A simple model of mechanotransduction in primate glabrous skin
Open accessAbstract
Tactile stimulation of the hand evokes highly precise and repeatable patterns of activity in mechanoreceptive afferents; the strength (i.e., firing rate) and timing of these responses have been shown to convey stimulus information. To achieve an understanding of the mechanisms underlying the representation of tactile stimuli in the nerve, we developed a two-stage computational model consisting of a nonlinear mechanical transduction stage followed by a generalized integrate-and-fire mechanism. The model improves upon a recently published counterpart in two important ways. First, complexity is dramatically reduced (at least one order of magnitude fewer parameters). Second, the model comprises a saturating nonlinearity and therefore can be applied to a much wider range of stimuli. We show that both the rate and timing of afferent responses are predicted with remarkable precision and that observed adaptation patterns and threshold behavior are well captured. We conclude that the responses of mechanoreceptive afferents can be understood using a very parsimonious mechanistic model, which can then be used to accurately simulate the responses of afferent populations.
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The combined population response is determined not only by contact pressure but also by its first and second derivatives.
“The combined population response is thus determined not only by contact pressure but also by its first and second derivatives (Dong et al., 2013;Kim et al., 2010).”
From Biomimetic approaches to bionic touch through a peripheral nerve interface (2015) · cites this paper as Dong et al., 2013
Materials and Methods models responses using indentation displacement and its first and second derivatives; Results shows that their contributions differ across afferent types. The paper does not establish the same relationship for contact pressure or explicitly model a combined population response. Page numbers are unavailable in our copy. (LLM, conf=0.86)
Experimental — accuracy not yet measuredThe responses of SA1 afferents roughly track contact pressure, while those of RA and PC afferents track changes in pressure.
“While the responses of SA1 afferents roughly track contact pressure, those of RA and PC afferents track changes in pressure.”
From Biomimetic approaches to bionic touch through a peripheral nerve interface (2015) · cites this paper as Dong et al., 2013
The Results (page not provided) say SA afferents are primarily driven by indentation displacement, RA afferents by its velocity, and PC afferents strongly by acceleration. This supports the broad distinction between sustained deformation and changing stimulation, but the paper describes indentation and skin deformation, not contact pressure or changes in pressure. (LLM, conf=0.87)
Experimental — accuracy not yet measuredThe peripheral response to an indented object is not just proportional to the indentation depth; RA and PC afferents also respond to the first two derivatives of time-varying depth.
“The peripheral response to an indented object is not just proportional to the indentation depth as RA and PC afferents also respond to the first two derivatives of time-varying depth (Dong et al., 2013;Kim et al., 2010).”
From Biomimetic approaches to bionic touch through a peripheral nerve interface (2015) · cites this paper as Dong et al., 2013
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Cites
Log-normal distributions across the sciences: keys and clues
LIMPERT, E., STAHEL, W. A., & ABBT, M. (2001) · BioScience · doi:10.1641/0006-3568(2001)051[0341:lndats]2.0.co;2 · Cited by 2,621 · see citing works
Kleitman, N., & Eckenhoff, J. E. (1964) · The American Journal of the Medical Sciences · doi:10.1097/00000441-196502000-00024 · Cited by 1,079 · see citing works
The roles and functions of cutaneous mechanoreceptors.
