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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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Total probability of switching errors as a function of the number of successor chains. (a) Total probability of switching error for two potential successor chains assuming structured cross-inhibition as a function of the number of inhibitory cross connections kc and number of global inhibitory connections kg. The plot shows the summed probabilities of Fig. 8(a) (probability of activating more than one successor chain) and Fig. 8(b) (probability of activating no successor chain). The red cross marks the parameter set kc = kg = 7. (b) As for (a) for the case of unstructured cross-inhibition; the red cross marks the parameter set kc = 19 and kg = 7. (c) As for (b) but for three potential successor chains. (d) As for (b) but for four potential successor chains; the red cross marks kc = 25 and kg = 7. (e) Conditional probability of switching to chain 2 as a function of the priming strength Jprim for kc = 25 and kg = 7 (blue squares; sigmoidal fit to the data, blue curve). Probability of activating no successor chains (grey triangles), probability of activating multiple successor chains (black triangles) and total switching errors (red triangles). Dotted lines indicate chance level, i.e. a 25% probability of activating chain 2. (f) As for (d) but with excitatory priming Jprim = 3 pA to chain 2
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Fig12: Total probability of switching errors as a function of the number of successor chains. (a) Total probability of switching error for two potential successor chains assuming structured cross-inhibition as a function of the number of inhibitory cross connections kc and number of global inhibitory connections kg. The plot shows the summed probabilities of Fig. 8(a) (probability of activating more than one successor chain) and Fig. 8(b) (probability of activating no successor chain). The red cross marks the parameter set kc = kg = 7. (b) As for (a) for the case of unstructured cross-inhibition; the red cross marks the parameter set kc = 19 and kg = 7. (c) As for (b) but for three potential successor chains. (d) As for (b) but for four potential successor chains; the red cross marks kc = 25 and kg = 7. (e) Conditional probability of switching to chain 2 as a function of the priming strength Jprim for kc = 25 and kg = 7 (blue squares; sigmoidal fit to the data, blue curve). Probability of activating no successor chains (grey triangles), probability of activating multiple successor chains (black triangles) and total switching errors (red triangles). Dotted lines indicate chance level, i.e. a 25% probability of activating chain 2. (f) As for (d) but with excitatory priming Jprim = 3 pA to chain 2

Mentions: To generate a series of parabolic segments, it is necessary that the network enables reliable switching from one chain to exactly one of its successor chains. The robust winner-takes-all mechanism proposed by Chang and Jin (2009) cannot be realized, as it depends on dominant global inhibition, whereas our network operates in the asynchronous irregular regime which entails a balance of excitation and inhibition (Brunel 2000). However, in Sections 3.2.1 and 3.2.2 we showed that switching to one of two possible successor chains is reliable if the inhibition strength and connectivity are chosen appropriately. Here we investigate whether competition by mutual inhibition can be extended to the case of more than two successor chains. Figure 12(a, b) shows the total error probabilities for structured and unstructured cross-inhibition when switching to one of two successor chains, i.e. the sum of the number of trials in which more than one successor chain was activated and the number of trials in which no successor chain was activated. The working regime for structured cross-inhibition is much smaller than that for unstructured cross-inhibition. We therefore investigate the total error probability for three and four successor chains assuming unstructured cross-inhibition, see Fig. 12(c, d). The working regime shrinks with increasing number of successor chains.Fig. 12


Compositionality of arm movements can be realized by propagating synchrony.

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

Total probability of switching errors as a function of the number of successor chains. (a) Total probability of switching error for two potential successor chains assuming structured cross-inhibition as a function of the number of inhibitory cross connections kc and number of global inhibitory connections kg. The plot shows the summed probabilities of Fig. 8(a) (probability of activating more than one successor chain) and Fig. 8(b) (probability of activating no successor chain). The red cross marks the parameter set kc = kg = 7. (b) As for (a) for the case of unstructured cross-inhibition; the red cross marks the parameter set kc = 19 and kg = 7. (c) As for (b) but for three potential successor chains. (d) As for (b) but for four potential successor chains; the red cross marks kc = 25 and kg = 7. (e) Conditional probability of switching to chain 2 as a function of the priming strength Jprim for kc = 25 and kg = 7 (blue squares; sigmoidal fit to the data, blue curve). Probability of activating no successor chains (grey triangles), probability of activating multiple successor chains (black triangles) and total switching errors (red triangles). Dotted lines indicate chance level, i.e. a 25% probability of activating chain 2. (f) As for (d) but with excitatory priming Jprim = 3 pA to chain 2
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Fig12: Total probability of switching errors as a function of the number of successor chains. (a) Total probability of switching error for two potential successor chains assuming structured cross-inhibition as a function of the number of inhibitory cross connections kc and number of global inhibitory connections kg. The plot shows the summed probabilities of Fig. 8(a) (probability of activating more than one successor chain) and Fig. 8(b) (probability of activating no successor chain). The red cross marks the parameter set kc = kg = 7. (b) As for (a) for the case of unstructured cross-inhibition; the red cross marks the parameter set kc = 19 and kg = 7. (c) As for (b) but for three potential successor chains. (d) As for (b) but for four potential successor chains; the red cross marks kc = 25 and kg = 7. (e) Conditional probability of switching to chain 2 as a function of the priming strength Jprim for kc = 25 and kg = 7 (blue squares; sigmoidal fit to the data, blue curve). Probability of activating no successor chains (grey triangles), probability of activating multiple successor chains (black triangles) and total switching errors (red triangles). Dotted lines indicate chance level, i.e. a 25% probability of activating chain 2. (f) As for (d) but with excitatory priming Jprim = 3 pA to chain 2
Mentions: To generate a series of parabolic segments, it is necessary that the network enables reliable switching from one chain to exactly one of its successor chains. The robust winner-takes-all mechanism proposed by Chang and Jin (2009) cannot be realized, as it depends on dominant global inhibition, whereas our network operates in the asynchronous irregular regime which entails a balance of excitation and inhibition (Brunel 2000). However, in Sections 3.2.1 and 3.2.2 we showed that switching to one of two possible successor chains is reliable if the inhibition strength and connectivity are chosen appropriately. Here we investigate whether competition by mutual inhibition can be extended to the case of more than two successor chains. Figure 12(a, b) shows the total error probabilities for structured and unstructured cross-inhibition when switching to one of two successor chains, i.e. the sum of the number of trials in which more than one successor chain was activated and the number of trials in which no successor chain was activated. The working regime for structured cross-inhibition is much smaller than that for unstructured cross-inhibition. We therefore investigate the total error probability for three and four successor chains assuming unstructured cross-inhibition, see Fig. 12(c, d). The working regime shrinks with increasing number of successor chains.Fig. 12

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