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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.

Show MeSH
Structured cross-inhibition architecture. The final pool of one synfire chain activates the initial pools of two potential successor chains (excitatory connections illustrated by black pointed arrows). Each pool in a successor chain inhibits the next pool in the competitor chain (blue rounded arrows)
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Fig6: Structured cross-inhibition architecture. The final pool of one synfire chain activates the initial pools of two potential successor chains (excitatory connections illustrated by black pointed arrows). Each pool in a successor chain inhibits the next pool in the competitor chain (blue rounded arrows)

Mentions: Figure 6 illustrates the structured cross-inhibition architecture. Each neuron in the initial pools of the potential successor chains is activated by p·CE randomly chosen excitatory neurons from the final pool of the preceding chain. The symmetric connections ensure that the successor chains are stimulated equally. All inhibitory neurons of pool i of one potential successor chain project to kc neurons of pool i + 1 of the other potential successor chain, and vice versa. Thus each wave of synchronous activity directly inhibits the propagation of the activity to the next pool in the competitor chain, leading to a competition. The activity in the losing chain dies away leaving the activity in the winning chain to continue propagating, thus realizing a switching mechanism.Fig. 6


Compositionality of arm movements can be realized by propagating synchrony.

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

Structured cross-inhibition architecture. The final pool of one synfire chain activates the initial pools of two potential successor chains (excitatory connections illustrated by black pointed arrows). Each pool in a successor chain inhibits the next pool in the competitor chain (blue rounded arrows)
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3108016&req=5

Fig6: Structured cross-inhibition architecture. The final pool of one synfire chain activates the initial pools of two potential successor chains (excitatory connections illustrated by black pointed arrows). Each pool in a successor chain inhibits the next pool in the competitor chain (blue rounded arrows)
Mentions: Figure 6 illustrates the structured cross-inhibition architecture. Each neuron in the initial pools of the potential successor chains is activated by p·CE randomly chosen excitatory neurons from the final pool of the preceding chain. The symmetric connections ensure that the successor chains are stimulated equally. All inhibitory neurons of pool i of one potential successor chain project to kc neurons of pool i + 1 of the other potential successor chain, and vice versa. Thus each wave of synchronous activity directly inhibits the propagation of the activity to the next pool in the competitor chain, leading to a competition. The activity in the losing chain dies away leaving the activity in the winning chain to continue propagating, thus realizing a switching mechanism.Fig. 6

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