Motor tics are a cardinal feature of Tourette syndrome and are traditionally associated with an excess of striatal dopamine in the basal ganglia. Recent evidence increasingly supports a more articulated view where cerebellum and cortex, working closely in concert with basal ganglia, are also involved in tic production. Building on such evidence, this article proposes a computational model of the basal ganglia-cerebellar-thalamo-cortical system to study how motor tics are generated in Tourette syndrome. In particular, the model: (i) reproduces the main results of recent experiments about the involvement of the basal ganglia-cerebellar-thalamo-cortical system in tic generation; (ii) suggests an explanation of the system-level mechanisms underlying motor tic production: in this respect, the model predicts that the interplay between dopaminergic signal and cortical activity contributes to triggering the tic event and that the recently discovered basal ganglia-cerebellar anatomical pathway may support the involvement of the cerebellum in tic production; (iii) furnishes predictions on the amount of tics generated when striatal dopamine increases and when the cortex is externally stimulated. These predictions could be important in identifying new brain target areas for future therapies. Finally, the model represents the first computational attempt to study the role of the recently discovered basal ganglia-cerebellar anatomical links. Studying this non-cortex-mediated basal ganglia-cerebellar interaction could radically change our perspective about how these areas interact with each other and with the cortex. Overall, the model also shows the utility of casting Tourette syndrome within a system-level perspective rather than viewing it as related to the dysfunction of a single brain area.

The mirror system hypothesis for the evolution of the language-ready brain (e.g., [1, 2]) suggests a path for evolution of brain mechanisms atop the mirror system for grasping, with new processes supporting simple imitation, complex imitation, gesture, pantomime and finally protosign and protospeech. The present talk will briefly summarize the evolutionary story as background for a suggested program of research in neurolinguistics, with modeling challenges at the levels both of schemas and neural networks to make contact with data from psycholinguistics, neurophysiology, and neurology.

Motor training with the upper limb affected by stroke partially reverses the loss of cortical representation after lesion and has been proposed to increase spontaneous arm use. Moreover, repeated attempts to use the affected hand in daily activities create a form of practice that can potentially lead to further improvement in motor performance. We thus hypothesized that if motor retraining after stroke increases spontaneous arm use sufficiently, then the patient will enter a virtuous circle in which spontaneous arm use and motor performance reinforce each other. In contrast, if the dose of therapy is not sufficient to bring spontaneous use above threshold, then performance will not increase and the patient will further develop compensatory strategies with the less affected hand. To refine this hypothesis, we developed a computational model of bilateral hand use in arm reaching to study the interactions between adaptive decision making and motor relearning after motor cortex lesion. The model contains a left and a right motor cortex, each controlling the opposite arm, and a single action choice module. The action choice module learns, via reinforcement learning, the value of using each arm for reaching in specific directions. Each motor cortex uses a neural population code to specify the initial direction along which the contralateral hand moves towards a target. The motor cortex learns to minimize directional errors and to maximize neuronal activity for each movement. The derived learning rule accounts for the reversal of the loss of cortical representation after rehabilitation and the increase of this loss after stroke with insufficient rehabilitation. Further, our model exhibits nonlinear and bistable behavior: if natural recovery, motor training, or both, brings performance above a certain threshold, then training can be stopped, as the repeated spontaneous arm use provides a form of motor learning that further bootstraps performance and spontaneous use. Below this threshold, motor training is "in vain": there is little spontaneous arm use after training, the model exhibits learned nonuse, and compensatory movements with the less affected hand are reinforced. By exploring the nonlinear dynamics of stroke recovery using a biologically plausible neural model that accounts for reversal of the loss of motor cortex representation following rehabilitation or the lack thereof, respectively, we can explain previously hard to reconcile data on spontaneous arm use in stroke recovery. Further, our threshold prediction could be tested with an adaptive train-wait-train paradigm: if spontaneous arm use has increased in the "wait" period, then the threshold has been reached, and rehabilitation can be stopped. If spontaneous arm use is still low or has decreased, then another bout of rehabilitation is to be provided.

The article outlines an evolutionary account relating motivational systems to emotions and the cortical systems which elaborate them. It then addresses the issue of whether and how to characterise emotions in such a way that one might say that a robot has emotions even if they are not empathically linked to human emotions.

