A 3D graphical representation of RNA secondary structures

In this paper, we proposed a 3-D graphical representation of RNA secondary structures. Based on this representation, we outline an approach by constructing a 3-component vector whose components are the normalized leading eigenvalues of the L/L matrices associated with RNA secondary structure. The examination of similarities/dissimilarities among the secondary structure at the 3'-terminus of different viruses illustrates the utility of the approach.     

Recognition of 3D flexible objects by GRBF

Recently Poggio and Edelman have shown that for each object there exists a smooth mapping from an arbitrary view to its standard view and that the mapping can be learned from a sparse data set. In this paper, we extend their scheme further to deal with 3D flexible objects. We show the mappings from an arbitrary view to the standard view, and its rotated view can be synthesized even for a flexible object by learning from examples. To classify 3D flexible objects, we propose two methods, which do not require any special knowledge on the target flexible objects. They are: (1) learning the characteristic function of the object and (2) learning the view-change transformation. We show their performance by computer simulations. Received: 1 March 1993/Accepted in revised form: 7 June 1993     

C-curve: A novel 3D graphical representation of DNA sequence based on codons

In this paper, a novel 3D graphical representation of DNA sequence based on codons is proposed. Since there is not loss of information due to overlapping and containing loops, this representation will be useful for comparison of different DNA sequences. This 3D curve will be convenient for DNA mutations comparison specially. In continues we give a numerical characterization of DNA sequences based on the new 3D curve. This characterization facilitates quantitative comparisons of similarities/dissimilarities analysis of DNA sequences based on codons.     

SCA: symmetry-based center assignment of 2D projections of symmetric 3D objects

A method for finding the center of cryo-EM images which correspond to the projections of a symmetric 3D structure, based on mathematical properties of symmetry adapted functions and the Fourier-Bessel transform, is presented. It is a model independent one-step procedure with no parameters to be chosen by the user. The proposed method is tested in one synthetic tetrahedral case with different noise levels and in two real cases with D7 and icosahedral symmetries.     

Spatiotemporal representation of 3D hand trajectory based on beta-elliptic models

The aim of this paper was to model the hand trajectory during grasping by an extension in 3D of the 2D written language beta-elliptic model. The interest of this model is that it takes into account both geometric and velocity information. The method relies on the decomposition of the task space trajectories in elementary bricks. The latter is characterized by a velocity profile modelled with beta functions and a geometry modelled with elliptic shapes. A data base of grasping movements has been constructed and the errors of reconstruction were assessed (distance and curvature) considering two variations of the beta-elliptic model (‘quarter ellipse’ and ‘two tangents points’ method). The results showed that the method based on two tangent points outperforms the quarter ellipse method with average and maximum relative errors of 2.73% and 8.62%, respectively, and a maximum curvature error of 9.26% for the former. This modelling approach can find interesting application to characterize the improvement due to a rehabilitation or teaching process by a quantitative measurement of hand trajectory parameters.     

Neural representation of visual objects: encoding and top-down activation

Knowledge or experiences are voluntarily recalled from memory by reactivation of their neural representations in the association cortex. Mnemonic representations of visual objects, located in the ventral processing stream of visual perception, provide the best indication of how neuronal codes are created, organized and reactivated. Associative codes are created by neurons that have the ability to link the representations of temporally associated stimuli. Recent experiments suggest that not only bottom-up signals from the retina but also top-down signals from the prefrontal cortex can trigger the retrieval of associative codes, which may serve as a neural basis for conscious recall.     

Class similarity and viewpoint invariance in the recognition of 3D objects

 In human vision, the processes and the representations involved in identifying specific individuals are frequently assumed to be different from those used for basic level classification, because classification is largely viewpoint-invariant, but identification is not. This assumption was tested in psychophysical experiments, in which objective similarity between stimuli (and, consequently, the level of their distinction) varied in a controlled fashion. Subjects were trained to discriminate between two classes of computer-generated three-dimensional objects, one resembling monkeys and the other, dogs. Both classes were defined by the same set of 56 parameters, which encoded sizes, shapes, and placement of the limbs, ears, snout, etc. Interpolation between parameter vectors of the class prototypes yielded shapes that changed smoothly between monkey and dog. Within-class variation was induced in each trial by randomly perturbing all the parameters. After the subjects reached 90% correct performance on a fixed canonical view of each object, discrimination performance was tested for novel views that differed by up to 60° from the training view. In experiment 1 (in which the distribution of parameters in each class was unimodal) and in experiment 2 (bimodal classes), the stimuli differed only parametrically and consisted of the same geons (parts), yet were recognized virtually independently of viewpoint in the low-similarity condition. In experiment 3, the prototypes differed in their arrangement of geons, yet the subjects’ performance depended significantly on viewpoint in the high-similarity condition. In all three experiments, higher interstimulus similarity was associated with an increase in the mean error rate and, for misorientation of up to 45°, with an increase in the degree of viewpoint dependence. These results suggest that a geon-level difference between stimuli is neither strictly necessary nor always sufficient for viewpoint-invariant performance. Thus, basic and subordinate-level processes in visual recognition may be more closely related than previously thought. Received: 15 November 1993/Accepted in revised form: 14 July 1994     

