An oscillatory neuromotor model of handwriting generation

Garipelli, Gangadhar; Joseph, Denny; Chakravarthy, V. Srinivasa

doi:10.1007/s10032-007-0046-0

Cited by 47 publications

(42 citation statements)

References 21 publications

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“…A similar view of preparation had emerged out of a computational neuromotor model of handwriting generation (Gangadhar et al 2007). The model is inspired by Hollerbach (1981) oscillator theory of handwriting, according to which handwriting may be regarded as superimposed motion of two oscillatory processes-one vertical and the other horizontal.…”

Section: Motor Preparationmentioning

confidence: 97%

“…Following target onset, activity settles (curved paths) to a subspace of states appropriate for the desired movement (shaded area) (adapted from Churchland et al 2006) various parameter of oscillation like phase, amplitude at isolated instants. Gangadhar et al (2007) describe a network of neural oscillators that can learn arbitrary strokes by resolving stroke velocities in a Fourier-like fashion, in terms of oscillatory activities of neurons in the network. Accurate stroke execution depends not only the 'weights' of the network, but critically on the initial phase of the oscillators.…”

Section: Motor Preparationmentioning

confidence: 99%

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What do the basal ganglia do? A modeling perspective

2010

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Basal ganglia (BG) constitute a network of seven deep brain nuclei involved in a variety of crucial brain functions including: action selection, action gating, reward based learning, motor preparation, timing, etc. In spite of the immense amount of data available today, researchers continue to wonder how a single deep brain circuit performs such a bewildering range of functions. Computational models of BG have focused on individual functions and fail to give an integrative picture of BG function. A major breakthrough in our understanding of BG function is perhaps the insight that activities of mesencephalic dopaminergic cells represent some form of 'reward' to the organism. This insight enabled application of tools from 'reinforcement learning,' a branch of machine learning, in the study of BG function. Nevertheless, in spite of these bright spots, we are far from the goal of arriving at a comprehensive understanding of these 'mysterious nuclei.' A comprehensive knowledge of BG function has the potential to radically alter treatment and management of a variety of BG-related neurological disorders (Parkinson's disease, Huntington's chorea, etc.) and neuropsychiatric disorders (schizophrenia, obsessive compulsive disorder, etc.) also. In this article, we review the existing modeling literature on BG and hypothesize an integrative picture of the function of these nuclei.

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Section: Motor Preparationmentioning

confidence: 97%

Section: Motor Preparationmentioning

confidence: 99%

What do the basal ganglia do? A modeling perspective

2010

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“…the yo-yo (Hashimoto & Noritsugu, 1996;Jin et al, 2009;Jin & Zacksenhouse, 2003;Žlajpah, 2006) or a gyroscopic device called the Powerball (Cafuta & Curk, 2008;Gams et al, 2007;Heyda, 2002;Petrič et al, 2010), juggling (Buehler et al, 1994;Ronsse et al, 2007;Schaal & Atkeson, 1993;Williamson, 1999) and locomotion (Ijspeert, 2008b;Ilg et al, 1999;Morimoto et al, 2008). Rhythmic tasks are also handshaking (Jindai & Watanabe, 2007;Kasuga & Hashimoto, 2005;Sato et al, 2007) and even handwriting (Gangadhar et al, 2007;Hollerbach, 1981). Performing these tasks with robots requires appropriate trajectory generation and foremost precise frequency tuning by determining the basic frequency.…”

Section: Overview Of the Research Fieldmentioning

confidence: 99%

Performing Periodic Tasks: On-Line Learning, Adaptation and Synchronization with External Signals

Gams¹,

Petrič²,

Ude³

et al. 2012

The Future of Humanoid Robots - Research and Applications

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“…The network models of Schomaker (1991) and Kalveram (1998) had certain difficulties, which were addressed in the oscillatory neural network model of Gangadhar et al (2007). The oscillatory neural network described in Gangadhar et al (2007), had earlier been expanded as a model of copying also . The studies noted above are examples of oscillatory network models of handwriting movements.…”

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confidence: 98%

A phase dynamic model of systematic error in simple copying tasks

et al. 2009

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A crucial insight into handwriting dynamics is embodied in the idea that stable, robust handwriting movements correspond to attractors of an oscillatory dynamical system. We present a phase dynamic model of visuomotor performance involved in copying simple oriented lines. Our studies on human performance in copying oriented lines revealed a systematic error pattern in orientation of drawn lines, i.e., lines at certain orientation are drawn more accurately than at other values. Furthermore, human subjects exhibit "flips" in direction at certain characteristic orientations. It is argued that this flipping behavior has its roots in the fact that copying process is inherently ambiguous-a line of given orientation may be drawn in two different (mutually opposite) directions producing the same end result. The systematic error patterns seen in human copying performance is probably a result of the attempt of our visuomotor system to cope with this ambiguity and still be able to produce accurate copying movements. The proposed nonlinear phase-dynamic model explains the experimentally observed copying error pattern and also the flipping behavior with remarkable accuracy.

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An oscillatory neuromotor model of handwriting generation

Cited by 47 publications

References 21 publications

What do the basal ganglia do? A modeling perspective

What do the basal ganglia do? A modeling perspective

Performing Periodic Tasks: On-Line Learning, Adaptation and Synchronization with External Signals

A phase dynamic model of systematic error in simple copying tasks

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