual anisotropy has been demonstrated in multiple tasks where performance differs between vertical, horizontal, and oblique orientations of the stimuli. We explain some principles of visual anisotropy by anisotropic smoothing, which is based on a variation on Koenderink's approach in [1]. We tested the theory by presenting gaussian elongated luminance profiles and measuring the perceived orientations by means of an adjustment task. Our framework is based on the smoothing of the image with elliptical gaussian kernels and it correctly predicted an illusory orientation bias towards the vertical axis. We discuss the scope of the theory in the context of other anisotropies in perception.     

A computational model of a class of gene networks with positive and negative controls

This article proposes a computational framework for modelling the logical behavior of a class of gene networks. We characterize the basic behavior of genes in terms of a state-and-transition structure, and model the individual genes as language-generating automata. We consider positive and negative controls as the interaction mechanisms among the genes, and treat such controls as constraints (also expressed in automata) imposed on the behavior of the gene network. By computing the intersection of the languages generated by the gene models and the constraints, we obtain the complete set of pathways in a gene network. Implications and possible improvement of this work are discussed.     

A computational model for cell differentiation

In this paper, we present a word set generating mechanism, called cell-differentiation system, inspired by the tissue process formation in multicellular organisms, which might model some properties of evolving communities of living cells at the syntactical level. The tools utilized to model these biological phenomena belong to the formal language theory. In this context chromosomal mutations are defined as operations on strings and the differentiation according to the control of gene expression is represented by some random-context conditions in formal languages.In the presented formal framework we prove that in a simplified form of this formalism, with only one cell-type which is regular, one single cell and no mitosis involved, the problem of establishing whether or not the set of vectors of integers indicating the number of cells in each population, is finite, linear or semilinear, is recursively undecidable. However, one can algorithmically decide whether or not a cell-differentiation system of finite cell-type can produce a specific generation of cells.     

A phylogenetic model for understanding the effect of gene duplication on cancer progression

As biotechnology advances rapidly, a tremendous amount of cancer genetic data has become available, providing an unprecedented opportunity for understanding the genetic mechanisms of cancer. To understand the effects of duplications and deletions on cancer progression, two genomes (normal and tumor) were sequenced from each of five stomach cancer patients in different stages (I, II, III and IV). We developed a phylogenetic model for analyzing stomach cancer data. The model assumes that duplication and deletion occur in accordance with a continuous time Markov Chain along the branches of a phylogenetic tree attached with five extended branches leading to the tumor genomes. Moreover, coalescence times of the phylogenetic tree follow a coalescence process. The simulation study suggests that the maximum likelihood approach can accurately estimate parameters in the phylogenetic model. The phylogenetic model was applied to the stomach cancer data. We found that the expected number of changes (duplication and deletion) per gene for the tumor genomes is significantly higher than that for the normal genomes. The goodness-of-fit test suggests that the phylogenetic model with constant duplication and deletion rates can adequately fit the duplication data for the normal genomes. The analysis found nine duplicated genes that are significantly associated with stomach cancer.     

A computational model of transmembrane integrin clustering

The presented work describes a structural model for integrin homooligomerization, focusing on the transmembrane domains. The two noncovalently linked integrin subunits, alpha and beta, were previously shown to homodimerize or homotrimerize, respectively. Our work is based on published mutational work that induced homotrimerization of beta3 integrins. The mutations provided structural restraints for the creation of a structural model of the beta3 homotrimer by a computational search of the conformational space of homomeric interactions of the beta3 integrin. Additionally, we explored possible conformations of the alphaIIb integrin homodimer, for which no unique solution was found. Two possible models of signal transduction, involving two different alphaIIb conformations, are discussed. One of the possible homodimeric alphaIIb conformations is GpA like, which is in line with experimental evidence. Based on our here-presented structural models and on recent experiments, we will argue that most probably the heteromeric alpha/beta transmembrane complex separates in the course of clustering.     

A computational model of chemotaxis-based cell aggregation

We present a computational model that successfully captures the cell behaviors that play important roles in 2-D cell aggregation. A virtual cell in our model is designed as an independent, discrete unit with a set of parameters and actions. Each cell is defined by its location, size, rates of chemoattractant emission and response, age, life cycle stage, proliferation rate and number of attached cells. All cells are capable of emitting and sensing a chemoattractant chemical, moving, attaching to other cells, dividing, aging and dying. We validated and fine-tuned our in silico model by comparing simulated 24-h aggregation experiments with data derived from in vitro experiments using PC12 pheochromocytoma cells. Quantitative comparisons of the aggregate size distributions from the two experiments are produced using the Earth Mover's Distance (EMD) metric. We compared the two size distributions produced after 24 h of in vitro cell aggregation and the corresponding computer simulated process. Iteratively modifying the model's parameter values and measuring the difference between the in silico and in vitro results allow us to determine the optimal values that produce simulated aggregation outcomes closely matched to the PC12 experiments. Simulation results demonstrate the ability of the model to recreate large-scale aggregation behaviors seen in live cell experiments.