scholarly journals Speed invariance of tactile texture perception

2017 ◽  
Vol 118 (4) ◽  
pp. 2371-2377 ◽  
Author(s):  
Zoe M. Boundy-Singer ◽  
Hannes P. Saal ◽  
Sliman J. Bensmaia
Keyword(s):  
Activity Patterns ◽  
Strong Dependence ◽  
Scanning Speed ◽  
Nerve Fibers ◽  
Neural Mechanisms ◽  
Neural Representation ◽  
Perceptual Constancy ◽  
Texture Perception ◽  
Wide Range ◽  
Sense Of Touch

The nervous system achieves stable perceptual representations of objects despite large variations in the activity patterns of sensory receptors. Here, we explore perceptual constancy in the sense of touch. Specifically, we investigate the invariance of tactile texture perception across changes in scanning speed. Texture signals in the nerve have been shown to be highly dependent on speed: temporal spiking patterns in nerve fibers that encode fine textural features contract or dilate systematically with increases or decreases in scanning speed, respectively, resulting in concomitant changes in response rate. Nevertheless, texture perception has been shown, albeit with restricted stimulus sets and limited perceptual assays, to be independent of scanning speed. Indeed, previous studies investigated the effect of scanning speed on perceived roughness, only one aspect of texture, often with impoverished stimuli, namely gratings and embossed dot patterns. To fill this gap, we probe the perceptual constancy of a wide range of textures using two different paradigms: one that probes texture perception along well-established sensory dimensions independently and one that probes texture perception as a whole. We find that texture perception is highly stable across scanning speeds, irrespective of the texture or the perceptual assay. Any speed-related effects are dwarfed by differences in percepts evoked by different textures. This remarkable speed invariance of texture perception stands in stark contrast to the strong dependence of the texture responses of nerve fibers on scanning speed. Our results imply neural mechanisms that compensate for scanning speed to achieve stable representations of surface texture. NEW & NOTEWORTHY Our brain forms stable representations of objects regardless of viewpoint, a phenomenon known as invariance that has been described in several sensory modalities. Here, we explore invariance in the sense of touch and show that the tactile perception of texture does not depend on scanning speed. This perceptual constancy implies neural mechanisms that extract information about texture from the response of nerve fibers such that the resulting neural representation is stable across speeds.

scholarly journals Emergence of an invariant representation of texture in primate somatosensory cortex

2019 ◽  
Author(s):  
Justin D. Lieber ◽  
Sliman J. Bensmaia
Keyword(s):  
Cortical Neurons ◽  
Rhesus Macaques ◽  
Scanning Speed ◽  
Nerve Fibers ◽  
Invariant Representation ◽  
Major Function ◽  
Neural Representations ◽  
Wide Range ◽  
Sensory Modalities ◽  
Exploratory Movements

ABSTRACTA major function of sensory processing is to achieve neural representations of objects that are stable across changes in context and perspective. Small changes in exploratory behavior can lead to large changes in signals at the sensory periphery, thus resulting in ambiguous neural representations of objects. Overcoming this ambiguity is a hallmark of human object recognition across sensory modalities. Here, we investigate how the perception of tactile texture remains stable across exploratory movements of the hand, including changes in scanning speed, despite the concomitant changes in afferent responses. To this end, we scanned a wide range of everyday textures across the fingertips of Rhesus macaques at multiple speeds and recorded the responses evoked in tactile nerve fibers and somatosensory cortical neurons. We found that individual cortical neurons exhibit a wider range of speed-sensitivities than do nerve fibers. The resulting representations of speed and texture in cortex are more independent than are their counterparts in the nerve and account for speed-invariant perception of texture. We demonstrate that this separation of speed and texture information is a natural consequence of previously described cortical computations.

Neural Mechanisms of Tactile Texture Perception

2019 ◽  
Author(s):  
Justin D. Lieber ◽  
Sliman J. Bensmaia
Keyword(s):  
Spatial Patterns ◽  
Material Properties ◽  
Nerve Fibers ◽  
Neural Mechanisms ◽  
High Dimensional ◽  
Surface Microstructure ◽  
Perceptual Space ◽  
Dimensional Representation ◽  
Texture Perception ◽  
First Approximation

The ability to identify tactile objects depends in part on the perception of their surface microstructure and material properties. Texture perception can, on a first approximation, be described by a number of nameable perceptual axes, such as rough/smooth, hard/soft, sticky/slippery, and warm/cool, which exist within a complex perceptual space. The perception of texture relies on two different neural streams of information: Coarser features, measured in millimeters, are primarily encoded by spatial patterns of activity across one population of tactile nerve fibers, while finer features, down to the micron level, are encoded by finely timed temporal patterns within two other populations of afferents. These two streams of information ascend the somatosensory neuraxis and are eventually combined and further elaborated in the cortex to yield a high-dimensional representation that accounts for our exquisite and stable perception of texture.