Johnson, K. (2001) · Current Opinion in Neurobiology · doi:10.1016/s0959-4388(00)00234-8 · Cited by 1,063 · see citing works
Talbot, W. H., Darian-Smith, I., Kornhuber, H. H., & Mountcastle, V. B. (1968) · Journal of Neurophysiology · doi:10.1152/jn.1968.31.2.301 · Cited by 993 · see citing works
First spikes in ensembles of human tactile afferents code complex spatial fingertip events
Johansson, R. S., & Birznieks, I. (2004) · Nature Neuroscience · doi:10.1038/nn1177 · Cited by 637 · see citing works
van Rossum, M. C. W. (2001) · Neural Computation · doi:10.1162/089976601300014321 · Cited by 526 · see citing works
Reduction of the Hodgkin-Huxley Equations to a Single-Variable Threshold Model
Kistler, W. M., Gerstner, W., & van Hemmen, J. L. (1997) · Neural Computation · doi:10.1162/neco.1997.9.5.1015 · Cited by 326 · see citing works
Maximum Likelihood Estimation of a Stochastic Integrate-and-Fire Neural Encoding Model
Paninski, L., Pillow, J. W., & Simoncelli, E. P. (2004) · Neural Computation · doi:10.1162/0899766042321797 · Cited by 270 · see citing works
Muniak, M. A., Ray, S., Hsiao, S. S., Dammann, J. F., & Bensmaia, S. J. (2007) · The Journal of Neuroscience · doi:10.1523/jneurosci.1486-07.2007 · Cited by 235 · cited 3× · see citing works
“Note that gluing the probe to the skin has little effect on the perception of these vibrations (Muniak et al. 2007).”
BackgroundMethodInfluentialJohnson, K. O. (1974) · Journal of Neurophysiology · doi:10.1152/jn.1974.37.1.48 · Cited by 230 · see citing works
Millisecond precision spike timing shapes tactile perception
Mackevicius, E. L., Best, M. D., Saal, H. P., & Bensmaia, S. J. (2012) · The Journal of Neuroscience · doi:10.1523/jneurosci.2161-12.2012 · Cited by 171 · see citing works
A Generalized Linear Integrate-and-Fire Neural Model Produces Diverse Spiking Behaviors
Mihalaş, Ş., & Niebur, E. (2008) · Neural Computation · doi:10.1162/neco.2008.12-07-680 · Cited by 159 · see citing works
The spontaneous activity of neurones in the cat’s cerebral cortex
Burns, F. R. S. B. D., & Webb, A. C. (1976) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.1976.0074 · Cited by 157 · see citing works
Paré, M., Smith, A. M., & Rice, F. L. (2002) · The Journal of Comparative Neurology · doi:10.1002/cne.10196 · Cited by 152 · see citing works
Vibratory Adaptation of Cutaneous Mechanoreceptive Afferents
Bensmaı̈a, S. J., Leung, Y. Y., Hsiao, S. S., & Johnson, K. O. (2005) · Journal of Neurophysiology · doi:10.1152/jn.00002.2005 · Cited by 147 · see citing works
A benchmark test for a quantitative assessment of simple neuron models
Jolivet, R., Kobayashi, R., Rauch, A., Naud, R., Shinomoto, S., & Gerstner, W. (2007) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2007.11.006 · Cited by 144 · see citing works
Freeman, A. W., & Johnson, K. O. (1982) · The Journal of Physiology · doi:10.1113/jphysiol.1982.sp014060 · Cited by 143 · see citing works
Vega‐Bermudez, F., & Johnson, K. O. (1999) · Journal of Neurophysiology · doi:10.1152/jn.1999.81.6.2701 · Cited by 126 · see citing works
Time-Course of Vibratory Adaptation and Recovery in Cutaneous Mechanoreceptive Afferents
Leung, Y. Y., Bensmaı̈a, S. J., Hsiao, S. S., & Johnson, K. O. (2005) · Journal of Neurophysiology · doi:10.1152/jn.00001.2005 · Cited by 107 · see citing works
