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REVIEW 3 major objections 5 minor 60 references

Invariant neuromorphic representations of tactile stimuli improve robustness of a real-time texture classification system

T0 review · 3 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read Neuromorphic encoding that cancels scanning speed and contact force lets a tactile texture classifier identify textures under never-experienced exploration conditions, raising accuracy at 50 principal components from 37.44% to 83.15%.

desk verdict Speed scaling is a genuinely useful contribution; the force invariance headline result is undermined by per-texture coefficient fitting that uses the true label at test time, so the 83% novel-condition number is not deployment-feasible. read the letter →

arxiv 2411.17060 v1 pith:RJA33PJZ submitted 2024-11-26 cs.RO eess.SP

Mark M. Iskarous , Zan Chaudhry , Fangjie Li , Samuel Bello , Sriramana Sankar , Ariel Slepyan , Natasha Chugh , Christopher L. Hunt
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Rebecca J. Greene Nitish V. Thakor
This is my paper · ORCID
classification cs.ROeess.SP
keywords tactilesensingneuromorphicencodingtextureclassificationspeedinvarianceforceneuroroboticsneuroprosthesisspikingneuralnetworks
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper aims to show that a robot or prosthetic hand can recognize textures more reliably when the raw touch signal is first converted into neuron-like spike trains that are made insensitive to how hard and how fast the surface is scanned. The proposed encoding has three stages: an analog force-invariance module that rescales each sensor pixel's reading toward a 500 g reference, an Izhikevich spiking encoder that mimics slowly and rapidly adapting mechanoreceptors, and a spiking-domain speed-invariance module that multiplies spike times by the ratio of actual to reference speed. On a 16-texture, 15-condition drum dataset, the full invariant representation lifts classification accuracy on speed-force pairs excluded from classifier training from 37.44% to 83.15% at 50 principal components, and it reaches high accuracy with fewer features. The speed-invariance module alone also improves a real-time, human-operated five-texture classifier, because natural variation in hand speed acts like an unseen condition. The authors read these results as evidence that biologically motivated invariant encoding makes neurorobotic tactile sensing more accurate, more efficient, and more robust.

What carries the argument

The load-bearing mechanism is a three-stage neuromorphic encoding pipeline whose two novel modules remove the nuisance parameters from the tactile signal. The force-invariance module solves an optimization for per-texture, per-taxel, per-speed, per-force scaling coefficients $C$ so that the SA spike rate matches the 500 g reference, using binary search on the monotonic spike-rate map. The spiking activity encoding module applies the Izhikevich Tonic Spiking model to each taxel to produce SA spikes proportional to signal level and RA spikes proportional to signal change. The speed-invariance module multiplies every spike time by $\frac{j}{120}$, where $j$ is the actual scanning speed in mm/s, contracting slow trials and expanding fast trials to the reference duration. The output is a spike-train representation whose windowed spike-rate and spike-count features, after principal component analysis and linear discriminant analysis, classify texture identity rather than exploration condition.

What would settle it

Refit the force-scaling coefficients using only the 250 g and 500 g recordings, apply them to the 1000 g trials, and measure classification accuracy on the untrained-force conditions with the same LDA setup; if accuracy at 50 PCs stays near 88.7% (untrained force) or 83.2% (untrained speed and force), the force-invariance claim holds without using test-force data during fitting, whereas a drop toward the 57.4% or 37.4% unscaled baselines would show the reported robustness depends on fitting 1000 g coefficients on 1000 g data.

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Extended reading notes

Core claim

The authors' central claim is that tactile texture recognition improves along three axes—accuracy, computational efficiency, and robustness to novel exploratory conditions—when the sensor signal is transformed into a speed- and force-invariant spiking representation before classification. The transformation combines per-taxel analog scaling that matches SA spike rates to the 500 g reference (force invariance), SA and RA spike encoding with the Izhikevich Tonic Spiking model, and multiplicative time scaling of spike times by the ratio of the actual scanning speed to the 120 mm/s reference (speed invariance). On the offline drum dataset, the complete pipeline raises extrapolation accuracy on untrained speed-force combinations from 37.44% to 83.15% at 50 PCs, and on all-condition individual-texture classification from 91.78% to 98.85%. The two invariance modules are complementary: force scaling carries the gain when only the force is novel, speed scaling carries the gain when only the speed is novel, and together they compound. In the real-time human-operated system, speed scaling alone raises five-texture plateau accuracy from about 65% to about 90%, supporting the claim that human imprecision is experienced by the classifier as a novel condition.

