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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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Equi-affine analysis of the generated trajectory. (a) Section of spike data shown in Fig. 16(a). Vertical dashed lines indicate the extent of individual synfire chain activities. (b) First section of the scribbling trajectory shown in Fig. 16(c), generated between 1.7 s and 2.7 s. (b) Velocity of the generated trajectory in the x (black) and y (gray) directions as functions of time. (c) Acceleration of the generated trajectory in the x (black) and y (gray) directions as functions of time. (d) Equi-affine curvature κ of the generated trajectory as a function of time. Colored vertical lines indicate the start of the corresponding parabolic primitive
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Fig17: Equi-affine analysis of the generated trajectory. (a) Section of spike data shown in Fig. 16(a). Vertical dashed lines indicate the extent of individual synfire chain activities. (b) First section of the scribbling trajectory shown in Fig. 16(c), generated between 1.7 s and 2.7 s. (b) Velocity of the generated trajectory in the x (black) and y (gray) directions as functions of time. (c) Acceleration of the generated trajectory in the x (black) and y (gray) directions as functions of time. (d) Equi-affine curvature κ of the generated trajectory as a function of time. Colored vertical lines indicate the start of the corresponding parabolic primitive

Mentions: We analyze the characteristics of the trajectory shown in Fig. 16(c). In Fig. 17(b) the first part of the trajectory is displayed in the colors of the most active synfire chain as calculated from the spiking activity in Fig. 17(a). For increased clarity in Fig. 17(c–e), colored vertical lines indicate transitions to the corresponding parabolic segments as identified in Fig. 17(b). The velocity of the trajectory in the x and y directions is extracted from the spiking activity by calculating the population coding in 2 ms bins and smoothing with a Gaussian kernel with standard deviation σ = 10 ms. Figure 17(c) shows that the velocities vary approximately linearly during the activity time of a given chain. This can also be seen by considering the accelerations in the x and y directions, which are calculated using the finite difference method between successive sample points of the velocities and shown in Fig. 17(d). The accelerations are approximately constant during the activity time of a given chain. Due to the piece-wise constant accelerations, following the derivation in Section 2.1 we conclude that the trajectory does indeed fulfil the two-thirds power law (e.g. Viviani and Flash 1995). Figure 17(e) shows that the equi-affine curvature of the trajectory is close to zero (see Section 2.5 for details of analysis). We therefore conclude that the trajectory does indeed consist of a series of parabolic segments.Fig. 17


Compositionality of arm movements can be realized by propagating synchrony.

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

Equi-affine analysis of the generated trajectory. (a) Section of spike data shown in Fig. 16(a). Vertical dashed lines indicate the extent of individual synfire chain activities. (b) First section of the scribbling trajectory shown in Fig. 16(c), generated between 1.7 s and 2.7 s. (b) Velocity of the generated trajectory in the x (black) and y (gray) directions as functions of time. (c) Acceleration of the generated trajectory in the x (black) and y (gray) directions as functions of time. (d) Equi-affine curvature κ of the generated trajectory as a function of time. Colored vertical lines indicate the start of the corresponding parabolic primitive
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Fig17: Equi-affine analysis of the generated trajectory. (a) Section of spike data shown in Fig. 16(a). Vertical dashed lines indicate the extent of individual synfire chain activities. (b) First section of the scribbling trajectory shown in Fig. 16(c), generated between 1.7 s and 2.7 s. (b) Velocity of the generated trajectory in the x (black) and y (gray) directions as functions of time. (c) Acceleration of the generated trajectory in the x (black) and y (gray) directions as functions of time. (d) Equi-affine curvature κ of the generated trajectory as a function of time. Colored vertical lines indicate the start of the corresponding parabolic primitive
Mentions: We analyze the characteristics of the trajectory shown in Fig. 16(c). In Fig. 17(b) the first part of the trajectory is displayed in the colors of the most active synfire chain as calculated from the spiking activity in Fig. 17(a). For increased clarity in Fig. 17(c–e), colored vertical lines indicate transitions to the corresponding parabolic segments as identified in Fig. 17(b). The velocity of the trajectory in the x and y directions is extracted from the spiking activity by calculating the population coding in 2 ms bins and smoothing with a Gaussian kernel with standard deviation σ = 10 ms. Figure 17(c) shows that the velocities vary approximately linearly during the activity time of a given chain. This can also be seen by considering the accelerations in the x and y directions, which are calculated using the finite difference method between successive sample points of the velocities and shown in Fig. 17(d). The accelerations are approximately constant during the activity time of a given chain. Due to the piece-wise constant accelerations, following the derivation in Section 2.1 we conclude that the trajectory does indeed fulfil the two-thirds power law (e.g. Viviani and Flash 1995). Figure 17(e) shows that the equi-affine curvature of the trajectory is close to zero (see Section 2.5 for details of analysis). We therefore conclude that the trajectory does indeed consist of a series of parabolic segments.Fig. 17

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