Levine, M. W. (1991) · Biological Cybernetics · doi:10.1007/bf00204659 · Cited by 75 · see citing works
Predicting the timing of spikes evoked by tactile stimulation of the hand
Kim, S. S., Sripati, A. P., & Bensmaia, S. J. (2010) · Journal of Neurophysiology · doi:10.1152/jn.00187.2010 · Cited by 71 · see citing works
Information about Complex Fingertip Parameters in Individual Human Tactile Afferent Neurons
Saal, H. P., Vijayakumar, S., & Johansson, R. S. (2009) · Journal of Neuroscience · doi:10.1523/jneurosci.0665-09.2009 · Cited by 69 · see citing works
A log-domain implementation of the Mihalas-Niebur neuron model
André, V. S., Jin, C. T., McEwan, A. L., Hamilton, T. J., Mihalaş, Ş., & Niebur, E. (2010) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas.2010.5537563 · Cited by 51 · see citing works
Optimization Methods for Spiking Neurons and Networks
Russell, A., Orchard, G., Dong, Y., Mihalaş, Ş., Niebur, E., Tapson, J. C., & Etienne‐Cummings, R. (2010) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2010.2083685 · Cited by 51 · see citing works
Kim, S. S., Sripati, A. P., Vogelstein, R. J., Armiger, R. S., Russell, A. F., & Bensmaı̈a, S. J. (2009) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2009.2032396 · Cited by 50 · see citing works
An evolutionary annealing-simplex algorithm for global optimisation of water resource systems
Efstratiadis, A., & Koutsoyiannis, D. (2002) · DSpace - NTUA (National Technical University of Athens) · doi:10.13140/rg.2.1.1038.6162 · Cited by 39 · see citing works
A transduction model of the Meissner corpuscle
Bensmaı̈a, S. J. (2002) · Mathematical Biosciences · doi:10.1016/s0025-5564(02)00089-5 · Cited by 34 · see citing works
Silicon Modeling of the Mihalaş–Niebur Neuron
Folowosele, F. O., Hamilton, T. J., & Etienne‐Cummings, R. (2011) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2011.2167020 · Cited by 33 · see citing works
Kim, S. S., Mihalaş, Ş., Russell, A. F., Dong, Y., & Bensmaı̈a, S. J. (2011) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2011.2160560 · Cited by 31 · see citing works
Dong, Y., Mihalaş, Ş., Russell, A. F., Etienne‐Cummings, R., & Niebur, E. (2011) · Neural Computation · doi:10.1162/neco_a_00196 · Cited by 22 · see citing works
Looft, F. J. (1996) · Somatosensory & Motor Research · doi:10.3109/08990229609028908 · Cited by 20 · see citing works
Webb, A. C., & Burns, B. D. (1976) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.1976.0075 · Cited by 20 · see citing works
Improved Integral Equation Solution for the First Passage Time of Leaky Integrate-and-Fire Neurons
Dong, Y., Mihalaş, Ş., & Niebur, E. (2010) · Neural Computation · doi:10.1162/neco_a_00078 · Cited by 15 · see citing works
Webb, A. C. (1976) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.1976.0076 · Cited by 15 · see citing works
[A statistical study of the interspike-interval distribution of cortical neurons].
Beyer, H., Schmidt, J., Hinrichs, O., & Schmolke, D. (1975) · PubMed · Cited by 9 · see citing works
Probabilistic properties of wavelets in kinetic surface roughening
Bershadskii, A. (2001) · Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics · doi:10.1103/physreve.64.027104 · Cited by 2 · see citing works
Flagged first, then by citations
1 of the 37 references couldn’t be matched to a known paper.
