Compression of Dynamic Tactile Information in the Human Hand

Shao, Yitian

doi:10.1007/978-3-030-90839-3_4

Cited by 9 publications

(8 citation statements)

References 55 publications

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“…This likely arises from the mechanical attributes of the hand, which induces a ripple of vibration following even very localized touch, which will, in turn, cause a specific spatiotemporal pattern of mechanoreceptor activity across the entire hand (see also Fig. 4A) ( 6 , 8 ). At the cortical level, receptive fields can be complex and integrate information across a variety of receptor types ( 5 , 45 ).…”

Section: Discussionmentioning

confidence: 99%

“…Shared interfinger representation may be the result of input synchronization: Manfredi and colleagues (6) have demonstrated that even passive stimulation of a part of the hand triggers extensive ripple effects across the skin [see also (7)]. Because these ripples activate mechanoreceptors on other fingers, even localized tactile stimulation elicits widely distributed peripheral responses, with distinct spatiotemporal patterns (8). Together with related findings characterizing the organizational features of the S1 hand map that are not necessarily anchored to topographic maps (9)(10)(11)(12), these results suggest that shared somatosensory processing of inputs from multiple skin surfaces across the hand is more prevalent than typically appreciated.…”

Section: Introductionmentioning

confidence: 99%

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Malleability of the cortical hand map following a finger nerve block

et al. 2022

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Electrophysiological studies in monkeys show that finger amputation triggers local remapping within the deprived primary somatosensory cortex (S1). Human neuroimaging research, however, shows persistent S1 representation of the missing hand’s fingers, even decades after amputation. Here, we explore whether this apparent contradiction stems from underestimating the distributed peripheral and central representation of fingers in the hand map. Using pharmacological single-finger nerve block and 7-tesla neuroimaging, we first replicated previous accounts (electrophysiological and other) of local S1 remapping. Local blocking also triggered activity changes to nonblocked fingers across the entire hand area. Using methods exploiting interfinger representational overlap, however, we also show that the blocked finger representation remained persistent despite input loss. Computational modeling suggests that both local stability and global reorganization are driven by distributed processing underlying the topographic map, combined with homeostatic mechanisms. Our findings reveal complex interfinger representational features that play a key role in brain (re)organization, beyond (re)mapping.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Malleability of the cortical hand map following a finger nerve block

et al. 2022

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“…In contrast to SA1 and RA afferents, the information level for PC afferents was essentially constant for all density values considered across all tactile features. While this result might be taken to suggest that the PC population does not contribute information above that of a single afferent, there is evidence to suggest that PC populations might be important in different tactile contexts than the ones explored here: making contact with surfaces causes mechanical waves to spread throughout the hand, activating PC afferents as far away as the palm and their joint population activity carries information about how contact is made and other aspects of the grasp (34).…”

Section: Discussionmentioning

confidence: 85%

Population coding strategies in human tactile afferents

Corniani

Casal

Panzeri

et al. 2022

Preprint

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Sensory information is conveyed by populations of neurons, and coding strategies cannot always be deduced when considering individual neurons. Moreover, information coding depends on the number of neurons available and on the composition of the population when multiple classes with different response properties are available. Here, we study population coding in human tactile afferents by employing a recently developed simulator of mechanoreceptor firing activity. First, we demonstrate that the optimal afferent density for conveying maximal information depends on the tactile feature under consideration and the afferent class coding this feature. Second, we find that information is spread across different classes for all tactile features, such that combining information from multiple afferent classes improves information transmission, and is often more efficient than increasing the density of afferents from the same class. Finally, we test the importance of timing precision and afferent identity in the population code to probe whether temporal and spatial information can be traded against each other. Destroying temporal information turns out to be more destructive than removing spatial information, and the contribution of either cannot be completely recovered from the other. Overall, our results suggest that both optimal afferent innervation densities and the composition of the population depend in complex ways on the tactile features in question, potentially accounting for the variety in which tactile peripheral populations are assembled in different regions across the body.

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“…The sparsity condition ensures that the information is embedded in a population code with a minimum number of neurons active at any one time, leading to a more than 20-fold compression of images or audio waveforms without losing perceptual accuracy 25 . Similar efficient coding strategies have been observed in touch, and facilitate the classification of hand gestures from vibrotactile surface wave propagation 26 or to identify material properties from the vibrotactile signal they produce 27 .…”

Section: Efficient Coding Hypothesismentioning

confidence: 78%

Efficient Tactile Encoding of Object Slippage

Willemet

Huloux

Wiertlewski

2022

Preprint

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Humans exhibit a remarkably robust reaction to external perturbations that prevent dropping objects held in hand, using only tactile inputs. In less than 200 ms, the sensorimotor system processes tactile information stemming from the deformation of the skin, to determine the frictional strength of the contact and to react accordingly. Given the thousands of afferents innervating the fingertips, it is unclear how the nervous system can process such a large influx of data in a sufficiently short time span. In this study, we measured the deformation of the skin during the initial stages of incipient sliding for a wide range of frictional conditions. We show that the dominant patterns of deformation are sufficient to estimate the distance between the frictional force and the frictional strength of the contact. From these stereotypical patterns, a classifier is able to predict if an object is about to slide during the initial stages of incipient slip. The prediction is robust to the actual value of the interfacial friction, showing sensory invariance. These results suggest that the nervous system efficiently encodes tactile information by projecting the measured deformation of the skin onto a compact basis of deformation patterns, that we call Eigenstrains. Our findings suggest that only 6 of these Eigenstrains are necessary to classify the slippage sensed by tens of thousands of afferents. These findings are relevant to the understanding of the unconscious regulation of grasp, and the insights are directly applicable to the design of robotic grippers and prosthetics that rapidly react to external perturbations.

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Compression of Dynamic Tactile Information in the Human Hand

Cited by 9 publications

References 55 publications

Malleability of the cortical hand map following a finger nerve block

Malleability of the cortical hand map following a finger nerve block

Population coding strategies in human tactile afferents

Efficient Tactile Encoding of Object Slippage

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