Segmentation of 3D tubular objects with adaptive front propagation and minimal tree extraction for 3D medical imaging

We present a new fast approach for segmentation of thin branching structures, like vascular trees, based on Fast-Marching (FM) and Level Set (LS) methods. FM allows segmentation of tubular structures by inflating a "long balloon" from a user given single point. However, when the tubular shape is rather long, the front propagation may blow up through the boundary of the desired shape close to the starting point. Our contribution is focused on a method to propagate only the useful part of the front while freezing the rest of it. We demonstrate its ability to segment quickly and accurately tubular and tree-like structures. We also develop a useful stopping criterion for the causal front propagation. We finally derive an efficient algorithm for extracting an underlying 1D skeleton of the branching objects, with minimal path techniques. Each branch being represented by its centerline, we automatically detect the bifurcations, leading to the "Minimal Tree" representation. This so-called "Minimal Tree" is very useful for visualization and quantification of the pathologies in our anatomical data sets. We illustrate our algorithms by applying it to several arteries datasets.     

New 3D graphical representation of DNA sequence based on dual nucleotides

We introduce a 3D graphical representation of DNA sequences based on the pairs of dual nucleotides (DNs). Based on this representation, we consider some mathematical invariants and construct two 16-component vectors associated with these invariants. The vectors are used to characterize and compare the complete coding sequence part of beta globin gene of nine different species. The examination of similarities/dissimilarities illustrates the utility of the approach.     

Neural representation of objects in space: a dual coding account.

I present evidence on the nature of object coding in the brain and discuss the implications of this coding for models of visual selective attention. Neuropsychological studies of task-based constraints on: (i) visual neglect; and (ii) reading and counting, reveal the existence of parallel forms of spatial representation for objects: within-object representations, where elements are coded as parts of objects, and between-object representations, where elements are coded as independent objects. Aside from these spatial codes for objects, however, the coding of visual space is limited. We are extremely poor at remembering small spatial displacements across eye movements, indicating (at best) impoverished coding of spatial position per se. Also, effects of element separation on spatial extinction can be eliminated by filling the space with an occluding object, indicating that spatial effects on visual selection are moderated by object coding. Overall, there are separate limits on visual processing reflecting: (i) the competition to code parts within objects; (ii) the small number of independent objects that can be coded in parallel; and (iii) task-based selection of whether within- or between-object codes determine behaviour. Between-object coding may be linked to the dorsal visual system while parallel coding of parts within objects takes place in the ventral system, although there may additionally be some dorsal involvement either when attention must be shifted within objects or when explicit spatial coding of parts is necessary for object identification.     

Effects of visual experience on the representation of objects in the prefrontal cortex

The perception and recognition of objects are improved by experience. Here, we show that monkeys' ability to recognize degraded objects was improved by several days of practice with these objects. This improvement was reflected in the activity of neurons in the prefrontal (PF) cortex, a brain region critical for a wide range of visual behaviors. Familiar objects activated fewer neurons than did novel objects, but these neurons were more narrowly tuned, and the object representation was more resistant to the effects of degradation, after experience. These results demonstrate a neural correlate of visual learning in the PF cortex of adult monkeys.     

A high-order graph generating self-organizing structure

A large class of neural network models have their units organized in a lattice with fixed topology or generate their topology during the learning process. These network models can be used as neighborhood preserving map of the input manifold, but such a structure is difficult to manage since these maps are graphs with a number of nodes that is just one or two orders of magnitude less than the number of input points (i.e., the complexity of the map is comparable with the complexity of the manifold) and some hierarchical algorithms were proposed in order to obtain a high-level abstraction of these structures. In this paper a general structure capable to extract high order information from the graph generated by a large class of self-organizing networks is presented. This algorithm will allow to build a two layers hierarchical structure starting from the results obtained by using the suitable neural network for the distribution of the input data. Moreover the proposed algorithm is also capable to build a topology preserving map if it is trained using a graph that is also a topology preserving map.     

