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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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The effect of synfire chain switching realized by unstructured cross-inhibition on collective signals. (a) Spiking activity of 10% of the neurons of a reduced network with synfire chain competition, connected as 1→2,7 and 2,7→1. (b) Firing rate (black), average membrane potential (red) and approximated LFP (blue; arbitrary units) calculated from activity in the reduced network. Arrows indicate signatures of synfire chain switching in the collective signals. (c, d) As in (a, b) but without synfire chain competition: connected as 1→2→7→1
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Fig10: The effect of synfire chain switching realized by unstructured cross-inhibition on collective signals. (a) Spiking activity of 10% of the neurons of a reduced network with synfire chain competition, connected as 1→2,7 and 2,7→1. (b) Firing rate (black), average membrane potential (red) and approximated LFP (blue; arbitrary units) calculated from activity in the reduced network. Arrows indicate signatures of synfire chain switching in the collective signals. (c, d) As in (a, b) but without synfire chain competition: connected as 1→2→7→1

Mentions: Figure 10 demonstrates synfire chain switching on the basis of unstructured cross-inhibition and its effect on collective signals. The reduced network connectivity and the spike data analysis are as previously described in Section 3.2.1.Fig. 10


Compositionality of arm movements can be realized by propagating synchrony.

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

The effect of synfire chain switching realized by unstructured cross-inhibition on collective signals. (a) Spiking activity of 10% of the neurons of a reduced network with synfire chain competition, connected as 1→2,7 and 2,7→1. (b) Firing rate (black), average membrane potential (red) and approximated LFP (blue; arbitrary units) calculated from activity in the reduced network. Arrows indicate signatures of synfire chain switching in the collective signals. (c, d) As in (a, b) but without synfire chain competition: connected as 1→2→7→1
© Copyright Policy
Related In: Results  -  Collection

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

Fig10: The effect of synfire chain switching realized by unstructured cross-inhibition on collective signals. (a) Spiking activity of 10% of the neurons of a reduced network with synfire chain competition, connected as 1→2,7 and 2,7→1. (b) Firing rate (black), average membrane potential (red) and approximated LFP (blue; arbitrary units) calculated from activity in the reduced network. Arrows indicate signatures of synfire chain switching in the collective signals. (c, d) As in (a, b) but without synfire chain competition: connected as 1→2→7→1
Mentions: Figure 10 demonstrates synfire chain switching on the basis of unstructured cross-inhibition and its effect on collective signals. The reduced network connectivity and the spike data analysis are as previously described in Section 3.2.1.Fig. 10

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