Modularity, evolution, and the binding problem: a view from stability theory

Slotine, Jean‐Jacques E.; Lohmiller, Winfried

doi:10.1016/s0893-6080(00)00089-7

Cited by 68 publications

(65 citation statements)

References 23 publications

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“…This allows both stable aggregation of contracting systems, and variation or optimization of individual subsystems while preserving overall functionality (Slotine and Lohmiller, 2001). We present here three standard combinations of contracting systems which preserve both contraction of the system and diagonality of the metric.…”

Section: Combination Of Contracting Systemsmentioning

confidence: 99%

Where neuroscience and dynamic system theory meet autonomous robotics: A contracting basal ganglia model for action selection

et al. 2008

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Action selection, the problem of choosing what to do next, is central to any autonomous agent architecture. We use here a multidisciplinary approach at the convergence of neuroscience, dynamical systems theory and autonomous robotics, in order to propose an efficient action selection mechanism based on a new model of the basal ganglia. We first describe new developments of contraction theory regarding locally projected dynamical systems. We exploit these results to design a stable computational model of the cortico-basothalamo-cortical loops. Based on recent anatomical data, we include usually neglected neural projections, which participate in performing accurate selection. Finally, the efficiency of this model as an autonomous robot action selection mechanism is assessed in a standard survival task. The model exhibits valuable dithering avoidance and energy-saving properPreprint submitted to Elsevier Science 18 May 2017 ties, when compared with a simple if-then-else decision rule.

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Section: Combination Of Contracting Systemsmentioning

confidence: 99%

Where neuroscience and dynamic system theory meet autonomous robotics: A contracting basal ganglia model for action selection

et al. 2008

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“…Such modular architectures ( Figure 2) may exist within the spinal cord as well as in higher brain structures [71]. However, even if all modules are stable, not every combination of modules is guaranteed to be stable [72]. A load adaptation scheme designed on the basis of this approach, involving a flexible combination of simple computational elements [73], produced behavior that was qualitatively similar to human performance [74].…”

Section: Motor Learning and Adaptationmentioning

confidence: 99%

Computational approaches to motor control

Flash

Sejnowski²

2001

Current Opinion in Neurobiology

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New concepts and computational models that integrate behavioral and neurophysiological observations have addressed several of the most fundamental long-standing problems in motor control. These problems include the selection of particular trajectories among the large number of possibilities, the solution of inverse kinematics and dynamics problems, motor adaptation and the learning of sequential behaviors.

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“…However, Slotine and Lohmiller (2001) have shown that a certain form of stability, called contraction 6 , ensures that any combination of such contracting systems is also contracting.…”

Section: Mathematical Models For the Generation Of Discrete And Rhythmentioning

confidence: 99%

“…6 Contracting systems are defined as nonlinear dynamical systems in which "initial conditions or temporary disturbances are forgotten exponentially fast" (Slotine and Lohmiller (2001), p.138).…”

Section: Two/two Hypothesismentioning

confidence: 99%

Modeling discrete and rhythmic movements through motor primitives: a review

Dégallier

Ijspeert

2010

Biol Cybern

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Rhythmic and discrete movements are frequently considered separately in motor control, probably because different techniques are commonly used to study and model them. Yet, an increasing interest for a comprehensive model for movement generation requires to bridge the different perspectives arising from the study of those two types of movements. In this article, we consider discrete and rhythmic movement within the framework of motor primitives, i.e. of modular generation of movements. Thereby we hope to get an insight into the functional relationships between discrete and rhythmic movements and thus into a suitable representation for both of them. Within this framework we can define four possible categories of modeling for discrete and rhythmic movements depending on the required command signals and on the spinal processes involved in the generation of the movements. These categories are first discussed relatively to biological concepts such as force fields and central pattern generators and are then illustrated by several mathematical models based on dynamical system theory. A discussion on the plausibility of theses models concludes this work.

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Modularity, evolution, and the binding problem: a view from stability theory

Cited by 68 publications

References 23 publications

Where neuroscience and dynamic system theory meet autonomous robotics: A contracting basal ganglia model for action selection

Where neuroscience and dynamic system theory meet autonomous robotics: A contracting basal ganglia model for action selection

Computational approaches to motor control

Modeling discrete and rhythmic movements through motor primitives: a review

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