Load-bearing premise

The load-bearing assumption is that per-texture, per-taxel force-scaling coefficients fitted on one sensor session remain a valid force-invariance map when anything about the sensor changes, which the paper itself notes is not the case.

Editorial extensions

If this is right

  • A classifier trained on only a subset of speed-force combinations can generalize to untrained speeds and forces, so a deployed touch system does not need to be trained on every possible exploration condition.
  • The invariant representation reaches a given classification accuracy with fewer principal components, which the authors equate with reduced computational cost for embedded and real-time use.
  • Force scaling and speed scaling act on different failure modes: force scaling mainly restores accuracy when the contact force is novel, speed scaling when the speed is novel, and the two together outperform either alone.
  • In human-operated sensing, where hand speed varies naturally, speed scaling effectively converts 'unseen' exploration conditions into conditions the classifier can handle, raising plateau accuracy from about 65% to about 90% in the five-texture demonstration.
  • Because the invariant code is built from SA/RA-like spiking activity, the same representations could be used to drive electrical stimulation patterns for more naturalistic prosthetic sensory feedback.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The force-scaling coefficients are fitted once on the full dataset, including the 1000 g condition used as the 'untrained force' test, so the extrapolation result should be read as testing the classifier's generalization, not the force module's ability to handle a force it has never seen; a stricter test would exclude the target force from coefficient fitting.
  • The speed-invariance module operates purely on spike times, so the same time-scaling rule could be applied to any event-based tactile encoding, independent of the specific sensor array or neuron model used here.
  • The paper's own limitation statement implies that force invariance would not transfer across sensor sessions or recalibrations; a direct test would measure classification accuracy with old coefficients on newly collected data from the same sensor, which would likely show the module's benefit is session-specific.
  • A real-time force-invariance module could be built without external ground-truth force if the sensor's baseline or a co-located load cell provides an online force estimate, extending the speed-only real-time system in this paper.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The paper proposes a neuromorphic tactile texture encoding pipeline with three stages: an analog force-scaling module, an Izhikevich-based SA/RA spiking encoding module, and a spike-time speed-scaling module. The resulting representations are evaluated offline on a rotating-drum dataset of 16 textures, 15 speed-force conditions, and 24,000 trials, and in a real-time human-operated system with 5 textures. The authors report that the invariant representations improve classification accuracy, computational efficiency, and robustness to novel speed-force conditions; the headline offline result is an improvement from 37.44% to 83.15% accuracy at 50 PCs when both speed and force are novel (Table 1, Fig. 3C).

Significance. If the claims are fully supported, the work is significant: it connects biological tactile invariance to an engineered neuromorphic pipeline, provides an unusually large controlled tactile dataset, and demonstrates a real-time, deployable speed-invariance module with improved accuracy and fewer principal components. The speed-scaling algorithm is simple, mechanistic, label-free, and tested in a human-operated system, which is a credible and useful contribution. However, the force-invariance module—a central component of the paper's title and abstract—is evaluated in a way that leaks test information, both through coefficients indexed by the texture label and through whole-dataset preprocessing. The offline force-related results therefore do not yet establish a deployable force-invariant representation.

