Limits...
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
Unstructured 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 inhibitory neuron in a successor chain projects to a number of randomly chosen neurons in the competitor chain (blue rounded arrows)
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC3108016&req=5

Fig9: Unstructured 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 inhibitory neuron in a successor chain projects to a number of randomly chosen neurons in the competitor chain (blue rounded arrows)

Mentions: Figure 9 illustrates the unstructured cross-inhibition architecture. As above, 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 and symmetric connections ensure that the successor chains are stimulated equally. All inhibitory neurons of one potential successor chain project to kc randomly chosen neurons from the other potential successor chain, and vice versa. This architecture uniformly inhibits synfire activity in the competitor, rather than simply the propagation to the next pool as in Section 3.2.1. This architecture also leads to a competition between the activated synfire chains, such that the activity in the losing chain dies away and the activity in the winning chain continues propagating.Fig. 9


Compositionality of arm movements can be realized by propagating synchrony.

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

Unstructured 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 inhibitory neuron in a successor chain projects to a number of randomly chosen neurons in the competitor chain (blue rounded arrows)
© Copyright Policy
Related In: Results  -  Collection

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

Fig9: Unstructured 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 inhibitory neuron in a successor chain projects to a number of randomly chosen neurons in the competitor chain (blue rounded arrows)
Mentions: Figure 9 illustrates the unstructured cross-inhibition architecture. As above, 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 and symmetric connections ensure that the successor chains are stimulated equally. All inhibitory neurons of one potential successor chain project to kc randomly chosen neurons from the other potential successor chain, and vice versa. This architecture uniformly inhibits synfire activity in the competitor, rather than simply the propagation to the next pool as in Section 3.2.1. This architecture also leads to a competition between the activated synfire chains, such that the activity in the losing chain dies away and the activity in the winning chain continues propagating.Fig. 9

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