Neuronal mechanisms for illusory brightness perception in humans

Biological visual systems are extraordinarily capable of recovering the shape and brightness of objects from sparse and fragmentary information. Using functional magnetic imaging, we show that two associative areas of the dorsal pathway--in the caudal region of the intrapariatal sulcus and in the lateral occipital sulcus--respond specifically to the Craik-O'Brien-Cornsweet illusion generated by high-pass filtered edges. Other visual areas, including primary visual cortex, also respond strongly to the retinotopic location of the edge, but these areas respond equally well to a line of matched contrast and detectability, rather than specifically to the brightness illusion. The reconstruction of surface and/or its brightness seems to be achieved by associative areas from the information about visual features provided by the primary visual cortices, even where there is no physical difference in luminance.     

An algorithmic model of heading perception

On the basis of Hanada and Ejimas (2000) model, an algorithmic model was presented to explain psychophysical data of van den Berg and Beintema (2000) that are inconsistent with vector-subtractive compensation for the rotational flow. The earlier model was modified in order not to use vector-subtractive compensation for the rotational flow. The proposed model computes the center of flow first and then estimates self-rotation; finally, heading is recovered from the center of flow and the estimate of self-rotation. The model explains the data of van de Berg and Beintema (2000). A fusion model of rotation estimates from different sources (efferent signals, proprioceptive feedback, vestibular signals about eye and head rotation, and visual motion) was also presented.     

An object-oriented database for protein structure analysis

An object-oriented database system has been developed which is being used to store protein structure data. The database can be queried using the logic programming language Prolog or the query language Daplex. Queries retrieve information by navigating through a network of objects which represent the primary, secondary and tertiary structures of proteins. Routines written in both Prolog and Daplex can integrate complex calculations with the retrieval of data from the database, and can also be stored in the database for sharing among users. Thus object-oriented databases are better suited to prototyping applications and answering complex queries about protein structure than relational databases. This system has been used to find loops of varying length and anchor positions when modelling homologous protein structures.     

An object-oriented modelling framework for the arterial wall

An object-oriented modelling framework for the arterial wall is presented. The novelty of the framework is the possibility to generate customisable artery models, taking advantage of imaging technology. In our knowledge, this is the first object-oriented modelling framework for the arterial wall. Existing models do not allow close structural mapping with arterial microstructure as in the object-oriented framework. In the implemented model, passive behaviour of the arterial wall was considered and the tunica adventitia was the objective system. As verification, a model of an arterial segment was generated. In order to simulate its deformation, a matrix structural mechanics simulator was implemented. Two simulations were conducted, one for an axial loading test and other for a pressure–volume test. Each simulation began with a sensitivity analysis in order to determinate the best parameter combination and to compare the results with analogue controls. In both cases, the simulated results closely reproduced qualitatively and quantitatively the analogue control plots.     

A model of binocular brightness and binaural loudness perception in humans with general applications to nonlinear summation of sensory inputs

A single neural model is proposed to account for how responses of the two eyes and two ears combine to form the perception of binocular brightness and binaural loudness respectively. It involves nonlinear reciprocal feedback inhibition between left and right channels, followed by linear summation between the channels. Local circuit synaptic interactions are an important source of nonlinearity. The model combines inputs in a manner that approximates vector magnitude models in general. This suggests that the model can be applied to a variety of circumstances beyond the visual and auditory data discussed here.     

A generic, object-oriented data model for life cycle analysis applications

Life Cycle Analysis (LCA) requires the manipulation of a large amount of both quantitative and qualitative data. It is also a continuous and iterative process in which the knowledge of previous steps is always necessary for the realization and understanding of subsequent stages. Current LCA data models are mostly organized as lists from which quantitative data can be extracted and manipulated, subsequently leading to lists of numerical results. Performing an LCA can benefit from more flexible structures that allow for complete or partial treatments and provides links between the successive transformations of various types of information. A generic, object-oriented model is presented, written in ISO/STEP EXPRESS, using the STEP principles.     

Computational model for perception of objects and motions

Perception of objects and motions in the visual scene is one of the basic problems in the visual system. There exist ‘What’ and ‘Where’ pathways in the superior visual cortex, starting from the simple cells in the primary visual cortex. The former is able to perceive objects such as forms, color, and texture, and the latter perceives ‘where’, for example, velocity and direction of spatial movement of objects. This paper explores brain-like computational architectures of visual information processing. We propose a visual perceptual model and computational mechanism for training the perceptual model. The computational model is a three-layer network. The first layer is the input layer which is used to receive the stimuli from natural environments. The second layer is designed for representing the internal neural information. The connections between the first layer and the second layer, called the receptive fields of neurons, are self-adaptively learned based on principle of sparse neural representation. To this end, we introduce Kullback-Leibler divergence as the measure of independence between neural responses and derive the learning algorithm based on minimizing the cost function. The proposed algorithm is applied to train the basis functions, namely receptive fields, which are localized, oriented, and bandpassed. The resultant receptive fields of neurons in the second layer have the characteristics resembling that of simple cells in the primary visual cortex. Based on these basis functions, we further construct the third layer for perception of what and where in the superior visual cortex. The proposed model is able to perceive objects and their motions with a high accuracy and strong robustness against additive noise. Computer simulation results in the final section show the feasibility of the proposed perceptual model and high efficiency of the learning algorithm.     

