Predicting the timing of spikes evoked by tactile stimulation of the hand
Open accessAbstract
What does the hand tell the brain? Tactile stimulation of the hand evokes remarkably precise patterns of neural activity, suggesting that the timing of individual spikes may convey information. However, many aspects of the transformation of mechanical deformations of the skin into spike trains remain unknown. Here we describe an integrate-and-fire model that accurately predicts the timing of individual spikes evoked by arbitrary mechanical vibrations in three types of mechanoreceptive afferent fibers that innervate the hand. The model accounts for most known properties of the three fiber types, including rectification, frequency-sensitivity, and patterns of spike entrainment as a function of stimulus frequency. These results not only shed light on the mechanisms of mechanotransduction but can be used to provide realistic tactile feedback in upper-limb neuroprostheses.
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The 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 Kim et al., 2010
The 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 Kim et al., 2010
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 Kim et al., 2010
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HAMASAKI, T., & Yamada, D. (2021) · Advances in Mechanical Engineering · doi:10.1177/1687814020984651 · 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
Tactile memory impairments in younger and older adults
Lange, L.-S., Chrysidou, A., Liu, P., & Kuehn, E. (2024) · Scientific Reports · doi:10.1038/s41598-024-62683-y · Cited by 3 · see citing works
Temporally Local Tactile Codes Can Be Stored in Working Memory
Bhattacharjee, A., & Schwarz, C. (2022) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2022.840108 · 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
Active tactile sensing in a neurorobotic Braille-reading system
Bologna, L. L., Pinoteau, J., Garrido, J. A., & Arleo, A. (2012) · IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob) · doi:10.1109/biorob.2012.6290937 · Cited by 3 · see citing works
Jahangiri, A., & Gerling, G. J. (2011) · International IEEE/EMBS Conference on Neural Engineering · doi:10.1109/ner.2011.5910511 · Cited by 3 · see citing works
Simulation model of Pacinian corpuscle for haptic system design
Liu, D. (2011) · RIT Scholar Works (Rochester Institute of Technology) · Cited by 3 · see citing works
Biomimetic bidirectional hand neuroprostheses for restoring somatosensory and motor functions
Iberite, F., Mendez, V., Mazzoni, A., Shokur, S., & Micera, S. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-822828-9.00011-3 · 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
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
The Bayesian Decoding of Force Stimuli from Slowly Adapting Type I Fibers in Humans
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Neuromorphic Tactile Sensing and Encoding
Kumar, D., Silva, A. N., & Thakor, N. V. (2021) · Handbook of Neuroengineering · doi:10.1007/978-981-15-2848-4_117-1 · Cited by 1 · see citing works
Mazzoni, A. (2017) · Springer INdAM series · doi:10.1007/978-3-319-68297-6_1 · Cited by 1 · see citing works
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A Novel Stimulation Method Based on a Neuromorphic Mechanoreceptor Model for Haptic Illusion
Gwak, K., Park, J.-C., & Kim, D. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-31404-9_9
A point process approach to encode tactile afferents
Kasi, P. K., Birznieks, I., & André, V. S. (2015) · International IEEE/EMBS Conference on Neural Engineering · doi:10.1109/ner.2015.7146626
glabrous skin A simple model of mechanotransduction in primate
Dong, Y., Mihalaş, Ş., Kim, S. S., Yoshioka, T., & Bensmaı̈a, S. J. (2013)
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FPGA implementation of a FA-1 mechanoreceptor model for efficient representation of tactile features
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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
- Authors
- Kim, S. S., Sripati, A. P., & Bensmaia, S. J.
- Affiliations
- Johns Hopkins University (US)
- Center for the Neural Basis of Cognition (US)
- Zanvyl Krieger Mind/Brain Institute and
- ORCID
- Kim, Sung Soo
- Venue
- Journal of Neurophysiology
- Publisher
- American Physiological Society
- Published
- 2010-09
- Volume
- 104
- Issue
- 3
- Pages
- 1484-1496
- ISSN
- 0022-3077 (ISSN-L)
- 1522-1598
- PMC
- PMC2944675
- OpenAlex
- W2127076263
- Cited by
- 71
- References
- 59
- Citations by year
- 2025: 2 · 2024: 2 · 2023: 3 · 2022: 8 · 2021: 6 · 2020: 6 · 2019: 2 · 2018: 3 · 2017: 4 · 2016: 3 · 2015: 9 · 2014: 5 · 2013: 5 · 2012: 9
- FWCI
- 3.49
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Journal of Neurophysiology (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- NOT FOUND REPOSITORY (Indian Institute of Science Bangalore) (acceptedVersion)
- Funders
- National Eye Institute (R01EY018620, EY-018620)
- National Institute of Neurological Disorders and Stroke (NS-18787, R01NS018787)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Tactile and Sensory Interactions
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neural Engineering
- Keywords
- tactile stimulation, spike timing, mechanoreceptors, integrate-and-fire model, mechanotransduction, frequency sensitivity, rectification, mechanical vibration, hand, afferent nerve fibers, tactile feedback, neural prostheses
- MeSH terms
- Action Potentials, Animals, Evoked Potentials, Somatosensory, Forecasting, Hand, Macaca mulatta, Mechanoreceptors, Time Factors, Touch
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