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A Computational Model of a Descending Mechanosensory Pathway Involved in Active Tactile Sensing.

Ache JM, Dürr V - PLoS Comput. Biol. (2015)

Bottom Line: Finally, we propose a computational framework that can model the response properties of all important DIN types, using the hair field model as its only input.This DIN model is validated by comparison of tuning characteristics, and by mapping the modelled neurons into the two-dimensional coding-space of the real DIN population.This reveals the versatility of the framework for modelling a complete descending neural pathway.

View Article: PubMed Central - PubMed

Affiliation: Department of Biological Cybernetics, Faculty of Biology, Bielefeld University, Bielefeld, Germany; Cognitive Interaction Technology-Center of Excellence, Bielefeld University, Bielefeld, Germany.

ABSTRACT
Many animals, including humans, rely on active tactile sensing to explore the environment and negotiate obstacles, especially in the dark. Here, we model a descending neural pathway that mediates short-latency proprioceptive information from a tactile sensor on the head to thoracic neural networks. We studied the nocturnal stick insect Carausius morosus, a model organism for the study of adaptive locomotion, including tactually mediated reaching movements. Like mammals, insects need to move their tactile sensors for probing the environment. Cues about sensor position and motion are therefore crucial for the spatial localization of tactile contacts and the coordination of fast, adaptive motor responses. Our model explains how proprioceptive information about motion and position of the antennae, the main tactile sensors in insects, can be encoded by a single type of mechanosensory afferents. Moreover, it explains how this information is integrated and mediated to thoracic neural networks by a diverse population of descending interneurons (DINs). First, we quantified responses of a DIN population to changes in antennal position, motion and direction of movement. Using principal component (PC) analysis, we find that only two PCs account for a large fraction of the variance in the DIN response properties. We call the two-dimensional space spanned by these PCs 'coding-space' because it captures essential features of the entire DIN population. Second, we model the mechanoreceptive input elements of this descending pathway, a population of proprioceptive mechanosensory hairs monitoring deflection of the antennal joints. Finally, we propose a computational framework that can model the response properties of all important DIN types, using the hair field model as its only input. This DIN model is validated by comparison of tuning characteristics, and by mapping the modelled neurons into the two-dimensional coding-space of the real DIN population. This reveals the versatility of the framework for modelling a complete descending neural pathway.

No MeSH data available.


Related in: MedlinePlus

Modelled ON- and OFF-type DINs have a similar velocity tuning as their recorded counterparts.Stars show the mean spike rates of modelled ON-type (red, left y-axis) and OFF-type (black, right y-axis) velocity-sensitive DINs at different velocities. The dotted lines represent linear fits to the mean spike rates of two representative recorded DINs. The curvature of the linear fits results from the semi-logarithmic plotting. Grey area is outside the range used for the PCA. Values are means of n = 4 sweeps.
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pcbi.1004263.g008: Modelled ON- and OFF-type DINs have a similar velocity tuning as their recorded counterparts.Stars show the mean spike rates of modelled ON-type (red, left y-axis) and OFF-type (black, right y-axis) velocity-sensitive DINs at different velocities. The dotted lines represent linear fits to the mean spike rates of two representative recorded DINs. The curvature of the linear fits results from the semi-logarithmic plotting. Grey area is outside the range used for the PCA. Values are means of n = 4 sweeps.

Mentions: A) Examples of spike trains recorded from seven representative DINs during stimulation at movement velocities of 40°/s. Blue, simple position-sensitive DINs (dorsal and ventral); red, ON-type velocity-sensitive DIN; green, OFF-type velocity-sensitive DIN; Cyan, dynamic position-sensitive DINs (dorsal, ventral and extreme position). B) Examples of modelled spike trains evoked by the same stimulus. Colours as in A. The modelled spike trains reflected the overall spike distributions and frequencies of the recorded DINs. Note that OFF-type velocity-sensitive DINs were only weakly inhibited at these relatively slow velocities (see also Fig 8).