major comments (3)
  1. [Materials and Methods – Force Invariance Module; Eq. (1); Table 1; Fig. 3C] The force scaling coefficients C^T_{i,j,k} in Eq. (1) are indexed by the applied texture label T and are fitted separately for each texture, and the text states that the calculation is done once for the whole dataset. In the classification evaluations, each test trial is scaled with the coefficient belonging to its own texture, so the scaling operation uses the very label that the classifier is asked to predict. Moreover, for the 'untrained force' condition, the coefficient for 1000 g is fitted on the same 1000 g trials that later constitute the test set. This makes the 'Force Scaled' and 'Speed and Force Scaled' accuracies in Table 1 and Fig. 3C-D leak test information and does not represent a deployable system.”
  2. [Materials and Methods – Tactile Texture Dataset – Data Collection] The taxel readings are normalized between 0 and 1 using the minimum and maximum readings from the whole dataset before any train/test split. This is a preprocessing leak: the normalization statistics include test trials, which can inflate all offline classification accuracies, including the speed-only results, because the scaled features depend on test-set statistics. Normalization parameters should be estimated from the training folds only and then applied to the test folds.
  3. [Discussion – limitations paragraph] The paper acknowledges that the force scaling coefficients are ineffective if the tactile sensor changes, but the more fundamental problem is that the coefficients are label-conditioned as defined in Eq. (1). Since the real-time system never includes the force module and no label-free or train-only force-invariance evaluation is presented, the paper's claims that the representation is 'force invariant' and that force scaling improves robustness to novel conditions are not supported by the current evidence. The claims need to be substantially qualified or the force module needs to be re-evaluated with coefficients estimated without using test labels or test data.
minor comments (5)
  1. [Materials and Methods – Real-time Texture Classification – Speed Invariance Module] The text says the reference speed in the real-time experiment is 100 mm/s, but Eq. (2) divides by 120; please reconcile the equation with the stated reference speed or clarify that the denominator was changed in the real-time implementation.
  2. [Fig. S5 and Fig. 4 legend] The effect-size notation for the two-proportion z-test uses 'd' where 'h' (Cohen's h) is intended; please correct the symbol in Fig. S5 and any matching legend text.
  3. [Materials and Methods – Force Invariance Module] Equation (1) defines C as belonging to R^{16×18×5×3}, but coefficients are optimized only for 250 and 1000 g; please specify the values for the 500 g reference condition or adjust the matrix dimensions accordingly.
  4. [Results, Eq. (2)] The variable j in Eq. (2) is called a speed setting but its units are not stated; please define j as the scanning speed in mm/s or as an explicit index with a mapping to speeds.
  5. [Materials and Methods – Force Invariance Module] The statement that 62 out of 4320 coefficients did not converge should clarify whether the 500 g reference coefficients are included in the denominator, since they are not optimized.

Circularity Check

2 steps flagged · score 6.0 of 10

Offline force-invariance gains are partly circular: Eq. (1) fits per-texture scaling coefficients on the whole dataset, including the 'novel' 1000 g test trials, then applies each test trial's ground-truth texture coefficient before classification.

  1. fitted input called prediction [Materials and Methods, 'Tactile Texture Dataset – Force Invariance Module' (Eq. 1); Results, 'Texture Classification in Trained and Novel Conditions' (Fig. 3C)]
    "We solve for the matrix 𝐶 ∈ 𝑅𝟙𝟞×𝟙𝟠×𝟝×𝟛 (whose entries 𝐶𝑖,𝑗,𝑘𝑇 are scaling coefficients for applied texture T, taxel i, speed setting j, and force setting k) such that: |SR(Φ𝑆𝐴(𝐶𝑖,𝑗,𝑘𝑇 𝑅𝑖,𝑗,𝑘𝑇 )) − 𝑆𝑅(Φ𝑆𝐴(𝑅𝑖,𝑗,500𝑇 ))| < ϵ (1). ... The calculation of the force scaling coefficients is only done one time for the whole dataset."

    The untrained-force test condition (1000 g) is exactly one of the k values for which Eq. (1) fits C^T_{i,j,k} from R^T_{i,j,k}, i.e. from the very 1000 g trials that later form the Fig. 3C/D test set. The 83.15% 'Speed and Force Scaled' accuracy for novel speed+force is therefore not an extrapolation to an unseen force: the normalization for that force was calibrated on the same trials being classified. The 'novel condition' claim reduces to a within-dataset fit of the force module, not a prediction.

  2. self definitional [Materials and Methods, 'Tactile Texture Dataset – Force Invariance Module', Eq. (1); Results, 'Texture Classification in Trained and Novel Conditions' (Figs. 3A-D and Table 1)]
    "Therefore, scaling coefficients are determined for each texture for each taxel for each speed at 250 and 1000 g. ... (whose entries 𝐶𝑖,𝑗,𝑘𝑇 are scaling coefficients for applied texture T, taxel i, speed setting j, and force setting k)."

