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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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Architecture of the network model. Two networks are coupled with an inhibitory/excitatory reciprocal connection. Excitatory connections are shown as black pointed arrows, inhibitory connections as blue rounded arrows. The synfire chain network (SFCN) consists of 10 synfire chains connected into a directed graph as shown in Fig. 4. The backward-and-forward connected network (BFCN) consists of a single backward-and-forward connected chain. Both networks are driven by an independent excitatory Poisson input to each neuron with rate νx = 7.7 kHz
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Fig5: Architecture of the network model. Two networks are coupled with an inhibitory/excitatory reciprocal connection. Excitatory connections are shown as black pointed arrows, inhibitory connections as blue rounded arrows. The synfire chain network (SFCN) consists of 10 synfire chains connected into a directed graph as shown in Fig. 4. The backward-and-forward connected network (BFCN) consists of a single backward-and-forward connected chain. Both networks are driven by an independent excitatory Poisson input to each neuron with rate νx = 7.7 kHz

Mentions: We develop a spiking network model to realize a generator of random trajectories consisting of parabolic segments. Our model comprises two interconnected networks as shown in Fig. 5. The synfire chain network (SFCN) consists of ten chains, each chain corresponding to one of the arrows in velocity space shown in Fig. 4 and thus encoding a parabolic segment. Each chain consists of 80% excitatory neurons that make feed-forward connections with dilution factor p = 0.75 and 20% inhibitory neurons making kg random connections to other neurons in SFCN. To distinguish the random inhibitory connections from other connectivity patterns, we will refer to kg as the global inhibition parameter. The graph vertices specified in Fig. 4 are realized by feed-forward connections from the final group of each chain to the initial groups of two other chains, e.g. the final group of chain 1 has feed-forward connections to the initial groups of chains 2 and 7. The preferred velocity of the last group of a chain is the same as the first groups of the chains it connects to in order to generate trajectories that are smooth at the transition points. Reliable switching at the transition points is enabled by mutual inhibition between potential successor chains; this is discussed in detail in Section 3.2.Fig. 5


Compositionality of arm movements can be realized by propagating synchrony.

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

Architecture of the network model. Two networks are coupled with an inhibitory/excitatory reciprocal connection. Excitatory connections are shown as black pointed arrows, inhibitory connections as blue rounded arrows. The synfire chain network (SFCN) consists of 10 synfire chains connected into a directed graph as shown in Fig. 4. The backward-and-forward connected network (BFCN) consists of a single backward-and-forward connected chain. Both networks are driven by an independent excitatory Poisson input to each neuron with rate νx = 7.7 kHz
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3108016&req=5

Fig5: Architecture of the network model. Two networks are coupled with an inhibitory/excitatory reciprocal connection. Excitatory connections are shown as black pointed arrows, inhibitory connections as blue rounded arrows. The synfire chain network (SFCN) consists of 10 synfire chains connected into a directed graph as shown in Fig. 4. The backward-and-forward connected network (BFCN) consists of a single backward-and-forward connected chain. Both networks are driven by an independent excitatory Poisson input to each neuron with rate νx = 7.7 kHz
Mentions: We develop a spiking network model to realize a generator of random trajectories consisting of parabolic segments. Our model comprises two interconnected networks as shown in Fig. 5. The synfire chain network (SFCN) consists of ten chains, each chain corresponding to one of the arrows in velocity space shown in Fig. 4 and thus encoding a parabolic segment. Each chain consists of 80% excitatory neurons that make feed-forward connections with dilution factor p = 0.75 and 20% inhibitory neurons making kg random connections to other neurons in SFCN. To distinguish the random inhibitory connections from other connectivity patterns, we will refer to kg as the global inhibition parameter. The graph vertices specified in Fig. 4 are realized by feed-forward connections from the final group of each chain to the initial groups of two other chains, e.g. the final group of chain 1 has feed-forward connections to the initial groups of chains 2 and 7. The preferred velocity of the last group of a chain is the same as the first groups of the chains it connects to in order to generate trajectories that are smooth at the transition points. Reliable switching at the transition points is enabled by mutual inhibition between potential successor chains; this is discussed in detail in Section 3.2.Fig. 5

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