A Computational Model of a Descending Mechanosensory Pathway Involved in Active Tactile Sensing.

Ache JM, Dürr V - PLoS Comput. Biol. (2015)

Modelled ON- and OFF-type DINs have a similar velocity tuning as their recorded counterparts.Stars show the mean spike rates of modelled ON-type (red, left y-axis) and OFF-type (black, right y-axis) velocity-sensitive DINs at different velocities. The dotted lines represent linear fits to the mean spike rates of two representative recorded DINs. The curvature of the linear fits results from the semi-logarithmic plotting. Grey area is outside the range used for the PCA. Values are means of n = 4 sweeps.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4497639&req=5

pcbi.1004263.g008: Modelled ON- and OFF-type DINs have a similar velocity tuning as their recorded counterparts.Stars show the mean spike rates of modelled ON-type (red, left y-axis) and OFF-type (black, right y-axis) velocity-sensitive DINs at different velocities. The dotted lines represent linear fits to the mean spike rates of two representative recorded DINs. The curvature of the linear fits results from the semi-logarithmic plotting. Grey area is outside the range used for the PCA. Values are means of n = 4 sweeps.
Mentions: A) Examples of spike trains recorded from seven representative DINs during stimulation at movement velocities of 40°/s. Blue, simple position-sensitive DINs (dorsal and ventral); red, ON-type velocity-sensitive DIN; green, OFF-type velocity-sensitive DIN; Cyan, dynamic position-sensitive DINs (dorsal, ventral and extreme position). B) Examples of modelled spike trains evoked by the same stimulus. Colours as in A. The modelled spike trains reflected the overall spike distributions and frequencies of the recorded DINs. Note that OFF-type velocity-sensitive DINs were only weakly inhibited at these relatively slow velocities (see also Fig 8).

Bottom Line: Finally, we propose a computational framework that can model the response properties of all important DIN types, using the hair field model as its only input.This DIN model is validated by comparison of tuning characteristics, and by mapping the modelled neurons into the two-dimensional coding-space of the real DIN population.This reveals the versatility of the framework for modelling a complete descending neural pathway.

View Article: PubMed Central - PubMed

Affiliation: Department of Biological Cybernetics, Faculty of Biology, Bielefeld University, Bielefeld, Germany; Cognitive Interaction Technology-Center of Excellence, Bielefeld University, Bielefeld, Germany.

ABSTRACT
Many animals, including humans, rely on active tactile sensing to explore the environment and negotiate obstacles, especially in the dark. Here, we model a descending neural pathway that mediates short-latency proprioceptive information from a tactile sensor on the head to thoracic neural networks. We studied the nocturnal stick insect Carausius morosus, a model organism for the study of adaptive locomotion, including tactually mediated reaching movements. Like mammals, insects need to move their tactile sensors for probing the environment. Cues about sensor position and motion are therefore crucial for the spatial localization of tactile contacts and the coordination of fast, adaptive motor responses. Our model explains how proprioceptive information about motion and position of the antennae, the main tactile sensors in insects, can be encoded by a single type of mechanosensory afferents. Moreover, it explains how this information is integrated and mediated to thoracic neural networks by a diverse population of descending interneurons (DINs). First, we quantified responses of a DIN population to changes in antennal position, motion and direction of movement. Using principal component (PC) analysis, we find that only two PCs account for a large fraction of the variance in the DIN response properties. We call the two-dimensional space spanned by these PCs 'coding-space' because it captures essential features of the entire DIN population. Second, we model the mechanoreceptive input elements of this descending pathway, a population of proprioceptive mechanosensory hairs monitoring deflection of the antennal joints. Finally, we propose a computational framework that can model the response properties of all important DIN types, using the hair field model as its only input. This DIN model is validated by comparison of tuning characteristics, and by mapping the modelled neurons into the two-dimensional coding-space of the real DIN population. This reveals the versatility of the framework for modelling a complete descending neural pathway.

No MeSH data available.


Related in: MedlinePlus