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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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Activity state transition in the BFCN with global inhibition, network set-up top right. (a) Spiking activity in the BFCN plotted with an external excitatory Poissonian input of fex = 7.65 kHz to each neuron and . Activity of 10% of the neurons is shown. (b) As in (a) but for fex = 7.9 kHz (c) Oscillation frequency of synfire waves as a function of the rate of the external Poisson input for different values of global inhibition:  (black squares),  (dark gray disks),  (light gray triangles)
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Fig14: Activity state transition in the BFCN with global inhibition, network set-up top right. (a) Spiking activity in the BFCN plotted with an external excitatory Poissonian input of fex = 7.65 kHz to each neuron and . Activity of 10% of the neurons is shown. (b) As in (a) but for fex = 7.9 kHz (c) Oscillation frequency of synfire waves as a function of the rate of the external Poisson input for different values of global inhibition: (black squares), (dark gray disks), (light gray triangles)

Mentions: The activity transition in the BFCN can be seen with greater clarity by investigating a more general version of the network with non-zero global inhibition () which balances the network activity and a variable strength of external drive. An example with is given in Fig. 14(a) and (b). If the external drive is chosen below the transition point (fex = 7.65 kHz) the network exhibits low rate asynchronous activity and no spontaneous synfire activity. This is illustrated in Fig. 14(a). If the external drive is chosen above the transition point (fex = 7.9 kHz in Fig. 14(b)), the network exhibits periodic waves of synfire activity. The oscillation frequency of the synfire activity increases with increasing external drive fex and the transition point shifts to higher values of fex with increased global inhibition (see Fig. 14(c)).Fig. 14


Compositionality of arm movements can be realized by propagating synchrony.

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

Activity state transition in the BFCN with global inhibition, network set-up top right. (a) Spiking activity in the BFCN plotted with an external excitatory Poissonian input of fex = 7.65 kHz to each neuron and . Activity of 10% of the neurons is shown. (b) As in (a) but for fex = 7.9 kHz (c) Oscillation frequency of synfire waves as a function of the rate of the external Poisson input for different values of global inhibition:  (black squares),  (dark gray disks),  (light gray triangles)
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3108016&req=5

Fig14: Activity state transition in the BFCN with global inhibition, network set-up top right. (a) Spiking activity in the BFCN plotted with an external excitatory Poissonian input of fex = 7.65 kHz to each neuron and . Activity of 10% of the neurons is shown. (b) As in (a) but for fex = 7.9 kHz (c) Oscillation frequency of synfire waves as a function of the rate of the external Poisson input for different values of global inhibition: (black squares), (dark gray disks), (light gray triangles)
Mentions: The activity transition in the BFCN can be seen with greater clarity by investigating a more general version of the network with non-zero global inhibition () which balances the network activity and a variable strength of external drive. An example with is given in Fig. 14(a) and (b). If the external drive is chosen below the transition point (fex = 7.65 kHz) the network exhibits low rate asynchronous activity and no spontaneous synfire activity. This is illustrated in Fig. 14(a). If the external drive is chosen above the transition point (fex = 7.9 kHz in Fig. 14(b)), the network exhibits periodic waves of synfire activity. The oscillation frequency of the synfire activity increases with increasing external drive fex and the transition point shifts to higher values of fex with increased global inhibition (see Fig. 14(c)).Fig. 14

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