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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

A computational model for different DIN types.Four groups of DINs can be modelled in the same computational framework, using only two modelled hair fields (see Fig 3) as input elements. Modelled DINs integrate afferent spike trains from both hair fields by use of linear low-pass filters (LPF) and weighted summation of the two input streams. In the model variant for simple position-sensitive DINs, represented by the left, blue branch, the weighted sum of the two input streams is fed directly into the spike generator (spike). The right branches show the model variant for OFF-type (green), ON-type (red), and dynamic position-sensitive DINs (cyan). This branch contains additional linear filters, subtraction of an offset and a group-specific rectification (abs., see Table 2 and text). Parameters that were varied to model different DIN types are shown in black. Wd: weight of dorsal hair field input; Wv: weight of ventral hair field input; +, addition, Offset: offset used to shift the DIN activation. LPF: low pass filter; HPF: high pass filter. Tau and w describe the time constant and the weight of the respective filters. The parameter settings are given in Table 2.
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pcbi.1004263.g006: A computational model for different DIN types.Four groups of DINs can be modelled in the same computational framework, using only two modelled hair fields (see Fig 3) as input elements. Modelled DINs integrate afferent spike trains from both hair fields by use of linear low-pass filters (LPF) and weighted summation of the two input streams. In the model variant for simple position-sensitive DINs, represented by the left, blue branch, the weighted sum of the two input streams is fed directly into the spike generator (spike). The right branches show the model variant for OFF-type (green), ON-type (red), and dynamic position-sensitive DINs (cyan). This branch contains additional linear filters, subtraction of an offset and a group-specific rectification (abs., see Table 2 and text). Parameters that were varied to model different DIN types are shown in black. Wd: weight of dorsal hair field input; Wv: weight of ventral hair field input; +, addition, Offset: offset used to shift the DIN activation. LPF: low pass filter; HPF: high pass filter. Tau and w describe the time constant and the weight of the respective filters. The parameter settings are given in Table 2.

Mentions: The schematics at the top show the basic concept of the model. Individual sensilla are placed in a hair row along the long axis of the pedicellar base, and are deflected once the flagellum moves. Each sensillum can be deflected (right). The velocity and amplitude of the deflection depends on angle and movement of the Sc-Pd joint. As the Sc-Pd joint angle changes, individual hairs are deflected according to their position within the hair field (Hair deflection). The hair angle is low- and high-pass filtered (LPF, HPF), and the two resulting filtered traces are subsequently added (+). This yields the activation function, which is an approximation of the membrane potential of each hair field afferent. After normalization (norm.) and subtraction of an offset, this activation function is fed into a noisy spike generator (see Material and Methods), which transforms the continuous activation function into discrete spike trains. This is done in parallel for all hair field afferents (grey boxes in background), generating spike trains for all afferents (Hair field). Ultimately, these spike trains are used as the input to a DIN model (see Fig 6). N: number of hairs per hair row; range: sensitivity range of each hair; deflection: characteristic describing the dependence of hair angle on joint angle; LPF: low pass filter; HPF: high pass filter; tau: time constant of linear filter (LPF or HPF); w: weight factor; Offset: constant that is subtracted from the activation function; Rmax: maximal spike rate of the afferent; dS: sampling interval (1 ms). Norm., normalization used to scale the activation function. Grey boxes highlight example time-courses. The parameter settings are given in Table 1. See main text for further details.


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

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

A computational model for different DIN types.Four groups of DINs can be modelled in the same computational framework, using only two modelled hair fields (see Fig 3) as input elements. Modelled DINs integrate afferent spike trains from both hair fields by use of linear low-pass filters (LPF) and weighted summation of the two input streams. In the model variant for simple position-sensitive DINs, represented by the left, blue branch, the weighted sum of the two input streams is fed directly into the spike generator (spike). The right branches show the model variant for OFF-type (green), ON-type (red), and dynamic position-sensitive DINs (cyan). This branch contains additional linear filters, subtraction of an offset and a group-specific rectification (abs., see Table 2 and text). Parameters that were varied to model different DIN types are shown in black. Wd: weight of dorsal hair field input; Wv: weight of ventral hair field input; +, addition, Offset: offset used to shift the DIN activation. LPF: low pass filter; HPF: high pass filter. Tau and w describe the time constant and the weight of the respective filters. The parameter settings are given in Table 2.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4497639&req=5

pcbi.1004263.g006: A computational model for different DIN types.Four groups of DINs can be modelled in the same computational framework, using only two modelled hair fields (see Fig 3) as input elements. Modelled DINs integrate afferent spike trains from both hair fields by use of linear low-pass filters (LPF) and weighted summation of the two input streams. In the model variant for simple position-sensitive DINs, represented by the left, blue branch, the weighted sum of the two input streams is fed directly into the spike generator (spike). The right branches show the model variant for OFF-type (green), ON-type (red), and dynamic position-sensitive DINs (cyan). This branch contains additional linear filters, subtraction of an offset and a group-specific rectification (abs., see Table 2 and text). Parameters that were varied to model different DIN types are shown in black. Wd: weight of dorsal hair field input; Wv: weight of ventral hair field input; +, addition, Offset: offset used to shift the DIN activation. LPF: low pass filter; HPF: high pass filter. Tau and w describe the time constant and the weight of the respective filters. The parameter settings are given in Table 2.
Mentions: The schematics at the top show the basic concept of the model. Individual sensilla are placed in a hair row along the long axis of the pedicellar base, and are deflected once the flagellum moves. Each sensillum can be deflected (right). The velocity and amplitude of the deflection depends on angle and movement of the Sc-Pd joint. As the Sc-Pd joint angle changes, individual hairs are deflected according to their position within the hair field (Hair deflection). The hair angle is low- and high-pass filtered (LPF, HPF), and the two resulting filtered traces are subsequently added (+). This yields the activation function, which is an approximation of the membrane potential of each hair field afferent. After normalization (norm.) and subtraction of an offset, this activation function is fed into a noisy spike generator (see Material and Methods), which transforms the continuous activation function into discrete spike trains. This is done in parallel for all hair field afferents (grey boxes in background), generating spike trains for all afferents (Hair field). Ultimately, these spike trains are used as the input to a DIN model (see Fig 6). N: number of hairs per hair row; range: sensitivity range of each hair; deflection: characteristic describing the dependence of hair angle on joint angle; LPF: low pass filter; HPF: high pass filter; tau: time constant of linear filter (LPF or HPF); w: weight factor; Offset: constant that is subtracted from the activation function; Rmax: maximal spike rate of the afferent; dS: sampling interval (1 ms). Norm., normalization used to scale the activation function. Grey boxes highlight example time-courses. The parameter settings are given in Table 1. See main text for further details.

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