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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 (horizontal axis) and the number of global inhibitory connections kg (vertical axis). (a) Percentage of trials resulting in the activation of both successor chains (b) Percentage of trials resulting in the activation of neither of the successor chains. In both plots the red cross indicates the chosen parameter set kc = 19 and kg = 7
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Fig11: Switching errors as a function of the number of inhibitory cross connections kc (horizontal axis) and the number of global inhibitory connections kg (vertical axis). (a) Percentage of trials resulting in the activation of both successor chains (b) Percentage of trials resulting in the activation of neither of the successor chains. In both plots the red cross indicates the chosen parameter set kc = 19 and kg = 7

Mentions: As in the case of structured cross-inhibition, to find the optimal values for the number of outgoing connections that each inhibitory neuron in a synfire chain establishes with kg randomly selected neurons and kc neurons in its competitor chain, 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. Figure 11 shows the probabilities of generating the two types of switching errors for the network as functions of kc and kg. Each configuration is stimulated 100 times by applying a Gaussian pulse packet to the initial pool of chain 1. As the number of unstructured cross-inhibition connections kc increases, the probability of activating both successor chains decreases (Fig. 11(a)) whilst the probability that neither chain is activated increases (Fig. 11(b)). The influence of kg on the switching errors is less pronounced but acts in the same direction as kc. A good choice for the number of inhibitory connections is kc = 19 and kg = 7: for this configuration, the probability of activating both successor chains is p2 = 0%, whereas the probability that neither chain is activated is . As before, the means and standard deviations for these two values are calculated by performing the 100 switching trials on 100 different network realizations. This configuration exhibits a reasonable compromise between reliable switching and stability and is used for the rest of this manuscript. As in the case of structured cross-inhibition, for a given realization of the network a bias towards selecting chain 2 or chain 7 can be observed, however on average there is no bias due to the symmetry of the connection distribution (data not shown).Fig. 11


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 (horizontal axis) and the number of global inhibitory connections kg (vertical axis). (a) Percentage of trials resulting in the activation of both successor chains (b) Percentage of trials resulting in the activation of neither of the successor chains. In both plots the red cross indicates the chosen parameter set kc = 19 and kg = 7
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Related In: Results  -  Collection

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

Fig11: Switching errors as a function of the number of inhibitory cross connections kc (horizontal axis) and the number of global inhibitory connections kg (vertical axis). (a) Percentage of trials resulting in the activation of both successor chains (b) Percentage of trials resulting in the activation of neither of the successor chains. In both plots the red cross indicates the chosen parameter set kc = 19 and kg = 7
Mentions: As in the case of structured cross-inhibition, to find the optimal values for the number of outgoing connections that each inhibitory neuron in a synfire chain establishes with kg randomly selected neurons and kc neurons in its competitor chain, 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. Figure 11 shows the probabilities of generating the two types of switching errors for the network as functions of kc and kg. Each configuration is stimulated 100 times by applying a Gaussian pulse packet to the initial pool of chain 1. As the number of unstructured cross-inhibition connections kc increases, the probability of activating both successor chains decreases (Fig. 11(a)) whilst the probability that neither chain is activated increases (Fig. 11(b)). The influence of kg on the switching errors is less pronounced but acts in the same direction as kc. A good choice for the number of inhibitory connections is kc = 19 and kg = 7: for this configuration, the probability of activating both successor chains is p2 = 0%, whereas the probability that neither chain is activated is . As before, the means and standard deviations for these two values are calculated by performing the 100 switching trials on 100 different network realizations. This configuration exhibits a reasonable compromise between reliable switching and stability and is used for the rest of this manuscript. As in the case of structured cross-inhibition, for a given realization of the network a bias towards selecting chain 2 or chain 7 can be observed, however on average there is no bias due to the symmetry of the connection distribution (data not shown).Fig. 11

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