Presents a color image segmentation method which divides the color space into clusters. Competitive learning is used as a tool for clustering the color space based on the least sum-of-squares criterion. We show that competitive learning converges to approximate the optimum solution based on this criterion, theoretically and experimentally. We apply this method to various color scenes and show its efficiency as a color image segmentation method. We also show the effects of using different color coordinates to be clustered, with some experimental results.>

A computational framework of learning, recognition and reproduction of temporal sequences are provided, based on an interference theory of forgetting in short-term memory (STM), modelled as a network of neural units with mutual inhibition. The STM model provides information for recognition and reproduction of arbitrary temporal sequences. Sequences are acquired by a new learning rule, the attentional learning rule, which combines Hebbian learning and a normalization rule with sequential system activation. Acquired sequences can be recognized without being affected by speed of presentation or certain distortions in symbol form. Different layers of the STM model can be naturally constructed in a feedforward manner to recognize hierarchical sequences, significantly expanding the model's capability in a way similar to human information chunking. A model of sequence reproduction is presented that consists of two reciprocally connected networks, one of which behaves as a sequence recognizer. Reproduction of complex sequences can maintain interval lengths of sequence components, and vary the overall speed. A mechanism of degree self-organization based on a global inhibitor is proposed for the model to learn required context lengths in order to disambiguate associations in complex sequence reproduction. Certain implications of the model are discussed at the end of the paper.>

The authors propose that the toad discriminates visual objects based on temporal responses and that discrimination is reflected in different average neuronal firing rates at some higher visual center, hypothetically the anterior thalamus. This theory is developed through a large-scale neuronal simulation which includes retina, tectum, and anterior thalamus. The neural model based on this theory predicts that retinal R2 cells play a primary role in the discrimination via tectal small pear cells (SP) and R3 cells refine the feature analysis by inhibition. The simulation demonstrates that the retinal response to the trailing edge of a stimulus is as crucial for pattern discrimination as the response to the leading edge. The new dishabituation hierarchies are predicted by this model by shrinking stimulus size and reversing contrast

It is noted that almost all attempts to construct special-purpose robot programming languages have proceeded by taking a computer-programming language and adding some special primitives. Here, the authors have taken the approach of trying to define computation at its most primitive level in terms of the characteristics of the robot domain. They construct a special model of computation, called RS (robot schemas), with properties designed to facilitate sensory-based robot programming. This approach offers the potential to construct robot task representations which are easy to use and concise, and which execute in an efficient manner. The authors define the model formally using port automata. These definitions ensure consistency and well-definedness, and the facilitate plan verification and automatic plan generation.>

Automatic planning of an assembly task suitable for execution by a robot system requires analysis of constraints arising from task and object geometry as well as constraints arising from the specific configuration of the robot system. This paper describes our current efforts toward building a high-level planner that plans robot assembly tasks while confining its attention to constraints arising from task and object characteristics. The robot itself is modelled in terms of its basic capabilities such as, grasp, free motion and certain fine-motion commands. The planner works from a specification of the assembly operations, the objects involved in the operations, and an incomplete geometric model of the objects. The assembly planning problem is viewed as comprising of the determination of positions and orientations of component objects relative to each other and the determination of the robot motion primitives required to achieve these relations. The paper discusses how we decompose the assembly planning problem to reflect this view, the intermediate representations used, and our current implementations by use of an example assembly task.

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Artificial intelligence (AI) joins forces with brain research, cognitive psychology, and linguistics to probe the nature of intelligeice. It is asserted that the current AI emphasis on representation of knowledge will be augmented by increasing attention to cooperative computation and learning. AI also contributes to a man-machine symbiosis: providing specialized packets of intelligence to augment our own. As we augment our intelligence with our machines, building on current success with robotics, scene analysis, language understanding, and expert systems, we must better understand our own intelligence to enable the construction of high-bandwidth man–machine interfaces. This, too, is a form of cooperative computation.

This paper contributes to the literature on tree automata (also known as algebra automata), which is providing new insights at the interface between automata theory and formal language theory—an interface which hopefully will show increasing relevance both to linguists and compiler writers. Formal language theory has long employed pushdown automata, linear bounded automata, etc., to process, parse, and recognize the strings of a formal language. Lurking in the background have been the derivation trees which show how such strings can be derived by various rewriting rules from some initial symbol. The new approach (spurred by the linguists' interest in transformational grammars which actually transform derivation trees, and by Buchi's observation—built upon by such workers as Mezei, Thatcher, Eilenberg, Wright, and Giveon in the pages of Information and Control—that ordinary automata may be viewed as a special kind of universal algebra) looks at automata that act directly on the trees, rather than on the strings they produce.

This paper 1 compares the notions of Turing machine, finite automaton and neural net. A new notation is introduced to replace net diagrams. “Equivalence” theorems are proved for nets with receptors, and finite automata; and for nets with receptors and effectors, and Turing machines. These theorems are discussed in relation to papers of Copi, Elgot and Wright; Rabin and Scott; and McCulloch and Pitts. It is shown that sets of positive integers “accepted” by finite automata are recursive; and a strengthened form of a theorem of Kleene is proved.