Research works printed but not linked: 2
- Chubbuck J. Small motion biological stimulator. APL Tech Digest 5: 18–23, 1966 [Google Scholar]
- Dong Y. Analysis and parameter estimation of spiking neuronal systems. PhD Thesis, Johns Hopkins Dept. of Neuroscience, Baltimore, MD, 2010 [Google Scholar]
Cited by
Sorted by citations
Generalized leaky integrate-and-fire models classify multiple neuron types
Teeter, C., Iyer, R., Menon, V., Gouwens, N. W., Feng, D. H., Berg, J., Szafer, A., Cain, N., Zeng, H., Hawrylycz, M., et al. (2018) · Nature Communications · doi:10.1038/s41467-017-02717-4 · Cited by 277 · see citing works
Simulating tactile signals from the whole hand with millisecond precision
Saal, H. P., Delhaye, B. P., Rayhaun, B. C., & Bensmaı̈a, S. J. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1704856114 · Cited by 263 · see citing works
The neural mechanisms of manual dexterity
Sobinov, A. R., & Bensmaia, S. J. (2021) · Nature Reviews Neuroscience · doi:10.1038/s41583-021-00528-7 · Cited by 248 · see citing works
Natural scenes in tactile texture
Manfredi, L. R., Saal, H. P., Brown, K. J., Zielinski, M. C., Dammann, J. F., Polashock, V. S., & Bensmaia, S. J. (2014) · Journal of Neurophysiology · doi:10.1152/jn.00680.2013 · Cited by 211 · see citing works
Biomimetic approaches to bionic touch through a peripheral nerve interface
Saal, H. P., & Bensmaia, S. J. (2015) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2015.06.010 · Cited by 179 · see citing works
“The combined population response is thus determined not only by contact pressure but also by its first and second derivatives (Dong et al., 2013; Kim et al., 2010).”
Kim, S., Callier, T., Tabot, G. A., Gaunt, R. A., Tenore, F. V., & Bensmaia, S. J. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1509265112 · Cited by 125 · see citing works
Lesniak, D. R., Marshall, K. L., Wellnitz, S. A., Jenkins, B. A., Baba, Y., Rasband, M. N., Gerling, G. J., & Lumpkin, E. A. (2014) · eLife · doi:10.7554/elife.01488 · Cited by 106 · see citing works
Delhaye, B. P., Jarocka, E., Barrea, A., Thonnard, J., Edin, B. B., & Lefèvre, P. (2021) · eLife · doi:10.7554/elife.64679 · Cited by 99 · see citing works
Biomimetic encoding model for restoring touch in bionic hands through a nerve interface
Okorokova, E. V., He, Q., & Bensmaı̈a, S. J. (2018) · Journal of Neural Engineering · doi:10.1088/1741-2552/aae398 · Cited by 90 · see citing works
Automated High-Throughput Characterization of Single Neurons by Means of Simplified Spiking Models
Pozzorini, C., Mensi, S., Hagens, O., Naud, R., Koch, C., & Wulfram Gerstner, B. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004275 · Cited by 89 · see citing works
Biological and bionic hands: natural neural coding and artificial perception
Bensmaia, S. J. (2015) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2014.0209 · Cited by 67 · see citing works
Rate and timing of cortical responses driven by separate sensory channels
Saal, H. P., Harvey, M. A., & Bensmaı̈a, S. J. (2015) · eLife · doi:10.7554/elife.10450 · Cited by 63 · see citing works
Neural Coding of Contact Events in Somatosensory Cortex
Callier, T., Suresh, A. K., & Bensmaı̈a, S. J. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhy337 · Cited by 63 · see citing works
E-Skins: Biomimetic Sensing and Encoding for Upper Limb Prostheses
Iskarous, M. M., & Thakor, N. V. (2019) · Proceedings of the IEEE · doi:10.1109/jproc.2019.2939369 · Cited by 54 · see citing works
Biswas, A., Manivannan, M., & Srinivasan, M. A. (2014) · IEEE Transactions on Haptics · doi:10.1109/toh.2014.2369422 · Cited by 42 · see citing works