    The coefficient matrix is indexed by the true texture label T. In the offline evaluation, each test trial's features are scaled with the coefficient for that trial's ground-truth texture before LDA is asked to predict that same texture. Thus the force-scaled representation is constructed using the answer key; a deployment system that does not know T cannot apply this normalization. The reported improvement of Force Scaled over Original in Figs. 3A-D and Table 1 is therefore not a clean measure of a label-free invariant representation.

full rationale

Most of the pipeline is self-contained. Speed invariance (Eq. 2) is a deterministic time rescaling by the measured scanning speed and is validated in the real-time human-operated system; no circularity appears there. The paper contains no load-bearing self-citation and invokes no imported uniqueness theorem; references 47-50 are prior apparatus and sensor work, not the invariance claim. However, the force invariance module's offline evaluation is circular in two connected ways. Eq. (1) defines C^T_{i,j,k} using the true texture label T and using R^T_{i,j,k} from the very force condition later called 'untrained', and these coefficients are computed once on the whole dataset and then applied to the same trials before LDA classification. Consequently, the headline extrapolation numbers (37.44% to 83.15% at 50 PCs in Table 1/Fig. 3C) are achieved with label-conditioned, test-condition-fitted preprocessing, not with a deployable invariant representation. The paper's own limitation statement that force coefficients are 'ineffective if anything about the tactile sensor changes' reinforces that the force invariance is dataset-specific rather than a general physical property. Because the speed module and its real-time benefit stand independently, the circularity is partial rather than total.

Assumptions & free parameters 3 free parameters · 4 assumptions · 2 invented entities

The central claim rests on a small set of engineering assumptions: the Izhikevich model as the encoding backbone, the choice of 500 g as the force reference, the sufficiency of scalar scaling for force compensation, and the adequacy of PCA/LDA for decoding. The force scaling coefficients are free parameters fitted to the dataset. No new physical entities are introduced, only signal processing stages, which keeps the invented-entity count low.

free parameters (3)
  • Force scaling coefficients C_i,j,k^T = 62 of 4320 coefficients did not converge and were capped at 5; the rest are fitted per texture/taxel/speed/force
    Equation (1) defines an optimization that finds coefficients to match the SA spike rate at 250 or 1000 g to the reference 500 g rate. These are fitted values, not derived from first principles, and the paper states they are ineffective when the sensor changes.
  • Izhikevich model parameters (a, b, c, d, k) = Tonic Spiking parameters a=0.02, b=0.2, c=-65, d=6; k=100 (SA), k=3 (RA)
    These are taken from the prior literature (Izhikevich 2003, 2004) and are standard for the Tonic Spiking model, so they are inputs from prior literature rather than free parameters fit to the present data.
  • Reference speed for speed invariance = 120 mm/s offline, 100 mm/s real-time
    The reference speed is chosen for convenience and the paper states that the real-time value is arbitrary, affecting only feature resolution. It does not affect the central comparison.
assumptions (4)
  • domain assumption The Izhikevich neuron model with the Tonic Spiking parameter set adequately captures the mechanoreceptor population responses relevant to texture discrimination.
    The spiking activity encoding module rests on this modeling choice, introduced in the section 'Speed and Force Invariant Texture Representation' and detailed in Materials and Methods. The paper does not validate the model against biological recordings.
  • ad hoc to paper The spike rate of the SA encoding at 500 g is the appropriate target for force invariance.
    The target spike rate is set to the 500 g condition averaged across trials (equation 1). This is a design choice made by the authors without independent biological or physical justification.
  • domain assumption Scaling sensor readings by a constant factor is sufficient to compensate for the nonlinear relationship between applied force and sensor output.
    The force scaling module multiplies the input current by C_i,j,k^T. The paper notes the relationship is nonlinear, yet uses a linear scaling; the binary search only finds the scalar that best matches the target spike rate.
  • standard math Linear discriminant analysis on principal components of spike-rate features is an adequate model for texture decoding.
    The classification pipeline is a standard statistical method, applied throughout the Results and Materials and Methods sections.
invented entities (2)
  • Force invariance module
    purpose: Scales analog sensor readings to make SA spike rates independent of contact force
    This is not a new physical entity but a signal processing stage. Its claimed property, force invariance, is defined relative to an empirical fit to the training data and is not validated outside the dataset or sensor session.
  • Speed invariance module independent evidence
    purpose: Scales spike times inversely proportional to scanning speed to make spiking representations speed invariant
    This is also a signal processing stage, not an invented physical entity. It has a falsifiable handle: the time-scaling prediction can be tested on novel speeds through classification accuracy, which the paper does report.

