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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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Synfire chain connectivity. Excitatory neurons (open circles) of pool i are connected to pool i + 1 in a feed-forward manner. Inhibitory neurons (blue filled circles) are projecting globally to the network. Connections are visualized by arrows in the corresponding (black = excitatory, blue = inhibitory) colors with pointed (excitatory) or round (inhibitory) arrowheads. In this figure the dilution rate is set to p = 1 for clarity
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Fig2: Synfire chain connectivity. Excitatory neurons (open circles) of pool i are connected to pool i + 1 in a feed-forward manner. Inhibitory neurons (blue filled circles) are projecting globally to the network. Connections are visualized by arrows in the corresponding (black = excitatory, blue = inhibitory) colors with pointed (excitatory) or round (inhibitory) arrowheads. In this figure the dilution rate is set to p = 1 for clarity

Mentions: A natural candidate neural architecture to produce spiking activity which can be associated with a uniform motion is the synfire chain (Abeles 1991). In the simplest formulation of the synfire chain concept, excitatory neurons are grouped in pools and each neuron is connected to all neurons of the following pool creating a chain of convergent and divergent feed-forward connections. If the first group is stimulated with sufficient strength and a sufficiently high degree of synchrony, a wave of synchronous activity propagates along the chain. The propagation along the chain is at constant speed (Wennekers and Palm 1996; Diesmann et al. 1999) and stable under fairly general conditions (Herrmann et al. 1995; Diesmann et al. 1999). As illustrated in Fig. 2, the basic concept of synfire chains has recently been extended by introducing a dilution rate p and globally projecting inhibitory neurons (Abeles et al. 2004; Hayon et al. 2005). Instead of connecting each neuron to all neurons of the following group, connections are drawn with probability p. Additionally, each synfire group comprises not only excitatory neurons making feed-forward connections, but also inhibitory neurons that make random connections to neurons selected from the entire network. The former adaptation has the effect that the propagation of activity can be more easily influenced by outside activity, such as synfire activity binding (Abeles et al. 2004; Hayon et al. 2005). The latter adaptation balances the network and enables control of the global network activity in the presence of synfire activity. The synfire chain connectivity is described in detail in Table 1, the corresponding parameters can be found in Table 3.Fig. 2


Compositionality of arm movements can be realized by propagating synchrony.

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

Synfire chain connectivity. Excitatory neurons (open circles) of pool i are connected to pool i + 1 in a feed-forward manner. Inhibitory neurons (blue filled circles) are projecting globally to the network. Connections are visualized by arrows in the corresponding (black = excitatory, blue = inhibitory) colors with pointed (excitatory) or round (inhibitory) arrowheads. In this figure the dilution rate is set to p = 1 for clarity
© Copyright Policy
Related In: Results  -  Collection

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

Fig2: Synfire chain connectivity. Excitatory neurons (open circles) of pool i are connected to pool i + 1 in a feed-forward manner. Inhibitory neurons (blue filled circles) are projecting globally to the network. Connections are visualized by arrows in the corresponding (black = excitatory, blue = inhibitory) colors with pointed (excitatory) or round (inhibitory) arrowheads. In this figure the dilution rate is set to p = 1 for clarity
Mentions: A natural candidate neural architecture to produce spiking activity which can be associated with a uniform motion is the synfire chain (Abeles 1991). In the simplest formulation of the synfire chain concept, excitatory neurons are grouped in pools and each neuron is connected to all neurons of the following pool creating a chain of convergent and divergent feed-forward connections. If the first group is stimulated with sufficient strength and a sufficiently high degree of synchrony, a wave of synchronous activity propagates along the chain. The propagation along the chain is at constant speed (Wennekers and Palm 1996; Diesmann et al. 1999) and stable under fairly general conditions (Herrmann et al. 1995; Diesmann et al. 1999). As illustrated in Fig. 2, the basic concept of synfire chains has recently been extended by introducing a dilution rate p and globally projecting inhibitory neurons (Abeles et al. 2004; Hayon et al. 2005). Instead of connecting each neuron to all neurons of the following group, connections are drawn with probability p. Additionally, each synfire group comprises not only excitatory neurons making feed-forward connections, but also inhibitory neurons that make random connections to neurons selected from the entire network. The former adaptation has the effect that the propagation of activity can be more easily influenced by outside activity, such as synfire activity binding (Abeles et al. 2004; Hayon et al. 2005). The latter adaptation balances the network and enables control of the global network activity in the presence of synfire activity. The synfire chain connectivity is described in detail in Table 1, the corresponding parameters can be found in Table 3.Fig. 2

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