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Three theories currently compete to explain the conceptual deficits that result from brain damage: sensory-functional theory, domain-specific theory, and conceptual structure theory. We argue that all three theories capture important aspects of conceptual deficits, and offer different insights into their origins. Conceptual topography theory (CTT) integrates these insights, beginning with A. R. Damasio’s (1989) convergence zone theory and elaborating it with the similarity-in-topography (SIT) principle. According to CTT, feature maps in sensory-motor systems represent the features of a category’s exemplars. A hierarchical system of convergence zones then conjoins these features to form both property and category representations. According to the SIT principle, the proximity of two conjunctive neurons in a convergence zone increases with the similarity of the features they conjoin. As a result, conjunctive neurons become topographically organised into local regions that represent properties and categories. Depending on the level and location of a lesion in this system, a wide variety of deficits is possible. Consistent with the literature, these deficits range from the loss of a single category to the loss of multiple categories that share sensory-motor properties.

Keywords: speech production; model; fMRI; Broca’s area; premotor cortex; motor cortex; speech acquisition; sensorimotor learning; neural transmission delays This paper describes a neural model of speech acquisition and production that accounts for a wide range of acoustic, kinematic, and neuroimaging data concerning the control of speech movements. The model is a neural network whose components correspond to regions of the cerebral cortex and cerebellum, including premotor, motor, auditory, and somatosensory cortical areas. Computer simulations of the model verify its ability to account for compensation to lip and jaw perturbations during speech. Specific anatomical locations of the model’s components are estimated, and these estimates are used to simulate fMRI experiments of simple syllable production. 1 1

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Advanced display technologies have made the virtual exploration of relatively complex models feasible in many applications. Unfortunately, only a few human interfaces allow natural interaction with the environment. Moreover, in surgical applications, such realistic interaction requires real-time rendering of volumetric data - placing an overwhelming performance burden on the system. We report on our advances towards developing a virtual reality system that provides intuitive interaction with complex volume data by employing real-time realistic volume rendering and convincing force feedback (haptic) sensations. We describe our methods for real-time volume rendering, model deformation, interaction, and the haptic devices, and demonstrate the utilization of this system in the real-world application of Endoscopic Sinus Surgery (ESS) simulation. 1. Introduction Human investigation of spatial information requires the subtle integration of several sensory modalities (visual, auditory, propri...

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Advanced display technologies have made the virtual exploration of relatively complex models feasible in many applications. Unfortunately, only a few human interfaces allow natural interaction with the environment. Moreover, in surgical applications, such realistic interaction requires real-time rendering of volumetric data - placing an overwhelming performance burden on the system. We report on a collaboration of an interdisciplinary group developing a virtual reality system that provides intuitive interaction with volume data by employing real-time volume rendering and force feedback (haptic) sensations. We describe our rendering methods and the haptic devices and explain its utility of this system in the real-world application of Endoscopic Sinus Surgery (ESS) simulation. 1. Introduction Human investigation of spatial information requires the subtle integration of several sensory modalities (visual, auditory, proprioception, which is internal information such as joint angles). Visi...

In this paper, we discuss the usefulness of topology preservation in an on-line learning neural control system. We discuss biological and information processing arguments. Then, we propose an experiment performed on a mobile robot that shows that with a Probabilistic Topological Map (PTM) much less information needs to be learned than using a Winner Take All. Keywords Neural Networks, Topology Preservation, Internal Representation, On-Line Learning 1. Introduction Self-organized feature maps [KOHONEN82] have been widely publicized both for their modelization of feature extractors commonly found in mammals nervous systems and for their ability to allow a relevant dimension reduction of the input space that preserves certain topology relations. Most research on applications of Kohonen maps has focused on establishing categories at the level of the map itself, [KOHONEN88],[RITTER89], defining them as connex zones in the array. This vision thus requires a supervisor at some stage of...

The planning of visually guided reaches is accomplished by independent specification of extent and direction. We investigated whether this separation of extent and direction planning for well practiced movements could be explained by differences in the adaptation to extent and directional errors during motor learning. We compared the time course and generalization of adaptation with two types of screen cursor transformation that altered the relationship between hand space and screen space. The first was a gain change that induced extent errors and required subjects to learn a new scaling factor. The second was a screen cursor rotation that induced directional errors and required subjects to learn new reference axes. Subjects learned a new scaling factor at the same rate when training with one or multiple target distances, whereas learning new reference axes took longer and was less complete when training with multiple

. Learning of temporal sequences is a topic of research in such different areas as speech and other temporal pattern recognition as well as motor control (for a survey see e.g. Mozer, 1993). In this paper an approach is presented which is particularly suitable for motor control due to the fact that it does not only reproduce temporal sequences in exactly the way they where learned but it is able to generate slightly modified sequences according to given parameters, too. Key words. Neural Networks, Temporal Sequences, Motor Control, Motor Programs. 1 Introduction The human motor system is based on the interaction of muscles which are controlled by specific parts of the nervous system. When examining the nervous system, one can recognize a hierarchical structure, physiologically as well as functionally. In the lowest level the spinal cord provides simple motion sequences commonly summarized as reflex actions. They are triggered either by sensory neurons or by signals from the higher le...