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Cite this review

Pith. "Pith review of Invariant neuromorphic representations of tactile stimuli improve robustness of a real-time texture classification system." pith.science (2026). https://pith.science/paper/RJA33PJZ

@misc{pith2026241117060,
  author       = {Pith},
  title        = {Pith review of: Invariant neuromorphic representations of tactile stimuli improve robustness of a real-time texture classification system},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/RJA33PJZ}},
  note         = {Machine review of arXiv:2411.17060}
}
read the original abstract

Humans have an exquisite sense of touch which robotic and prosthetic systems aim to recreate. We developed algorithms to create neuron-like (neuromorphic) spiking representations of texture that are invariant to the scanning speed and contact force applied in the sensing process. The spiking representations are based on mimicking activity from mechanoreceptors in human skin and further processing up to the brain. The neuromorphic encoding process transforms analog sensor readings into speed and force invariant spiking representations in three sequential stages: the force invariance module (in the analog domain), the spiking activity encoding module (transforms from analog to spiking domain), and the speed invariance module (in the spiking domain). The algorithms were tested on a tactile texture dataset collected in 15 speed-force conditions. An offline texture classification system built on the invariant representations has higher classification accuracy, improved computational efficiency, and increased capability to identify textures explored in novel speed-force conditions. The speed invariance algorithm was adapted to a real-time human-operated texture classification system. Similarly, the invariant representations improved classification accuracy, computational efficiency, and capability to identify textures explored in novel conditions. The invariant representation is even more crucial in this context due to human imprecision which seems to the classification system as a novel condition. These results demonstrate that invariant neuromorphic representations enable better performing neurorobotic tactile sensing systems. Furthermore, because the neuromorphic representations are based on biological processing, this work can be used in the future as the basis for naturalistic sensory feedback for upper limb amputees.

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Reference graph

Works this paper leans on

60 extracted references · 60 canonical work pages

  1. [1]

    U. Wijk, I. Carlsson, Forearm amputees’ views of prosthesis use and sensory feedback. J. Hand Ther. 28, 269–278 (2015)

    work page 2015

  2. [2]

    T. J. Prescott, M. E. Diamond, A. M. Wing, Active touch sensing. Philos. Trans. R. Soc. B Biol. Sci. 366, 2989–2995 (2011)

    work page 2011

  3. [3]

    Dargahi, S

    J. Dargahi, S. Najarian, Human tactile perception as a standard for artificial tactile sensing-- a review. Int. J. Med. Robot. 1, 23–35 (2004)

    work page 2004

  4. [4]

    W. M. B. Tiest, Tactual perception of material properties. Vision Res. 50, 2775–2782 (2010)

    work page 2010

  5. [5]

    M. M. Iskarous, N. V. Thakor, E-Skins: Biomimetic Sensing and Encoding for Upper Limb Prostheses. Proc. IEEE 107, 2052–2064 (2019)

    work page 2019

  6. [6]

    R. S. Dahiya, G. Metta, M. Valle, G. Sandini, Tactile Sensing—From Humans to Humanoids. IEEE Trans. Robot. 26, 1–20 (2010)

    work page 2010

  7. [7]

    M. L. Hammock, A. Chortos, B. C.-K. Tee, J. B.-H. Tok, Z. Bao, 25th Anniversary Article: The Evolution of Electronic Skin (E-Skin): A Brief History, Design Considerations, and Recent Progress. Adv. Mater. 25, 5997–6038 (2013)

    work page 2013

  8. [8]

    Chortos, J

    A. Chortos, J. Liu, Z. Bao, Pursuing prosthetic electronic skin. Nat. Mater. 15, 937–950 (2016)

    work page 2016

Show all 60 references
  1. [9]

    D. J. Weber, R. Friesen, L. E. Miller, Interfacing the somatosensory system to restore touch and proprioception: essential considerations. J. Mot. Behav. 44, 403–418 (2012). 16

    2012

  2. [10]

    Raspopovic, et al., Restoring Natural Sensory Feedback in Real-Time Bidirectional Hand Prostheses

    S. Raspopovic, et al., Restoring Natural Sensory Feedback in Real-Time Bidirectional Hand Prostheses. Sci. Transl. Med. 6, 222ra19-222ra19 (2014)

    2014

  3. [11]

    D. W. Tan, et al., A neural interface provides long-term stable natural touch perception. Sci. Transl. Med. 6, 257ra138-257ra138 (2014)

