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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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Sustained activity in the coupled network: (a) Gaussian filtered (σ = 5 ms) firing rate of the SFCN (red) and the BFCN (blue) as functions of time. Activity of 10% of the neurons is recorded. The blue arrow indicates a spontaneous emergence of synfire activity in the BFCN, resulting in a substantial transient increase in firing rate. The red arrow highlights a switching failure in which neither successor chain is activated, causing activity in the SFCN to die out temporarily. (b) Power spectrum of the average Gaussian filtered firing rate between 0 and 10.24 s (vertical black dashed line in (a)). Black arrow indicates peak at around 7 Hz
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Fig15: Sustained activity in the coupled network: (a) Gaussian filtered (σ = 5 ms) firing rate of the SFCN (red) and the BFCN (blue) as functions of time. Activity of 10% of the neurons is recorded. The blue arrow indicates a spontaneous emergence of synfire activity in the BFCN, resulting in a substantial transient increase in firing rate. The red arrow highlights a switching failure in which neither successor chain is activated, causing activity in the SFCN to die out temporarily. (b) Power spectrum of the average Gaussian filtered firing rate between 0 and 10.24 s (vertical black dashed line in (a)). Black arrow indicates peak at around 7 Hz

Mentions: We exploit the self-igniting property of the BFCN to sustain activity in the SFCN described in Section 3.1 and illustrated in Fig. 5. Inhibitory connections from the SFCN suppress the activity of the BFCN when synfire activity is present, as this is characterized by a high firing rate. When no synfire activity is present in the SFCN, it fires in the asynchronous irregular regime at low rate and consequently is not able to suppress the activity in the BFCN. An example is given in Fig. 15(a). In the beginning no synfire activity is present in the SFCN and the BFCN is not suppressed. At around 1.8 s the BFCN self-ignites as described in Section 3.3, leading to a sharp increase in its firing rate. The BFCN activates chain 1 in the SFCN. The subsequent synfire activity suppresses the BFCN activity below the self-ignition threshold. The synfire activity lasts for 8 s before the activity dies away due to a switching error. The inhibitory input to the BFCN decreases as the SFCN firing rate drops, allowing the BFCN activity to rise above the self-ignition threshold again. At around 9.8 s the BFCN self-ignites again, inducing a fresh sequence of SFCN activity. The low frequency signature of the synfire chain competition discussed in Section 3.2 can be clearly seen in the full network as a peak at around 7 Hz in the Fourier transformed firing rate shown in Fig. 15(b). This is the expected frequency because the synfire activity duration in each chain is approximately 140 ms (see Fig. 3).Fig. 15


Compositionality of arm movements can be realized by propagating synchrony.

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

Sustained activity in the coupled network: (a) Gaussian filtered (σ = 5 ms) firing rate of the SFCN (red) and the BFCN (blue) as functions of time. Activity of 10% of the neurons is recorded. The blue arrow indicates a spontaneous emergence of synfire activity in the BFCN, resulting in a substantial transient increase in firing rate. The red arrow highlights a switching failure in which neither successor chain is activated, causing activity in the SFCN to die out temporarily. (b) Power spectrum of the average Gaussian filtered firing rate between 0 and 10.24 s (vertical black dashed line in (a)). Black arrow indicates peak at around 7 Hz
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Related In: Results  -  Collection

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Fig15: Sustained activity in the coupled network: (a) Gaussian filtered (σ = 5 ms) firing rate of the SFCN (red) and the BFCN (blue) as functions of time. Activity of 10% of the neurons is recorded. The blue arrow indicates a spontaneous emergence of synfire activity in the BFCN, resulting in a substantial transient increase in firing rate. The red arrow highlights a switching failure in which neither successor chain is activated, causing activity in the SFCN to die out temporarily. (b) Power spectrum of the average Gaussian filtered firing rate between 0 and 10.24 s (vertical black dashed line in (a)). Black arrow indicates peak at around 7 Hz
Mentions: We exploit the self-igniting property of the BFCN to sustain activity in the SFCN described in Section 3.1 and illustrated in Fig. 5. Inhibitory connections from the SFCN suppress the activity of the BFCN when synfire activity is present, as this is characterized by a high firing rate. When no synfire activity is present in the SFCN, it fires in the asynchronous irregular regime at low rate and consequently is not able to suppress the activity in the BFCN. An example is given in Fig. 15(a). In the beginning no synfire activity is present in the SFCN and the BFCN is not suppressed. At around 1.8 s the BFCN self-ignites as described in Section 3.3, leading to a sharp increase in its firing rate. The BFCN activates chain 1 in the SFCN. The subsequent synfire activity suppresses the BFCN activity below the self-ignition threshold. The synfire activity lasts for 8 s before the activity dies away due to a switching error. The inhibitory input to the BFCN decreases as the SFCN firing rate drops, allowing the BFCN activity to rise above the self-ignition threshold again. At around 9.8 s the BFCN self-ignites again, inducing a fresh sequence of SFCN activity. The low frequency signature of the synfire chain competition discussed in Section 3.2 can be clearly seen in the full network as a peak at around 7 Hz in the Fourier transformed firing rate shown in Fig. 15(b). This is the expected frequency because the synfire activity duration in each chain is approximately 140 ms (see Fig. 3).Fig. 15

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