Computer generation and quantitative morphometric analysis of virtual neurons

An important goal in computational neuroanatomy is the complete and accurate simulation of neuronal morphology. We are developing computational tools to model three-dimensional dendritic structures based on sets of stochastic rules. This paper reports an extensive, quantitative anatomical characterization of simulated motoneurons and Purkinje cells. We used several local and global algorithms implemented in the L-Neuron and ArborVitae programs to generate sets of virtual neurons. Parameters statistics for all algorithms were measured from experimental data, thus providing a compact and consistent description of these morphological classes. We compared the emergent anatomical features of each group of virtual neurons with those of the experimental database in order to gain insights on the plausibility of the model assumptions, potential improvements to the algorithms, and non-trivial relations among morphological parameters. Algorithms mainly based on local constraints (e.g., branch diameter) were successful in reproducing many morphological properties of both motoneurons and Purkinje cells (e.g. total length, asymmetry, number of bifurcations). The addition of global constraints (e.g., trophic factors) improved the angle-dependent emergent characteristics (average Euclidean distance from the soma to the dendritic terminations, dendritic spread). Virtual neurons systematically displayed greater anatomical variability than real cells, suggesting the need for additional constraints in the models. For several emergent anatomical properties, a specific algorithm reproduced the experimental statistics better than the others did. However, relative performances were often reversed for different anatomical properties and/or morphological classes. Thus, combining the strengths of alternative generative models could lead to comprehensive algorithms for the complete and accurate simulation of dendritic morphology.

A CAD-based computer vision system

A vision system for recognizing rigid and articulated three-dimensional objects in two-dimensional images is described. Geometrical models are extracted from a commercial computer aided design package. The models are then augmented with appearance and functional information which improves the system's hypothesis generation, hypothesis verification, and pose refinement. Significant advantages over existing CAD-based vision systems, which utilize only information available in the CAD system, are realized. Examples show the system recognizing, locating, and tracking a variety of objects in a robot work-cell and in natural scenes.

Computer intensive methods in control and signal processing: the curse of dimensionality

This volume is based upon the 2nd IEEE European Workshop on Computer-Intensive Methods in Control and Signal Processing, held in Prague, August 1996.

Queue-based agent communication architecture for intelligent human-like computer interfaces

Human-like computer interaction systems requires far more than just simple speech input/output. Such a system should communicate with the user verbally, using a conversational style language. It should be aware of its surroundings and use this context for any decisions it makes. As a synthetic character, it should have a computer generated human-like appearance. This, in turn, should be used to convey emotions, expressions and gestures. Finally, and perhaps most important of all, the system should interact with the user in real time, in a fluent and believable manner.

Human-computer cooperative teleoperation with time delay

Presents a method for model based bilateral control of master-slave arm with time delay between master and slave arms, where the system supports cooperative action between manual and automatic modes. The method realises efficiencies in master-slave arm control with the simplicities of a computer and the flexibility of a skilled human operator.

Practical neural networks in a computer control package

This paper deals with the integration of radial basis function (RBF) networks into the industrial software control package Connoisseur. The paper shows the improved modelling capabilities offered by RBF networks within the Connoisseur environment compared to linear modelling techniques such as recursive least squares. The paper also goes on to mention the way this improved modelling capability, obtained through the RBF networks will be utilised within Connoisseur.

Computer-mediated communication in distance MA programmes: the student's perspective

This paper reports on a study of computer-mediated communication within the context of a distance MA in TEFL programme which used an e-mail discussion list and then a discussion board. The study focused on the computer/Internet access and skills of the target population and their CMC needs and wants. Data were collected from 63 questionnaires and 6 in-depth interviews with students. Findings indicate that computer use and access to the Internet are widespread within the target population. In addition, most respondents indicated some competence in Internet use. No single factor emerged as an overriding inhibiting factor for lack of personal use. There was limited use of the CMC tools provided on the course for student–student interaction, mainly attributable to time constraints. However, most respondents said that they would like more CMC interaction with tutors. The main factor which would contribute to greater Internet use was training. The paper concludes with recommendations and suggestions for learner training in this area.

Brain computer interface control via functional connectivity dynamics

The dynamics of inter-regional communication within the brain during cognitive processing – referred to as functional connectivity – are investigated as a control feature for a brain computer interface. EMDPL is used to map phase synchronization levels between all channel pair combinations in the EEG. This results in complex networks of channel connectivity at all time–frequency locations. The mean clustering coefficient is then used as a descriptive feature encapsulating information about inter-channel connectivity. Hidden Markov models are applied to characterize and classify dynamics of the resulting complex networks. Highly accurate levels of classification are achieved when this technique is applied to classify EEG recorded during real and imagined single finger taps. These results are compared to traditional features used in the classification of a finger tap BCI demonstrating that functional connectivity dynamics provide additional information and improved BCI control accuracies.

Investigating familiar interfaces to help older adults learn computer applications more easily

Many older adults wish to gain competence in using a computer, but many application interfaces are perceived as complex and difficult to use, deterring potential users from investing the time to learn them. Hence, this study looks at the potential of ‘familiar’ interface design which builds upon users’ knowledge of real world interactions, and applies existing skills to a new domain. Tools are provided in the form of familiar visual objects, and manipulated like real-world counterparts, rather than with buttons, icons and menus found in classic WIMP interfaces. This paper describes the formative evaluation of computer interactions that are based upon familiar real world tasks, which supports multitouch interaction, involves few buttons and icons, no menus, no right-clicks or double-clicks and no dialogs. Using an example of an email client to test the principles of using “familiarity”, the initial feedback was very encouraging, with 3 of the 4 participants being able to undertake some of the basic email tasks with no prior training and little or no help. The feedback has informed a number of refinements of the design principles, such as providing clearer affordance for visual objects. A full study is currently underway.