    2014

  4. [12]

    L. E. Osborn, et al., Prosthesis with neuromorphic multilayered e-dermis perceives touch and pain. Sci. Robot. 3, eaat3818 (2018)

    2018

  5. [13]

    Valle, et al., Biomimetic Intraneural Sensory Feedback Enhances Sensation Naturalness, Tactile Sensitivity, and Manual Dexterity in a Bidirectional Prosthesis

    G. Valle, et al., Biomimetic Intraneural Sensory Feedback Enhances Sensation Naturalness, Tactile Sensitivity, and Manual Dexterity in a Bidirectional Prosthesis. Neuron 100, 37- 45.e7 (2018)

    2018

  6. [14]

    D’Anna, et al., A closed-loop hand prosthesis with simultaneous intraneural tactile and position feedback

    E. D’Anna, et al., A closed-loop hand prosthesis with simultaneous intraneural tactile and position feedback. Sci. Robot. 4, eaau8892 (2019)

    2019

  7. [15]

    J. A. George, et al., Biomimetic sensory feedback through peripheral nerve stimulation improves dexterous use of a bionic hand. Sci. Robot. 4, eaax2352 (2019)

    2019

  8. [16]

    Zollo, et al., Restoring tactile sensations via neural interfaces for real-time force-and- slippage closed-loop control of bionic hands

    L. Zollo, et al., Restoring tactile sensations via neural interfaces for real-time force-and- slippage closed-loop control of bionic hands. Sci. Robot. 4, eaau9924 (2019)

    2019

  9. [17]

    V. E. Abraira, D. D. Ginty, The sensory neurons of touch. Neuron 79, 618–639 (2013)

    2013

  10. [18]

    K. O. Johnson, S. S. Hsiao, Neural mechanisms of tactual form and texture perception. Annu. Rev. Neurosci. 15, 227–250 (1992)

    1992

  11. [19]

    J. A. Pruszynski, R. S. Johansson, Edge-orientation processing in first-order tactile neurons. Nat. Neurosci. 17, 1404–1409 (2014)

    2014

  12. [20]

    Birznieks, P

    I. Birznieks, P. Jenmalm, A. W. Goodwin, R. S. Johansson, Encoding of Direction of Fingertip Forces by Human Tactile Afferents. J. Neurosci. 21, 8222–8237 (2001)

    2001

  13. [21]

    Z. M. Boundy-Singer, H. P. Saal, S. J. Bensmaia, Speed invariance of tactile texture perception. J. Neurophysiol. 118, 2371–2377 (2017)

    2017

  14. [22]

    S. J. Lederman, The perception of surface roughness by active and passive touch. Bull. Psychon. Soc. 18, 253–255 (1981)

    1981

  15. [23]

    J. D. Lieber, S. J. Bensmaia, Emergence of an Invariant Representation of Texture in Primate Somatosensory Cortex. Cereb. Cortex 30, 3228–3239 (2020)

    2020

  16. [24]

    A. I. Weber, et al., Spatial and temporal codes mediate the tactile perception of natural textures. Proc. Natl. Acad. Sci. 110, 17107–17112 (2013)

    2013

  17. [25]

    H. P. Saal, A. K. Suresh, L. E. Solorzano, A. I. Weber, S. J. Bensmaia, The Effect of Contact Force on the Responses of Tactile Nerve Fibers to Scanned Textures. Neuroscience 389, 99–103 (2018). 17

    2018

  18. [26]

    Fishel, G

    J. Fishel, G. Loeb, Bayesian Exploration for Intelligent Identification of Textures. Front. Neurorobotics 6, 4 (2012)

    2012

  19. [27]

    L. Qin, Z. Yi, Y. Zhang, Unsupervised surface roughness discrimination based on bio- inspired artificial fingertip. Sens. Actuators Phys. 269, 483–490 (2018)

    2018

  20. [28]

    U. B. Rongala, A. Mazzoni, C. M. Oddo, Neuromorphic Artificial Touch for Categorization of Naturalistic Textures. IEEE Trans. Neural Netw. Learn. Syst. 28, 819–829 (2017)

    2017

  21. [29]

    K. E. Friedl, A. R. Voelker, A. Peer, C. Eliasmith, Human-Inspired Neurorobotic System for Classifying Surface Textures by Touch. IEEE Robot. Autom. Lett. 1, 516–523 (2016)