scholarly journals Emergence of an Invariant Representation of Texture in Primate Somatosensory Cortex

2019 ◽  
Vol 30 (5) ◽  
pp. 3228-3239 ◽  
Author(s):  
Justin D Lieber ◽  
Sliman J Bensmaia
Keyword(s):  
Cortical Neurons ◽  
Rhesus Macaques ◽  
Scanning Speed ◽  
Nerve Fibers ◽  
Invariant Representation ◽  
Major Function ◽  
Neural Representations ◽  
Wide Range ◽  
Brodmann Areas ◽  
Exploratory Movements

Abstract A major function of sensory processing is to achieve neural representations of objects that are stable across changes in context and perspective. Small changes in exploratory behavior can lead to large changes in signals at the sensory periphery, thus resulting in ambiguous neural representations of objects. Overcoming this ambiguity is a hallmark of human object recognition across sensory modalities. Here, we investigate how the perception of tactile texture remains stable across exploratory movements of the hand, including changes in scanning speed, despite the concomitant changes in afferent responses. To this end, we scanned a wide range of everyday textures across the fingertips of rhesus macaques at multiple speeds and recorded the responses evoked in tactile nerve fibers and somatosensory cortical neurons (from Brodmann areas 3b, 1, and 2). We found that individual cortical neurons exhibit a wider range of speed-sensitivities than do nerve fibers. The resulting representations of speed and texture in cortex are more independent than are their counterparts in the nerve and account for speed-invariant perception of texture. We demonstrate that this separation of speed and texture information is a natural consequence of previously described cortical computations.

scholarly journals A Reinforcement Learning Framework for Spiking Networks with Dynamic Synapses

2011 ◽  
Vol 2011 ◽  
pp. 1-12 ◽  
Author(s):  
Karim El-Laithy ◽  
Martin Bogdan
Keyword(s):  
Reinforcement Learning ◽  
Spike Timing ◽  
Neural Representation ◽  
Model Parameters ◽  
Learning Framework ◽  
Reference Target ◽  
Wide Range ◽  
Spiking Network ◽  
Dynamic Synapses ◽  
Exclusive Or

An integration of both the Hebbian-based and reinforcement learning (RL) rules is presented for dynamic synapses. The proposed framework permits the Hebbian rule to update the hidden synaptic model parameters regulating the synaptic response rather than the synaptic weights. This is performed using both the value and the sign of the temporal difference in the reward signal after each trial. Applying this framework, a spiking network with spike-timing-dependent synapses is tested to learn the exclusive-OR computation on a temporally coded basis. Reward values are calculated with the distance between the output spike train of the network and a reference target one. Results show that the network is able to capture the required dynamics and that the proposed framework can reveal indeed an integrated version of Hebbian and RL. The proposed framework is tractable and less computationally expensive. The framework is applicable to a wide class of synaptic models and is not restricted to the used neural representation. This generality, along with the reported results, supports adopting the introduced approach to benefit from the biologically plausible synaptic models in a wide range of intuitive signal processing.

scholarly journals Early Dropout Prediction in MOOCs through Supervised Learning and Hyperparameter Optimization

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1701
Author(s):  
Theodor Panagiotakopoulos ◽  
Sotiris Kotsiantis ◽  
Georgios Kostopoulos ◽  
Omiros Iatrellis ◽  
Achilles Kameas
Keyword(s):  
Online Education ◽  
Online Courses ◽  
Early Stage ◽  
Activity Patterns ◽  
Daily Basis ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Educational Institutions ◽  
Student Dropout ◽  
Wide Range

Over recent years, massive open online courses (MOOCs) have gained increasing popularity in the field of online education. Students with different needs and learning specificities are able to attend a wide range of specialized online courses offered by universities and educational institutions. As a result, large amounts of data regarding students’ demographic characteristics, activity patterns, and learning performances are generated and stored in institutional repositories on a daily basis. Unfortunately, a key issue in MOOCs is low completion rates, which directly affect student success. Therefore, it is of utmost importance for educational institutions and faculty members to find more effective practices and reduce non-completer ratios. In this context, the main purpose of the present study is to employ a plethora of state-of-the-art supervised machine learning algorithms for predicting student dropout in a MOOC for smart city professionals at an early stage. The experimental results show that accuracy exceeds 96% based on data collected during the first week of the course, thus enabling effective intervention strategies and support actions.