Neonatal representation of odour objects: distinct memories of the whole and its parts

Extraction of relevant information from highly complex environments is a prerequisite to survival. Within odour mixtures, such information is contained in the odours of specific elements or in the mixture configuration perceived as a whole unique odour. For instance, an AB mixture of the element A (ethyl isobutyrate) and the element B (ethyl maltol) generates a configural AB percept in humans and apparently in another species, the rabbit. Here, we examined whether the memory of such a configuration is distinct from the memory of the individual odorants. Taking advantage of the newborn rabbit''s ability to learn odour mixtures, we combined behavioural and pharmacological tools to specifically eliminate elemental memory of A and B after conditioning to the AB mixture and evaluate consequences on configural memory of AB. The amnesic treatment suppressed responsiveness to A and B but not to AB. Two other experiments confirmed the specific perception and particular memory of the AB mixture. These data demonstrate the existence of configurations in certain odour mixtures and their representation as unique objects: after learning, animals form a configural memory of these mixtures, which coexists with, but is relatively dissociated from, memory of their elements. This capability emerges very early in life.     

A self-organizing model of walking patterns of insects

Insects generate walking patterns which depend upon external conditions. For example, when an insect is exposed to an additional load parallel to the direction in which it is walking, the walking pattern changes according to the magnitude of the load. Furthermore, even after some of its legs have been amputated, an insect will produce walking patterns with its remaining legs. These adaptations in insect walking could not previously be explained by a mathematical model, since the mathemati cal models were based upon the hypothesis that the relationship between walking velocity and walking patterns is fixed under all conditions. We have produced a mathematical model which describes self-organizing insect walking patterns in real-time by using feedback information regarding muscle load (Kimura et al. 1993). As part of this model, we introduced a new rule to coordinate leg movement, in which the information is circulated to optimize the efficiency of the energy transduction of each effector orga n. We describe this mechanism as ‘the least dissatisfaction for the greatest number of elements’. In this paper, we introduce the following aspects of this model, which reflect adaptability to changing circumstances: (1) after one leg is exposed to a transient perturbation, the walking pattern recovers swiftly; (2) when the external load parallel to the walking direction is continuously increased or decreased, the pattern transition point is shifted according to the magnitude of the load increme nt or decrement. This model generates a walking pattern which optimizes energy consumption at a given walking velocity even under these conditions; and (3) when some of the legs are amputated, the model generates walking patterns which are consistent with experimental results. We also discuss the ability of a hierarchical self-organizing model to describe a swift and flexible information processing system. Received: 8 February 1993/Accepted in revised form: 12 November 1993     

A self-organizing model of walking patterns of insects

It is well known that the motor systems of animals are controlled by a hierarchy consisting of a brain, central pattern generator, and effector organs. An animal's walking patterns change depending on its walking velocities, even when it has been decerebrated, which indicates that the walking patterns may, in fact, be generated in the subregions of the neural systems of the central pattern generator and the effector organs. In order to explain the self-organization of the walking pattern in response to changing circumstances, our model incorporates the following ideas: (1) the brain sends only a few commands to the central pattern generator (CPG) which act as constraints to self-organize the walking patterns in the CPG; (2) the neural network of the CPG is composed of oscillating elements such as the KYS oscillator, which has been shown to simulate effectively the diversity of the neural activities; and (3) we have introduced a rule to coordinate leg movement, in which the excitatory and inhibitory interactions among the neurons act to optimize the efficiency of the energy transduction of the effector organs. We describe this mechanism as the least dissatisfaction for the greatest number of elements, which is a self-organization rule in the generation of walking patterns. By this rule, each leg tends to share the load as efficiently as possible under any circumstances. Using this self-organizing model, we discuss the control mechanism of walking patterns.     

Cognitron: A self-organizing multilayered neural network

A new hypothesis for the organization of synapses between neurons is proposed: “The synapse from neuron x to neuron y is reinforced when x fires provided that no neuron in the vicinity of y is firing stronger than y”. By introducing this hypothesis, a new algorithm with which a multilayered neural network is effectively organized can be deduced. A self-organizing multilayered neural network, which is named “cognitron”, is constructed following this algorithm, and is simulated on a digital computer. Unlike the organization of a usual brain models such as a three-layered perceptron, the self-organization of a cognitron progresses favorably without having a “teacher” which instructs in all particulars how the individual cells respond. After repetitive presentations of several stimulus patterns, the cognitron is self-organized in such a way that the receptive fields of the cells become relatively larger in a deeper layer. Each cell in the final layer integrates the information from whole parts of the first layer and selectively responds to a specific stimulus pattern or a feature.