    2016

  22. [30]

    Nguyen, et al., Dynamic Texture Decoding Using a Neuromorphic Multilayer Tactile Sensor in 2018 IEEE Biomedical Circuits and Systems Conference (BioCAS), (2018), pp

    H. Nguyen, et al., Dynamic Texture Decoding Using a Neuromorphic Multilayer Tactile Sensor in 2018 IEEE Biomedical Circuits and Systems Conference (BioCAS), (2018), pp. 1–4

    2018

  23. [31]

    Rasouli, Y

    M. Rasouli, Y. Chen, A. Basu, S. L. Kukreja, N. V. Thakor, An Extreme Learning Machine- Based Neuromorphic Tactile Sensing System for Texture Recognition. IEEE Trans. Biomed. Circuits Syst. 12, 313–325 (2018)

    2018

  24. [32]

    W. W. Lee, et al., A neuro-inspired artificial peripheral nervous system for scalable electronic skins. Sci. Robot. 4, eaax2198 (2019)

    2019

  25. [33]

    Ward-Cherrier, N

    B. Ward-Cherrier, N. Pestell, N. F. Lepora, NeuroTac: A Neuromorphic Optical Tactile Sensor applied to Texture Recognition in 2020 IEEE International Conference on Robotics and Automation (ICRA), (2020), pp. 2654–2660

    2020

  26. [34]

    A. K. Gupta, A. Nakagawa-Silva, N. F. Lepora, N. V. Thakor, Spatio-Temporal Encoding Improves Neuromorphic Tactile Texture Classification. IEEE Sens. J. 21, 19038–19046 (2021)

    2021

  27. [35]

    Rostamian, et al., Texture recognition based on multi-sensory integration of proprioceptive and tactile signals

    B. Rostamian, et al., Texture recognition based on multi-sensory integration of proprioceptive and tactile signals. Sci. Rep. 12, 21690 (2022)

    2022

  28. [36]

    Bartolozzi, L

    C. Bartolozzi, L. Natale, F. Nori, G. Metta, Robots with a sense of touch. Nat. Mater. 15, 921–925 (2016)

    2016

  29. [37]

    Hays, et al., Neuromorphic vision and tactile fusion for upper limb prosthesis control in 2019 9th International IEEE/EMBS Conference on Neural Engineering (NER), (2019), pp

    M. Hays, et al., Neuromorphic vision and tactile fusion for upper limb prosthesis control in 2019 9th International IEEE/EMBS Conference on Neural Engineering (NER), (2019), pp. 981–984

    2019

  30. [38]

    A. Dabbous, et al., Artificial Bio-Inspired Tactile Receptive Fields for Edge Orientation Classification in 2021 IEEE International Symposium on Circuits and Systems (ISCAS), (2021), pp. 1–5

    2021

  31. [39]

    Rahiminejad, A

    E. Rahiminejad, A. Parvizi-Fard, M. M. Iskarous, N. V. Thakor, M. Amiri, A Biomimetic Circuit for Electronic Skin With Application in Hand Prosthesis. IEEE Trans. Neural Syst. Rehabil. Eng. 29, 2333–2344 (2021). 18

    2021

  32. [40]

    Parvizi-Fard, M

    A. Parvizi-Fard, M. Amiri, D. Kumar, M. M. Iskarous, N. V. Thakor, A functional spiking neuronal network for tactile sensing pathway to process edge orientation. Sci. Rep. 11, 1320 (2021)

    2021

  33. [41]

    F. L. A. Macdonald, N. F. Lepora, J. Conradt, B. Ward-Cherrier, Neuromorphic Tactile Edge Orientation Classification in an Unsupervised Spiking Neural Network. Sensors 22, 6998 (2022)

    2022

  34. [42]

    S. F. Müller-Cleve, et al., Braille letter reading: A benchmark for spatio-temporal pattern recognition on neuromorphic hardware. Front. Neurosci. 16 (2022)

    2022

  35. [43]

    Donati, G

    E. Donati, G. Valle, Neuromorphic hardware for somatosensory neuroprostheses. Nat. Commun. 15, 556 (2024)

    2024

  36. [44]

    Y. Tian, et al., Real-Time, Dynamic Sensory Feedback Using Neuromorphic Tactile Signals and Transcutaneous Electrical Nerve Stimulation in 2022 IEEE Biomedical Circuits and Systems Conference (BioCAS), (2022), pp. 399–403