Modeling foot sole cutaneous afferents: FootSim
Katic, N., Kazu, R. S., Cleland, L. D., Strzalkowski, N. D. J., Bent, L. R., Raspopović, S., & Saal, H. P. (2022) · iScience · doi:10.1016/j.isci.2022.105874 · Cited by 25 · see citing works
A finite-element model of mechanosensation by a Pacinian corpuscle cluster in human skin
Quindlen, J. C., & Barocas, V. H. (2018) · Biomechanics and Modeling in Mechanobiology · doi:10.1007/s10237-018-1011-1 · Cited by 26 · see citing works
A Variation Code Accounts for the Perceived Roughness of Coarsely Textured Surfaces
Goodman, J. M., & Bensmaı̈a, S. J. (2017) · Scientific Reports · doi:10.1038/srep46699 · Cited by 25 · see citing works
Merkel Cells Are Multimodal Sensory Cells: A Review of Study Methods
Bataille, A., Gall‐Ianotto, C. L., Misery, L., & Talagas, M. (2022) · Cells · doi:10.3390/cells11233827 · Cited by 21 · see citing works
Gerling, G. J., Wan, L., Hoffman, B. U., Wang, Y., & Lumpkin, E. A. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006264 · Cited by 19 · see citing works
The Neural Mechanisms of Touch and Proprioception at the Somatosensory Periphery
Goodman, J. M., & Bensmaı̈a, S. J. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-805408-6.00014-2 · Cited by 14 · see citing works
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Tian, Y., Valle, G., Cederna, P. S., & Kemp, S. W. P. (2025) · Biomimetics · doi:10.3390/biomimetics10030130 · Cited by 14 · see citing works
Vasudevan, M. K., Sadanand, V., Manivannan, M., & Srinivasan, M. A. (2020) · Somatosensory & Motor Research · doi:10.1080/08990220.2020.1726739 · Cited by 11 · see citing works
Ouyang, Q., Wu, J., Shao, Z., Chen, D., & Bisley, J. W. (2020) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2020.3007397 · Cited by 11 · see citing works
Esposito, D., Lanotte, F., Mugnai, C., Massari, L., Camboni, D., Mazzoni, A., & Oddo, C. M. (2019) · IEEE Transactions on Medical Robotics and Bionics · doi:10.1109/tmrb.2019.2912705 · Cited by 10 · see citing works
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Umeda, T., Watanabe, H., Sato, M.-A., Kawato, M., Isa, T., & Nishimura, Y. (2014) · Frontiers in Neuroscience · doi:10.3389/fnins.2014.00097 · Cited by 8 · see citing works
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Kuroki, S., Watanabe, J., & Nishida, S. (2016) · Journal of Neurophysiology · doi:10.1152/jn.00790.2015 · Cited by 8 · see citing works
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Ouyang, Q., Wu, J., Shao, Z., Wu, M., & Cao, Z. (2019) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2019.00027 · Cited by 6 · see citing works
OBSOLETE: The Neural Mechanisms of Touch and Proprioception at the Somatosensory Periphery
Goodman, J. M. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-809324-5.00014-6 · Cited by 5 · see citing works
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Barrett-Snyder, K., Lane, S., Lazarus, N. S., Alberts, W. C. K., & Hanrahan, B. (2021) · Micromachines · doi:10.3390/mi12050574 · Cited by 4 · see citing works
Ziat, M., & Bensmaı̈a, S. J. (2015) · Elsevier eBooks · doi:10.1016/b978-0-08-097086-8.55057-0 · Cited by 4 · see citing works
Nonlinear two stage mechanotransduction model and neural response of Pacinian Corpuscle
Biswas, A., Manivannan, M., & Srinivasan, M. A. (2014) · Biomedical Sciences and Engineering Conference (BSEC) · doi:10.1109/bsec.2014.6867740 · Cited by 3 · see citing works
Restoring the sense of touch with electrical stimulation of the nerve and brain
