The neural dynamics of goal-directed arm movements: A developmental perspective

Zibner, Stephan K. U.; Tekülve, Jan; Schöner, Gregor

doi:10.1109/devlrn.2015.7346134

Cited by 6 publications

(3 citation statements)

References 35 publications

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“…Beyond the toy example used here, DFT architectures have exploited the scaling properties of DFT to push both toward generating motor behaviors in autonomous robots (Knips et al, 2014; Strauss et al, 2015; Zibner et al, 2015) and toward higher cognitive function, such as grounding spatial language (Richter et al, 2014a), parsing action sequences (Lobato et al, 2015), or task learning (Sousa et al, 2015). These architectures are fairly complex.…”

Section: Discussionmentioning

confidence: 99%

Developing Dynamic Field Theory Architectures for Embodied Cognitive Systems with cedar

et al. 2016

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Embodied artificial cognitive systems, such as autonomous robots or intelligent observers, connect cognitive processes to sensory and effector systems in real time. Prime candidates for such embodied intelligence are neurally inspired architectures. While components such as forward neural networks are well established, designing pervasively autonomous neural architectures remains a challenge. This includes the problem of tuning the parameters of such architectures so that they deliver specified functionality under variable environmental conditions and retain these functions as the architectures are expanded. The scaling and autonomy problems are solved, in part, by dynamic field theory (DFT), a theoretical framework for the neural grounding of sensorimotor and cognitive processes. In this paper, we address how to efficiently build DFT architectures that control embodied agents and how to tune their parameters so that the desired cognitive functions emerge while such agents are situated in real environments. In DFT architectures, dynamic neural fields or nodes are assigned dynamic regimes, that is, attractor states and their instabilities, from which cognitive function emerges. Tuning thus amounts to determining values of the dynamic parameters for which the components of a DFT architecture are in the specified dynamic regime under the appropriate environmental conditions. The process of tuning is facilitated by the software framework cedar, which provides a graphical interface to build and execute DFT architectures. It enables to change dynamic parameters online and visualize the activation states of any component while the agent is receiving sensory inputs in real time. Using a simple example, we take the reader through the workflow of conceiving of DFT architectures, implementing them on embodied agents, tuning their parameters, and assessing performance while the system is coupled to real sensory inputs.

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Section: Discussionmentioning

confidence: 99%

Developing Dynamic Field Theory Architectures for Embodied Cognitive Systems with cedar

et al. 2016

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“…2011), as are the processes needed to extract movement parameters from such a scene representation (Erlhagen and Schöner 2002; Zibner et al. 2015). The initiation and termination of a discrete movement are modeled by a neural dynamic switching process (Schöner 1990; Richter et al.…”

Section: Modelmentioning

confidence: 99%

“…The scene representation and movement preparation processes (gray boxes) are replaced by parameter values, but have been modeled elsewhere (Zibner et al. 2011, 2015). An approximate mapping onto structures of the CNS is sketched by the boxes in light gray.…”

Section: Introductionmentioning

confidence: 99%

A process account of the uncontrolled manifold structure of joint space variance in pointing movements

2019

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In many situations, the human movement system has more degrees of freedom than needed to achieve a given movement task. Martin et al. (Neural Comput 21(5):1371–1414, 2009 ) accounted for signatures of such redundancy like self-motion and motor equivalence in a process model in which a neural oscillator generated timed end-effector virtual trajectories that a neural dynamics transformed into joint virtual trajectories while decoupling task-relevant and task-irrelevant combinations of joint angles. Neural control of muscle activation and the biomechanical dynamics of the arm were taken into account. The model did not address the main signature of redundancy, however, the UCM structure of variance: Many experimental studies have shown that across repetitions, variance of joint configuration trajectories is structured. Combinations of joint angles that affect task variables (lying in the uncontrolled manifold, UCM) are much more variable than combinations of joint angles that do not. This finding has been robust across movement systems, age, and tasks and is often preserved in clinical populations as well. Here, we provide an account for the UCM structure of variance by adding four types of noise sources to the model of Martin et al. (Neural Comput 21(5):1371–1414, 2009 ). Comparing the model to human pointing movements and systematically examining the role of each noise source and mechanism, we identify three causes of the UCM effect, all of which, we argue, contribute: (1) the decoupling of motor commands across the task-relevant and task-irrelevant subspaces together with “neural” noise at the level of these motor commands; (2) “muscle noise” combined with imperfect control of the limb; (3) back-coupling of sensed joint configurations into the motor commands which then yield to the sensed joint configuration within the UCM.

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The sequential organization of movement is critical to the development of reaching: A neural dynamics account

Zibner

Tekülve

Schöner

2015

2015 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob)

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The neural dynamics of goal-directed arm movements: A developmental perspective

Cited by 6 publications

References 35 publications

Developing Dynamic Field Theory Architectures for Embodied Cognitive Systems with cedar

Developing Dynamic Field Theory Architectures for Embodied Cognitive Systems with cedar

A process account of the uncontrolled manifold structure of joint space variance in pointing movements

The sequential organization of movement is critical to the development of reaching: A neural dynamics account

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