    2022

  37. [45]

    C. M. Oddo, et al., Intraneural stimulation elicits discrimination of textural features by artificial fingertip in intact and amputee humans. eLife 5, e09148 (2016)

    2016

  38. [46]

    Sankar, et al., Texture Discrimination with a Soft Biomimetic Finger Using a Flexible Neuromorphic Tactile Sensor Array That Provides Sensory Feedback

    S. Sankar, et al., Texture Discrimination with a Soft Biomimetic Finger Using a Flexible Neuromorphic Tactile Sensor Array That Provides Sensory Feedback. Soft Robot. 8, 577– 587 (2021)

    2021

  39. [47]

    Osborn, H

    L. Osborn, H. Nguyen, J. Betthauser, R. Kaliki, N. Thakor, Biologically inspired multi-layered synthetic skin for tactile feedback in prosthetic limbs in 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), (2016), pp. 4622–4625

    2016

  40. [48]

    E. M. Izhikevich, Simple model of spiking neurons. IEEE Trans. Neural Netw. 14, 1569–1572 (2003)

    2003

  41. [49]

    M. M. Iskarous, H. H. Nguyen, L. E. Osborn, J. L. Betthauser, N. V. Thakor, Unsupervised Learning and Adaptive Classification of Neuromorphic Tactile Encoding of Textures in 2018 IEEE Biomedical Circuits and Systems Conference (BioCAS), (2018), pp. 1–4

    2018

  42. [50]

    Chaudhry, F

    Z. Chaudhry, F. Li, M. Iskarous, N. Thakor, An Automated Tactile Stimulator Apparatus for Neuromorphic Tactile Sensing in 2023 11th International IEEE/EMBS Conference on Neural Engineering (NER), (2023), pp. 1–4

    2023

  43. [51]

    Callier, H

    T. Callier, H. P. Saal, E. C. Davis-Berg, S. J. Bensmaia, Kinematics of unconstrained tactile texture exploration. J. Neurophysiol. 113, 3013–3020 (2015)

    2015

  44. [52]

    E. M. Izhikevich, Which model to use for cortical spiking neurons? IEEE Trans. Neural Netw. 15, 1063–1070 (2004). 19

    2004

  45. [53]

    K. F. R. S. Pearson, LIII. On lines and planes of closest fit to systems of points in space. Lond. Edinb. Dublin Philos. Mag. J. Sci. 2, 559–572 (1901)

    1901

  46. [54]

    R. O. Duda, P. E. Hart, D. G. Stork, Pattern Classification, 2nd edition (Wiley-Interscience, 2000)

    2000

  47. [55]

    Stone, Cross-Validatory Choice and Assessment of Statistical Predictions

    M. Stone, Cross-Validatory Choice and Assessment of Statistical Predictions. J. R. Stat. Soc. Ser. B Methodol. 36, 111–133 (1974)

    1974

  48. [56]

    B. L. WELCH, THE GENERALIZATION OF ‘STUDENT’S’ PROBLEM WHEN SEVERAL DIFFERENT POPULATION VARLANCES ARE INVOLVED. Biometrika 34, 28–35 (1947)

    1947

  49. [57]

    Ch. 2: The t Test for Means

    “Ch. 2: The t Test for Means” in Statistical Power Analysis for the Behavioral Sciences, 2nd Ed., (Routledge, 1988), pp. 19–74

    1988

  50. [59]

    Ch. 6: Differences between Proportions

    “Ch. 6: Differences between Proportions” in Statistical Power Analysis for the Behavioral Sciences, 2nd Ed., (Routledge, 1988), pp. 179–214. 20 Figures and Tables Figure 1. Potential Neuromorphic Tactile Sensory Feedback in a Neuroprosthesis and the presently implemented signa...

    1988

  51. [60]

    Ch. 23: Comparing Two Proportions

    “Ch. 23: Comparing Two Proportions” in The Basic Practice of Statistics, (Macmillan Higher Education, 2017), pp. 950–987

    2017

  52. [61]

    Ch. 6: Differences between Proportions

    “Ch. 6: Differences between Proportions” in Statistical Power Analysis for the Behavioral Sciences, 2nd Ed., (Routledge, 1988), pp. 179–214

    1988

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.