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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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Generation of scribbling trajectories. (a) Spiking activity of BFCN and SFCN. Activity of 1% of the neurons is shown. The activity of each synfire chain is plotted in a strip marked by horizontal lines in the color of the corresponding arrow in velocity space shown in (b). BFC activity (blue) is plotted in the top strip. Above the raster plot the average firing rate of the SFCN (red) and the BFCN (blue) is plotted. (b) Reproduction of Fig. 4 for ease of reference. (c) Scribbling trajectory extracted from the spiking activity using population coding. Segments are drawn in the color of the most active chain
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Fig16: Generation of scribbling trajectories. (a) Spiking activity of BFCN and SFCN. Activity of 1% of the neurons is shown. The activity of each synfire chain is plotted in a strip marked by horizontal lines in the color of the corresponding arrow in velocity space shown in (b). BFC activity (blue) is plotted in the top strip. Above the raster plot the average firing rate of the SFCN (red) and the BFCN (blue) is plotted. (b) Reproduction of Fig. 4 for ease of reference. (c) Scribbling trajectory extracted from the spiking activity using population coding. Segments are drawn in the color of the most active chain

Mentions: The spiking activity of the complete network underlying the average firing rates shown in Fig. 15 is given in Fig. 16(a). The trajectory extracted from the spiking activity as described in Section 2.3 is shown in Fig. 16(c). The trajectory consists of a long random sequence of parabolic movement primitives. Small overlaps can be seen at the transition points where both successor chains are active before one of the chains wins the competition. The distribution of the length n of an uninterrupted sequence is well fitted by , where p0 is the probability that neither successor chain is activated during synfire chain switching (data not shown).Fig. 16


Compositionality of arm movements can be realized by propagating synchrony.

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

Generation of scribbling trajectories. (a) Spiking activity of BFCN and SFCN. Activity of 1% of the neurons is shown. The activity of each synfire chain is plotted in a strip marked by horizontal lines in the color of the corresponding arrow in velocity space shown in (b). BFC activity (blue) is plotted in the top strip. Above the raster plot the average firing rate of the SFCN (red) and the BFCN (blue) is plotted. (b) Reproduction of Fig. 4 for ease of reference. (c) Scribbling trajectory extracted from the spiking activity using population coding. Segments are drawn in the color of the most active chain
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3108016&req=5

Fig16: Generation of scribbling trajectories. (a) Spiking activity of BFCN and SFCN. Activity of 1% of the neurons is shown. The activity of each synfire chain is plotted in a strip marked by horizontal lines in the color of the corresponding arrow in velocity space shown in (b). BFC activity (blue) is plotted in the top strip. Above the raster plot the average firing rate of the SFCN (red) and the BFCN (blue) is plotted. (b) Reproduction of Fig. 4 for ease of reference. (c) Scribbling trajectory extracted from the spiking activity using population coding. Segments are drawn in the color of the most active chain
Mentions: The spiking activity of the complete network underlying the average firing rates shown in Fig. 15 is given in Fig. 16(a). The trajectory extracted from the spiking activity as described in Section 2.3 is shown in Fig. 16(c). The trajectory consists of a long random sequence of parabolic movement primitives. Small overlaps can be seen at the transition points where both successor chains are active before one of the chains wins the competition. The distribution of the length n of an uninterrupted sequence is well fitted by , where p0 is the probability that neither successor chain is activated during synfire chain switching (data not shown).Fig. 16

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