Neurodynamic analysis of Merkel cell–neurite complex transduction mechanism during tactile sensing

Yao, Mengqiu; Wang, Rubin

doi:10.1007/s11571-018-9507-z

Cited by 17 publications

(17 citation statements)

References 40 publications

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“…Synaptic functions of AiS-TSO. There is still active research about structural and phenomenological observations on MCNCs 11,23,24,50 including the relationship between the number and spatial density of MCNCs and SA perception 51 . Although the exact mechanism of SA firing in MCNCs has not been discovered, it is obvious that there are complex interactions between mechanosensitive Piezo-2 channels, cell membrane potentials, and synergistic synapses of MCNCs that allow Merkel cells to initiate Aβ afferent pulses to encode tactile information 9,22,52 .…”

Section: Gatementioning

confidence: 99%

A flexible artificial intrinsic-synaptic tactile sensory organ

et al. 2020

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Imbuing bio-inspired sensory devices with intelligent functions of human sensory organs has been limited by challenges in emulating the preprocessing abilities of sensory organs such as reception, filtering, adaptation, and sensory memory at the device level itself. Merkel cells, which is a part of tactile sensory organs, form synapse-like connections with afferent neuron terminals referred to as Merkel cell-neurite complexes. Here, inspired by structure and intelligent functions of Merkel cell-neurite complexes, we report a flexible, artificial, intrinsicsynaptic tactile sensory organ that mimics synapse-like connections using an organic synaptic transistor with ferroelectric nanocomposite gate dielectric of barium titanate nanoparticles and poly(vinylidene fluoride-trifluoroethylene). Modulation of the post-synaptic current of the device induced by ferroelectric dipole switching due to triboelectric-capacitive coupling under finger touch allowed reception and slow adaptation. Modulation of synaptic weight by varying the nanocomposite composition of gate dielectric layer enabled tuning of filtering and sensory memory functions.

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Section: Gatementioning

confidence: 99%

A flexible artificial intrinsic-synaptic tactile sensory organ

et al. 2020

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“…Although some very simple numerical models of tissue specimens have already been developed, they cannot study the whole process of human sensory-motor control. 15,38,39,47 This FE hand model has the potential to provide real-time neural spike information during active touch and other manipulation processes. The mechanical parameter which is related to human tactile perception such as strain energy density at the locations of mechanoreceptors can be derived to predict the neural signal.…”

Section: Discussionmentioning

confidence: 99%

“…When an object is sensed or manipulated, neural impulse signals are generated at the mechanoreceptors located underneath the epidermis according to the shape and texture of the object. 22,43,47 These mechanoreceptors receive and encode the mechanical information such as strain or strain energy density (SED) and send the encoded information to somatosensory cortex. 14,38 However, these mechanical parameters such as the SED are not measurable and observable with current technology.…”

Section: Introductionmentioning

confidence: 99%

Subject-Specific Finite Element Modelling of the Human Hand Complex: Muscle-Driven Simulations and Experimental Validation

et al. 2019

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This paper aims to develop and validate a subjectspecific framework for modelling the human hand. This was achieved by combining medical image-based finite element modelling, individualized muscle force and kinematic measurements. Firstly, a subject-specific human hand finite element (FE) model was developed. The geometries of the phalanges, carpal bones, wrist bones, ligaments, tendons, subcutaneous tissue and skin were all included. The material properties were derived from in-vivo and in-vitro experiment results available in the literature. The boundary and loading conditions were defined based on the kinematic data and muscle forces of a specific subject captured from the in-vivo grasping tests. The predicted contact pressure and contact area were in good agreement with the in-vivo test results of the same subject, with the relative errors for the contact pressures all being below 20%. Finally, sensitivity analysis was performed to investigate the effects of important modelling parameters on the predictions. The results showed that contact pressure and area were sensitive to the material properties and muscle forces. This FE human hand model can be used to make a detailed and quantitative evaluation into biomechanical and neurophysiological aspects of human hand contact during daily perception and manipulation. The findings can be applied to the design of the bionic hands or neuro-prosthetics in the future.

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“…This assumption makes Eq. 5, (6), and (8) evaluated closing the integral contour in the complex η plane [21,…”

Section: The Network Modelmentioning

confidence: 99%

“…The dynamics of these dynamical systems provide theoretical insight into the biological mechanisms of brain functions. In recent decades, lots of neuron models have been intensively studied, and their equilibria and bifurcations are used to explain the formation and transition modes of the neuronal firing patterns [1][2][3][4][5][6]. However, because of the high dimension and complexity, the large-scale network models which consist of many neurons and synapses are difficult to study by dynamical methods [7].…”

Section: Introductionmentioning

confidence: 99%

Dynamics of a Large-Scale Spiking Neural Network with Quadratic Integrate-and-Fire Neurons

2021

Neural Plasticity

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Since the high dimension and complexity of the large-scale spiking neural network, it is difficult to research the network dynamics. In recent decades, the mean-field approximation has been a useful method to reduce the dimension of the network. In this study, we construct a large-scale spiking neural network with quadratic integrate-and-fire neurons and reduce it to a mean-field model to research the network dynamics. We find that the activity of the mean-field model is consistent with the network activity. Based on this agreement, a two-parameter bifurcation analysis is performed on the mean-field model to understand the network dynamics. The bifurcation scenario indicates that the network model has the quiescence state, the steady state with a relatively high firing rate, and the synchronization state which correspond to the stable node, stable focus, and stable limit cycle of the system, respectively. There exist several stable limit cycles with different periods, so we can observe the synchronization states with different periods. Additionally, the model shows bistability in some regions of the bifurcation diagram which suggests that two different activities coexist in the network. The mechanisms that how these states switch are also indicated by the bifurcation curves.

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Neurodynamic analysis of Merkel cell–neurite complex transduction mechanism during tactile sensing

Cited by 17 publications

References 40 publications

A flexible artificial intrinsic-synaptic tactile sensory organ

A flexible artificial intrinsic-synaptic tactile sensory organ

Subject-Specific Finite Element Modelling of the Human Hand Complex: Muscle-Driven Simulations and Experimental Validation

Dynamics of a Large-Scale Spiking Neural Network with Quadratic Integrate-and-Fire Neurons

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