Soft-neuromorphic artificial touch for applications in neuro-robotics

Spigler, Giacomo; Oddo, Calogero Maria; Carrozza, Maria Chiara

doi:10.1109/biorob.2012.6290849

Cited by 32 publications

(24 citation statements)

References 15 publications

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“…S1), in our neuromorphic artificial touch system53 we used customized implementations of the Izhikevich spiking neuron model54 to emulate 2 artificial mechanoreceptor types, mimicking to some extent slowly (labelled S model) and fast (labelled F model) adapting receptors (see Supplementary Fig. S2 for comparison with electrophysiological literature under similar experimental conditions).…”

Section: Methodsmentioning

confidence: 99%

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Artificial spatiotemporal touch inputs reveal complementary decoding in neocortical neurons

Oddo

Mazzoni

Spanne

et al. 2017

Sci Rep

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106

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Investigations of the mechanisms of touch perception and decoding has been hampered by difficulties in achieving invariant patterns of skin sensor activation. To obtain reproducible spatiotemporal patterns of activation of sensory afferents, we used an artificial fingertip equipped with an array of neuromorphic sensors. The artificial fingertip was used to transduce real-world haptic stimuli into spatiotemporal patterns of spikes. These spike patterns were delivered to the skin afferents of the second digit of rats via an array of stimulation electrodes. Combined with low-noise intra- and extracellular recordings from neocortical neurons in vivo, this approach provided a previously inaccessible high resolution analysis of the representation of tactile information in the neocortical neuronal circuitry. The results indicate high information content in individual neurons and reveal multiple novel neuronal tactile coding features such as heterogeneous and complementary spatiotemporal input selectivity also between neighboring neurons. Such neuronal heterogeneity and complementariness can potentially support a very high decoding capacity in a limited population of neurons. Our results also indicate a potential neuroprosthetic approach to communicate with the brain at a very high resolution and provide a potential novel solution for evaluating the degree or state of neurological disease in animal models.

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Section: Methodsmentioning

confidence: 99%

“…6. In dimensioning the parameters for the Izhikevich spiking neuron model54, we used the set of coefficients inducing regular spiking with weak adaptation53. Whenever the membrane potential v x reached the threshold level (Eq.…”

Section: Methodsmentioning

confidence: 99%

Artificial spatiotemporal touch inputs reveal complementary decoding in neocortical neurons

Oddo

Mazzoni

Spanne

et al. 2017

Sci Rep

Self Cite

106

View full text Add to dashboard Cite

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“…tactile stimuli [4][5][6][7]. Reviewing the literature, some works have been reported aiming to convert the tactile information into neural spike-trains [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

“…Reviewing the literature, some works have been reported aiming to convert the tactile information into neural spike-trains [4][5][6][7]. Recent works have used the mechanical contact information, more specifically pressure values [5,7], as key inputs to develop their models for tactile transduction, predominantly predicting neural-spike trains of SAI afferents.…”

Section: Introductionmentioning

confidence: 99%

“…However, some limitations exist, with other types of afferents, namely SAII [4][5][6][7], and stresses -like shear [5,7], to be also incorporated to the mechanotransduction models. In fact, studies performed on human subjects [1,2] have demonstrated the ability of SA and RA mechanoreceptors at the fingertips to detect and encode multi-axial mechanical stimuli [1,2], with the brain relying on this pressure and shear information for object manipulation and haptic identification [2].…”

Section: Introductionmentioning

confidence: 99%

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A comprehensive mechanotransduction model for tactile feedback based on multi-axial stresses at the fingertip-contact interface

Valero

Hale

Tang

et al. 2017

2017 IEEE World Haptics Conference (WHC)

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Abstract-This paper presents a mechanotransduction model designed to convert the multi-axial mechanical loads at the fingertip-contact interface into neural-spike trains, the MultiAxial Stress Mechanotransduction (MASM) model. We believe this is a first attempt towards a comprehensive model, accounting for the conversion of measured multi-axial (pressure and shear) stresses at the fingertip-contact interface into spike trains with modelled slow adapting (SA) and rapidly adapting (RA) afferents type I (SAI, RAI) and II (SAII, RAII) based on the properties of those in human fingertips. To illustrate and assess how the MASM model works, artificial data mimicking typical stress stimuli used to evaluate the response of biological afferents were fed to the model and results examined. Subsequently, the suitability of the MASM model for real tactile applications was preliminary tested by inputting to the model real life, measured pressure and shear data in a fingertip 'press-push-lift' action. The response of the modelled SA and RA afferents was analysed and qualitatively compared to biological data reported in literature. Initial results show that it is possible to codify the mechanical contact tactile information measured by multi-axial sensor systems in a bio-inspired fashion, thus reproducing relevant features similar to those produced by biological mechanoreceptors.

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A Soft Neuromorphic Approach for Contact Spatial Shape Sensing Based on Vision-Based Tactile Sensor

Wang

Yang

Zhou

et al. 2022

Intelligent Robotics and Applications

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Soft-neuromorphic artificial touch for applications in neuro-robotics

Cited by 32 publications

References 15 publications

Artificial spatiotemporal touch inputs reveal complementary decoding in neocortical neurons

Artificial spatiotemporal touch inputs reveal complementary decoding in neocortical neurons

A comprehensive mechanotransduction model for tactile feedback based on multi-axial stresses at the fingertip-contact interface

A Soft Neuromorphic Approach for Contact Spatial Shape Sensing Based on Vision-Based Tactile Sensor

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