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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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Switching errors as a function of the number of inhibitory cross connections kc and global inhibitory connections kg. (a) Spiking activity of a reduced network connected as 1→2,7 with kc = kg = 7. Activity of 10% of the neurons is shown. A switching error in which both successor chains are activated occurs at around t = 140 ms. (b) As in (a), but depicting a switching error in which neither successor chain is activated at around t = 140 ms. (c) Percentage of trials resulting in the activation of both successor chains as a function of the number of inhibitory cross connections (horizontal axis) and the number of global inhibitory connections (vertical axis). (d) As in (c) but for the case that neither of the successor chains is activated. The red cross indicates the chosen parameter set kc = kg = 7
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Fig8: Switching errors as a function of the number of inhibitory cross connections kc and global inhibitory connections kg. (a) Spiking activity of a reduced network connected as 1→2,7 with kc = kg = 7. Activity of 10% of the neurons is shown. A switching error in which both successor chains are activated occurs at around t = 140 ms. (b) As in (a), but depicting a switching error in which neither successor chain is activated at around t = 140 ms. (c) Percentage of trials resulting in the activation of both successor chains as a function of the number of inhibitory cross connections (horizontal axis) and the number of global inhibitory connections (vertical axis). (d) As in (c) but for the case that neither of the successor chains is activated. The red cross indicates the chosen parameter set kc = kg = 7

Mentions: We now determine the optimal values for kg and kc, the number of outgoing connections that each inhibitory neuron in a synfire chain establishes with randomly selected neurons and with neurons in the competitor chain, respectively. To this end, we investigate the switching error in a network where the final pool of chain 1 is connected to the initial pools of chains 2 and 7, and the final pools of chains 2 and 7 have no outgoing feed-forward connections. For a given configuration of kg and kc, the network is stimulated 100 times by applying a Gaussian pulse packet to the initial pool of chain 1. We record the occurrence of the two types of switching error: the activation of both successor chains as depicted in Fig. 8(a) and the activation of neither of the successor chains as illustrated in Fig. 8(b).Fig. 8


Compositionality of arm movements can be realized by propagating synchrony.

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

Switching errors as a function of the number of inhibitory cross connections kc and global inhibitory connections kg. (a) Spiking activity of a reduced network connected as 1→2,7 with kc = kg = 7. Activity of 10% of the neurons is shown. A switching error in which both successor chains are activated occurs at around t = 140 ms. (b) As in (a), but depicting a switching error in which neither successor chain is activated at around t = 140 ms. (c) Percentage of trials resulting in the activation of both successor chains as a function of the number of inhibitory cross connections (horizontal axis) and the number of global inhibitory connections (vertical axis). (d) As in (c) but for the case that neither of the successor chains is activated. The red cross indicates the chosen parameter set kc = kg = 7
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3108016&req=5

Fig8: Switching errors as a function of the number of inhibitory cross connections kc and global inhibitory connections kg. (a) Spiking activity of a reduced network connected as 1→2,7 with kc = kg = 7. Activity of 10% of the neurons is shown. A switching error in which both successor chains are activated occurs at around t = 140 ms. (b) As in (a), but depicting a switching error in which neither successor chain is activated at around t = 140 ms. (c) Percentage of trials resulting in the activation of both successor chains as a function of the number of inhibitory cross connections (horizontal axis) and the number of global inhibitory connections (vertical axis). (d) As in (c) but for the case that neither of the successor chains is activated. The red cross indicates the chosen parameter set kc = kg = 7
Mentions: We now determine the optimal values for kg and kc, the number of outgoing connections that each inhibitory neuron in a synfire chain establishes with randomly selected neurons and with neurons in the competitor chain, respectively. To this end, we investigate the switching error in a network where the final pool of chain 1 is connected to the initial pools of chains 2 and 7, and the final pools of chains 2 and 7 have no outgoing feed-forward connections. For a given configuration of kg and kc, the network is stimulated 100 times by applying a Gaussian pulse packet to the initial pool of chain 1. We record the occurrence of the two types of switching error: the activation of both successor chains as depicted in Fig. 8(a) and the activation of neither of the successor chains as illustrated in Fig. 8(b).Fig. 8

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
Related in: MedlinePlus