Callier, T., & Bensmaı̈a, S. J. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-822828-9.00010-1 · Cited by 3 · see citing works
Experimental Analysis of a Novel, Magnetic-Driven Tactile Feedback Device
Alshaibani, F., Thompson, M. S., & Bergmann, J. (2020) · Prosthesis · doi:10.3390/prosthesis2010003 · Cited by 2 · see citing works
Biological and bionic hands: Natural neural coding and artificial perception
Bensmaı̈a, S. J. (2015) · IEEE World Haptics Conference (WHC) · doi:10.1109/whc.2015.7177674 · Cited by 2 · see citing works
Ishizuka, H., Kitaguchi, S., Nakatani, M., YOSHIMURA, H., & Shimokawa, F. (2024) · Journal of Biomechanical Science and Engineering · doi:10.1299/jbse.24-00027 · Cited by 1 · see citing works
Gao, T. Y., Deng, B., Wang, J., Zhang, W., & Yi, G. (2025) · Nonlinear Dynamics · doi:10.1007/s11071-025-10930-w · Cited by 1 · see citing works
Valero, M. R., Hale, N., Tang, J., & Jiang, L. (2017) · IEEE World Haptics Conference (WHC) · doi:10.1109/whc.2017.7989874 · Cited by 1 · see citing works
Osborn, L. E., Betthauser, J., & Thakor, N. V. (2019) · Wiley eBooks · doi:10.1002/047134608x.w1424.pub2
Kim, S. S. (2015) · Encyclopedia of Computational Neuroscience · doi:10.1007/978-1-4614-6675-8_380
Bensmaı̈a, S. J. (2013) · Encyclopedia of Computational Neuroscience · doi:10.1007/978-1-4614-7320-6_561-1
Kim, S. S. (2013) · Encyclopedia of Computational Neuroscience · doi:10.1007/978-1-4614-7320-6_380-2
Kim, S. S. (2022) · Encyclopedia of Computational Neuroscience · doi:10.1007/978-1-0716-1006-0_380
Bensmaı̈a, S. J. (2022) · Encyclopedia of Computational Neuroscience · doi:10.1007/978-1-0716-1006-0_561
Biophysical models of slowly and rapidly adapting mechanosensitive tactile afferents in human tongue
Rezaei, M., Güthrie, B., & Gerling, G. J. (2024) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc53108.2024.10782872
- Authors
- Dong, Y., Mihalas, S., Kim, S. S., Yoshioka, T., Bensmaia, S., & Niebur, E.
- Affiliations
- Johns Hopkins University (US)
- Zanvyl Krieger Mind/Brain Institute, Johns Hopkins University, Baltimore, Maryland;
- Howard Hughes Medical Institute (US)
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, Illinois
- Venue
- Journal of Neurophysiology
- Publisher
- American Physiological Society
- Published
- 2013-03-01
- Volume
- 109
- Issue
- 5
- Pages
- 1350-1359
- ISSN
- 0022-3077 (ISSN-L)
- 1522-1598
- PMC
- PMC3602832
- OpenAlex
- W2097581740
- Cited by
- 47
- References
- 37
- Citations by year
- 2025: 2 · 2024: 2 · 2023: 1 · 2022: 4 · 2021: 4 · 2020: 5 · 2019: 4 · 2018: 5 · 2017: 4 · 2016: 1 · 2015: 8 · 2014: 5 · 2013: 2
- FWCI
- 2.46
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Locations
- Journal of Neurophysiology (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Science Foundation
- National Institutes of Health (5r01ey016281-08)
- Office of Naval Research
- National Eye Institute (R01-EY016281)
- National Institute of Neurological Disorders and Stroke (5R01-NS-40596, R01NS18787, R01NS018787, R01NS040596)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Tactile and Sensory Interactions
- Life Sciences › Neuroscience › Sensory Systems › Ion Channels and Receptors
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- mechanotransduction, tactile perception, glabrous skin, computational model, integrate-and-fire neurons, saturating nonlinearity, sensory adaptation, tactile threshold
- MeSH terms
- Action Potentials, Adaptation, Physiological, Animals, Macaca mulatta, Mechanoreceptors, Models, Neurological, Reaction Time, Sensory Thresholds, Skin, Nonlinear Dynamics, Mechanotransduction, Cellular
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