Temporal Properties of Responses to Broadband Noise in the Auditory Nerve

2004 ◽  
Vol 91 (5) ◽  
pp. 2051-2065 ◽  
Author(s):  
Dries H. G. Louage ◽  
Marcel van der Heijden ◽  
Philip X. Joris
Keyword(s):  
Fourier Analysis ◽  
Auditory Nerve ◽  
Phase Locking ◽  
Strong Dependence ◽  
Peak Height ◽  
Nerve Fibers ◽  
Broadband Noise ◽  
Temporal Information ◽  
Temporal Behavior ◽  
Locking Range

Temporal information in the responses of auditory neurons to sustained sounds has been studied mostly with periodic stimuli, using measures that are based on Fourier analysis. Less information is available on temporal aspects of responses to nonperiodic wideband sounds. We recorded responses to a reference Gaussian noise and its polarity-inverted version in the auditory nerve of barbiturate-anesthetized cats and used shuffled autocorrelograms (SACs) to quantify spike timing. Two metrics were extracted from the central peak of autocorrelograms: the peak-height and the width at halfheight. Temporal information related to stimulus fine-structure was isolated from that to envelope by subtracting or adding responses to the reference and inverted noise. Peak-height and halfwidth generally behaved as expected from the existing body of data on phase-locking to pure tones and sinusoidally amplitude-modulated tones but showed some surprises as well. Compared with synchronization to low-frequency tones, SACs reveal large differences in temporal behavior between the different classes of nerve fibers (based on spontaneous rate) as well as a strong dependence on characteristic frequency (CF) throughout the phase-locking range. SACs also reveal a larger temporal consistency (i.e., tendency to discharge at the same point in time on repeated presentation of the same stimulus) in the responses to the stochastic noise stimulus than in the responses to periodic tones. Responses at high CFs reflect envelope phase-locking and are consistent with previous reports using sinusoidal AM. We conclude that the combined use of broadband noise and SAC analysis allow a more general characterization of temporal behavior than periodic stimuli and Fourier analysis.

Weather effects on intrahabitat movements of the western green lizard, Lacerta bilineata (Daudin, 1802), at its northern distribution range border: a radio-tracking study

2000 ◽  
Vol 78 (10) ◽  
pp. 1831-1839 ◽  
Author(s):  
P Sound ◽  
M Veith
Keyword(s):  
Activity Patterns ◽  
Strong Dependence ◽  
Weather Conditions ◽  
Distribution Range ◽  
Maximum Temperature ◽  
Second Phase ◽  
Northern Distribution ◽  
Radio Transmitters ◽  
Movement Parameters ◽  
Bad Weather

Daily activity patterns of male western green lizards, Lacerta bilineata (Daudin, 1802), at the edge of their northern distribution range in western Germany after the breeding season from June to October were recorded using implanted radio transmitters. Different activity indices discriminating between stimulation, duration, and length of movement were correlated with actual weather conditions (d0) and with weather conditions on the 2 previous days (d-1 and d-2). The lizards' dependence on weather showed two different phases throughout the study period. During the first period and in the period preceding a drastic change of weather in midsummer, weather had no significant influence on movement parameters. After that event, temperatures dropped and a strong dependence on weather of all movement parameters except those indicating displacements became apparent. Thresholds for 50% activity during this second phase were a maximum temperature of 17°C and a minimum humidity of 35%. Two days after periods of bad weather, the influence of weather conditions increased again. This can be explained by physiological deficits that require compensation during the period of marginal weather conditions prior to hibernation. Displacement movements were significantly longer than home-range movements and were neither triggered nor modulated by the weather. They must therefore represent activities such as patrolling territory boundaries.

scholarly journals Hyaluronan and the Fascial Frontier

2021 ◽  
Vol 22 (13) ◽  
pp. 6845
Author(s):  
Rebecca L. Pratt
Keyword(s):  
Nerve Fibers ◽  
The Body ◽  
Water Volume ◽  
Cell Behavior ◽  
Connective Tissues ◽  
Wide Range ◽  
The World ◽  
Extracellular Microenvironment ◽  
The Impact ◽  
The Relationship