Computational model for perception of objects and motions

Perception of objects and motions in the visual scene is one of the basic problems in the visual system. There exist 'What' and 'Where' pathways in the superior visual cortex, starting from the simple cells in the primary visual cortex. The former is able to perceive objects such as forms, color, and texture, and the latter perceives 'where', for example, velocity and direction of spatial movement of objects. This paper explores brain-like computational architectures of visual information processing. We propose a visual perceptual model and computational mechanism for training the perceptual model. The compu- tational model is a three-layer network. The first layer is the input layer which is used to receive the stimuli from natural environments. The second layer is designed for representing the internal neural information. The connections between the first layer and the second layer, called the receptive fields of neurons, are self-adaptively learned based on principle of sparse neural representation. To this end, we introduce Kullback-Leibler divergence as the measure of independence between neural responses and derive the learning algorithm based on minimizing the cost function. The proposed algorithm is applied to train the basis functions, namely receptive fields, which are localized, oriented, and bandpassed. The resultant receptive fields of neurons in the second layer have the characteristics resembling that of simple cells in the primary visual cortex. Based on these basis functions, we further construct the third layer for perception of what and where in the superior visual cortex. The proposed model is able to perceive objects and their motions with a high accuracy and strong robustness against additive noise. Computer simulation results in the final section show the feasibility of the proposed perceptual model and high efficiency of the learning algorithm.     

Mathematical model of visual perception

A mathematical model of visual perception is presented with the intention of throwing some light on the problem of perceptual invariance. Two types of differential manifolds (receptive and effector) are associated with the repertoire which is the fundamental concept in the model. The elements of the repertoire carry weights which control the input-output relation in the repertoire and which can be modified by a learning process. It is shown that, under reasonable conditions, these repertoires possess good stability properties and can adjust to the various environments to which they may be subjected. In particular cases, it is shown that the stochastic learning process can be considered as deterministic to a first approximation.     

A model of visual perception

In this paper we propose a model of visual perception in which a positive feedback mechanism can reproduce the pattern stimulus on a neurons screen. The pattern stimulus reproduction is based on informations coming from the spatial derivatives of visual pattern. This information together with the response of the feature extractors provides to the reproduction of the visual pattern as neuron screen electric activity. We simulate several input patterns and prove that the model reproduces the percept.     

A model for direction selectivity in threshold motion perception

Thresholds were measured for a moving line superimposed on moving sinusoidal gratings. When line and grating moved in the same direction significant subthreshold summation was observed over a range of spatial frequencies. For motion of the line and grating in opposite directions, summation was never observed. This supports the hypothesis that direction selective mechanisms are responsible for motion perception at threshold. Further analysis of the data produced estimates of the spatial frequency tuning of these mechanisms. A quantitative model is proposed to interpret the data, and it is suggested that flickering gratings are not decomposed into their moving components by the visual system.     

Electromechanical model for object roughness perception during finger sliding

Touch allows us to gather abundant information in the world around us. However, how sensory cells embedded in the fingers convey texture information into their firing patterns is still poorly understood. Here, we develop an electromechanical model for roughness perception by incorporating main ingredients such as voltage-gated ion channels, active ion pumps, mechanosensitive channels, and cell deformation. The model reveals that sensory cells can convey texture wavelengths into the period of their firing patterns as the finger slides across object surfaces, but they can only convey a limited range of texture wavelengths. We also show that an increase in sliding speed broadens the decoding wavelength range at the cost of reduction of lower perception limits. Thus, a smaller sliding speed and a bigger contact force may be needed to successfully discern a smooth surface, consistent with previous psychophysical observations. Moreover, we show that cells with slowly adapting mechanosensitive channels can still fire action potentials under static loadings, indicating that slowly adapting mechanosensitive channels may contribute to the perception of coarse textures under static touch. Our work thus provides a new theoretical framework to study roughness perception and may have important implications for the design of electronic skin, artificial touch, and haptic interfaces.     

An object-oriented, individual-based approach for simulating the dynamics of genes in subdivided populations

An object-oriented, individual-based simulation framework was developed for modeling the diffusion of genetic material in subdivided populations. Objects representing individual organisms were defined, each with a unique genotype composed of gene objects. The organisms mate and reproduce, and progeny disperse or recruit back to their native population through the use of a Movement interface. The object-oriented approach is also linked to analytical theory through the development of matrix-based equations. An implementation of the model demonstrates how changes to basic population parameters affect spatial and temporal genetic structure. Scalar changes to the system affect the duration over which processes occur as well as the degree of variance, but appear to leave overall structural patterns unchanged. Object-oriented programming provides some unique advantages for modeling population genetic processes, including the use of abstraction and implementation, as well as the ability to accommodate complex, heterogeneous behavior.     

A multilayer neural network model for perception of rotational motion

A multilayer neural nerwork model for the perception of rotational motion has been developed usingReichardt's motion detector array of correlation type, Kohonen's self-organized feature map and Schuster-Wagner's oscillating neural network. It is shown that the unsupervised learning could make the neurons on the second layer of the network tend to be self-organized in a form resembling columnar organization of selective directions in area MT of the primate's visual cortex. The output layer can interpret rotation information and give the directions and velocities of rotational motion. The computer simulation results are in agreement with some psychophysical observations of rotation-al perception. It is demonstrated that the temporal correlation between the oscillating neurons would be powerful for solving the "binding problem" of shear components of rotational motion.     

A model of constant color perception for continuous spectral functions

A trichromatic model is proposed of constant pattern of colour perception (MCCP) for scenes with a single illumination source. In MCCP functions of the source emission, surface reflection, photoacceptor sensitivity are approximated by the normal distribution curves. MCCP characteristics "colour tone", "cleanness", and "lightness" organize its output colour metric by the human's pattern, and in a wide range of spectral composition of illumination they are invariant under these changes. Special colorimetric regime of MCCP work which estimates the colour of visual stimulus permits a comparison between the efficiencies of two proposed algorithms of visual information processing for MCCP.