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Compositionality of arm movements can be realized by propagating synchrony.

Hanuschkin A, Herrmann JM, Morrison A, Diesmann M - J Comput Neurosci (2010)

Bottom Line: Here, we map the propagation of activity in a chain to a linearly evolving preferred velocity, which results in parabolic segments that fulfill the two-thirds power law.The model provides an explanation for the segmentation of the trajectory and the experimentally observed deviations of the trajectory from the parabolic shape at primitive transition sites.Furthermore, the model predicts low frequency oscillations (<10 Hz) of the motor cortex local field potential during ongoing movements and increasing firing rates of non-specific motor cortex neurons before movement onset.

View Article: PubMed Central - PubMed

Affiliation: Functional Neural Circuits Group, Faculty of Biology, Schänzlestrasse 1, 79104, Freiburg, Germany. hanuschkin@bccn.uni-freiburg.de

ABSTRACT
We present a biologically plausible spiking neuronal network model of free monkey scribbling that reproduces experimental findings on cortical activity and the properties of the scribbling trajectory. The model is based on the idea that synfire chains can encode movement primitives. Here, we map the propagation of activity in a chain to a linearly evolving preferred velocity, which results in parabolic segments that fulfill the two-thirds power law. Connections between chains that match the final velocity of one encoded primitive to the initial velocity of the next allow the composition of random sequences of primitives with smooth transitions. The model provides an explanation for the segmentation of the trajectory and the experimentally observed deviations of the trajectory from the parabolic shape at primitive transition sites. Furthermore, the model predicts low frequency oscillations (<10 Hz) of the motor cortex local field potential during ongoing movements and increasing firing rates of non-specific motor cortex neurons before movement onset.

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Mapping uniform motion to parabolic motion. (a) Uniform motion along a straight line in velocity space corresponding to motion with constant acceleration (arbitrary units). Motion starts at time t0 and finishes at time t1, t1/2 marks the middle point. (b) Parabolic trajectory in position space corresponding to (a), given an initial position
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Fig1: Mapping uniform motion to parabolic motion. (a) Uniform motion along a straight line in velocity space corresponding to motion with constant acceleration (arbitrary units). Motion starts at time t0 and finishes at time t1, t1/2 marks the middle point. (b) Parabolic trajectory in position space corresponding to (a), given an initial position

Mentions: Constant accelerations are equivalent to velocities that change linearly in time, as . In velocity space, linearly evolving velocities () are represented by a uniform motion along a straight line. Therefore, straight lines in velocity space can be mapped into parabolic trajectories in position space, as illustrated in Fig. 1.Fig. 1


Compositionality of arm movements can be realized by propagating synchrony.

Hanuschkin A, Herrmann JM, Morrison A, Diesmann M - J Comput Neurosci (2010)

Mapping uniform motion to parabolic motion. (a) Uniform motion along a straight line in velocity space corresponding to motion with constant acceleration (arbitrary units). Motion starts at time t0 and finishes at time t1, t1/2 marks the middle point. (b) Parabolic trajectory in position space corresponding to (a), given an initial position
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3108016&req=5

Fig1: Mapping uniform motion to parabolic motion. (a) Uniform motion along a straight line in velocity space corresponding to motion with constant acceleration (arbitrary units). Motion starts at time t0 and finishes at time t1, t1/2 marks the middle point. (b) Parabolic trajectory in position space corresponding to (a), given an initial position
Mentions: Constant accelerations are equivalent to velocities that change linearly in time, as . In velocity space, linearly evolving velocities () are represented by a uniform motion along a straight line. Therefore, straight lines in velocity space can be mapped into parabolic trajectories in position space, as illustrated in Fig. 1.Fig. 1

Bottom Line: Here, we map the propagation of activity in a chain to a linearly evolving preferred velocity, which results in parabolic segments that fulfill the two-thirds power law.The model provides an explanation for the segmentation of the trajectory and the experimentally observed deviations of the trajectory from the parabolic shape at primitive transition sites.Furthermore, the model predicts low frequency oscillations (<10 Hz) of the motor cortex local field potential during ongoing movements and increasing firing rates of non-specific motor cortex neurons before movement onset.

View Article: PubMed Central - PubMed

Affiliation: Functional Neural Circuits Group, Faculty of Biology, Schänzlestrasse 1, 79104, Freiburg, Germany. hanuschkin@bccn.uni-freiburg.de

ABSTRACT
We present a biologically plausible spiking neuronal network model of free monkey scribbling that reproduces experimental findings on cortical activity and the properties of the scribbling trajectory. The model is based on the idea that synfire chains can encode movement primitives. Here, we map the propagation of activity in a chain to a linearly evolving preferred velocity, which results in parabolic segments that fulfill the two-thirds power law. Connections between chains that match the final velocity of one encoded primitive to the initial velocity of the next allow the composition of random sequences of primitives with smooth transitions. The model provides an explanation for the segmentation of the trajectory and the experimentally observed deviations of the trajectory from the parabolic shape at primitive transition sites. Furthermore, the model predicts low frequency oscillations (<10 Hz) of the motor cortex local field potential during ongoing movements and increasing firing rates of non-specific motor cortex neurons before movement onset.

Show MeSH