The buzz about hyaluronan (HA) is real. Whether found in face cream to increase water volume loss and viscoelasticity or injected into the knee to restore the properties of synovial fluid, the impact of HA can be recognized in many disciplines from dermatology to orthopedics. HA is the most abundant polysaccharide of the extracellular matrix of connective tissues. HA can impact cell behavior in specific ways by binding cellular HA receptors, which can influence signals that facilitate cell survival, proliferation, adhesion, as well as migration. Characteristics of HA, such as its abundance in a variety of tissues and its responsiveness to chemical, mechanical and hormonal modifications, has made HA an attractive molecule for a wide range of applications. Despite being discovered over 80 years ago, its properties within the world of fascia have only recently received attention. Our fascial system penetrates and envelopes all organs, muscles, bones and nerve fibers, providing the body with a functional structure and an environment that enables all bodily systems to operate in an integrated manner. Recognized interactions between cells and their HA-rich extracellular microenvironment support the importance of studying the relationship between HA and the body’s fascial system. From fasciacytes to chronic pain, this review aims to highlight the connections between HA and fascial health.

The C-start escape response ofPolypterus senegalus: bilateral muscle activity and variation during stage 1 and 2

2002 ◽  
Vol 205 (17) ◽  
pp. 2591-2603 ◽  
Author(s):  
Eric D. Tytell ◽  
George V. Lauder
Keyword(s):  
Muscle Activity ◽  
Escape Response ◽  
High Speed ◽  
Wave Speed ◽  
Activity Patterns ◽  
Motor Pattern ◽  
The Body ◽  
Wide Range ◽  
Fast Start ◽  
Stage 1

SUMMARYThe fast-start escape response is the primary reflexive escape mechanism in a wide phylogenetic range of fishes. To add detail to previously reported novel muscle activity patterns during the escape response of the bichir, Polypterus, we analyzed escape kinematics and muscle activity patterns in Polypterus senegalus using high-speed video and electromyography (EMG). Five fish were filmed at 250 Hz while synchronously recording white muscle activity at five sites on both sides of the body simultaneously (10 sites in total). Body wave speed and center of mass velocity, acceleration and curvature were calculated from digitized outlines. Six EMG variables per channel were also measured to characterize the motor pattern. P. senegalus shows a wide range of activity patterns, from very strong responses, in which the head often touched the tail, to very weak responses. This variation in strength is significantly correlated with the stimulus and is mechanically driven by changes in stage 1 muscle activity duration. Besides these changes in duration, the stage 1 muscle activity is unusual because it has strong bilateral activity, although the observed contralateral activity is significantly weaker and shorter in duration than ipsilateral activity. Bilateral activity may stiffen the body, but it does so by a constant amount over the variation we observed; therefore, P. senegalus does not modulate fast-start wave speed by changing body stiffness. Escape responses almost always have stage 2 contralateral muscle activity, often only in the anterior third of the body. The magnitude of the stage 2 activity is the primary predictor of final escape velocity.

scholarly journals Harmonic Brain Modes: A Unifying Framework for Linking Space and Time in Brain Dynamics

2017 ◽  
Vol 24 (3) ◽  
pp. 277-293 ◽  
Author(s):  
Selen Atasoy ◽  
Gustavo Deco ◽  
Morten L. Kringelbach ◽  
Joel Pearson
Keyword(s):  
Neural Activity ◽  
Brain Activity ◽  
Activity Patterns ◽  
Structural Connectivity ◽  
Building Blocks ◽  
Mental States ◽  
Brain Dynamics ◽  
Space And Time ◽  
Wide Range ◽  
The Brain

A fundamental characteristic of spontaneous brain activity is coherent oscillations covering a wide range of frequencies. Interestingly, these temporal oscillations are highly correlated among spatially distributed cortical areas forming structured correlation patterns known as the resting state networks, although the brain is never truly at “rest.” Here, we introduce the concept of harmonic brain modes—fundamental building blocks of complex spatiotemporal patterns of neural activity. We define these elementary harmonic brain modes as harmonic modes of structural connectivity; that is, connectome harmonics, yielding fully synchronous neural activity patterns with different frequency oscillations emerging on and constrained by the particular structure of the brain. Hence, this particular definition implicitly links the hitherto poorly understood dimensions of space and time in brain dynamics and its underlying anatomy. Further we show how harmonic brain modes can explain the relationship between neurophysiological, temporal, and network-level changes in the brain across different mental states ( wakefulness, sleep, anesthesia, psychedelic). Notably, when decoded as activation of connectome harmonics, spatial and temporal characteristics of neural activity naturally emerge from the interplay between excitation and inhibition and this critical relation fits the spatial, temporal, and neurophysiological changes associated with different mental states. Thus, the introduced framework of harmonic brain modes not only establishes a relation between the spatial structure of correlation patterns and temporal oscillations (linking space and time in brain dynamics), but also enables a new dimension of tools for understanding fundamental principles underlying brain dynamics in different states of consciousness.

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