scholarly journals Rapid geometric feature signaling in the simulated spiking activity of a complete population of tactile nerve fibers

2019 ◽  
Vol 121 (6) ◽  
pp. 2071-2082 ◽  
Author(s):  
Benoit P. Delhaye ◽  
Xinyue Xia ◽  
Sliman J. Bensmaia
Keyword(s):  
Feature Extraction ◽  
Spatial Pattern ◽  
Time Course ◽  
Object Manipulation ◽  
Nerve Fibers ◽  
Spatial Code ◽  
Neural Population ◽  
Geometric Features ◽  
Dexterous Manipulation ◽  
Edge Orientation

Tactile feature extraction is essential to guide the dexterous manipulation of objects. The longstanding theory is that geometric features at each location of contact between hand and object are extracted from the spatial layout of the response of populations of tactile nerve fibers. However, recent evidence suggests that some features (e.g., edge orientation) are extracted very rapidly (<200 ms), casting doubt that this information relies on a spatial code, which ostensibly requires integrating responses over time. An alternative hypothesis is that orientation is conveyed in precise temporal spiking patterns. Here we simulate, using a recently developed and validated model, the responses of the two relevant subpopulations of tactile fibers from the entire human fingertip (~800 afferents) to edges indented into the skin. We show that edge orientation can be quickly (<50 ms) and accurately (<3°) decoded from the spatial pattern of activation across the afferent population, starting with the very first spike. Next, we implement a biomimetic decoder of edge orientation, consisting of a bank of oriented Gabor filters, designed to mimic the documented responses of cortical neurons. We find that the biomimetic approach leads to orientation decoding performance that approaches the limit set by optimal decoders and is actually more robust to changes in other stimulus features. Finally, we show that orientation signals, measured from single units in the somatosensory cortex of nonhuman primates (2 macaque monkeys, 1 female), follow a time course consistent with that of their counterparts in the nerve. We conclude that a spatial code is fast and accurate enough to support object manipulation. NEW & NOTEWORTHY The dexterous manipulation of objects relies on the rapid and accurate extraction of the objects’ geometric features by the sense of touch. Here we simulate the responses of all the nerve fibers that innervate the fingertip when an edge is indented into the skin and characterize the time course over which signals about its orientation evolve in this neural population. We show that orientation can be rapidly and accurately decoded from the spatial pattern of afferent activation using spatial filters that mimic the response properties of neurons in cortical somatosensory neurons along a time course consistent with that observed in cortex. We conclude that the classical model of tactile feature extraction is rapid and accurate enough to support object manipulation.

scholarly journals Rapid geometric feature signaling in the spiking activity of a complete population of tactile nerve fibers

2018 ◽  
Author(s):  
Benoit P. Delhaye ◽  
Xinyue Xia ◽  
Sliman J. Bensmaia
Keyword(s):  
Cortical Neurons ◽  
Time Course ◽  
Alternative Hypothesis ◽  
Nerve Fibers ◽  
Spatial Code ◽  
Geometric Feature ◽  
Dexterous Manipulation ◽  
Edge Orientation ◽  
Biomimetic Approach ◽  
Stimulus Features

AbstractTactile feature extraction is essential to guide the dexterous manipulation of objects. The longstanding theory is that geometric features at each location of contact between hand and object are extracted from the spatial layout of the response of populations of tactile nerve fibers. However, recent evidence suggests that some features (edge orientation, e.g.) are extracted very rapidly (<200ms), casting doubt that this information relies on a spatial code, which ostensibly requires integrating responses over time. An alternative hypothesis is that orientation is conveyed in precise temporal spiking patterns. Here, we simulate, using a recently developed and validated model, the responses of tactile fibers from the entire human fingertip (∼800 afferents) to edges indented into the skin. We show that edge orientation can be quickly (<50 ms) and accurately (<3°) decoded from the spatial pattern of activation across the afferent population, starting with the very first spike. Next, we implement a biomimetic decoder of edge orientation, consisting of a bank of oriented Gabor filters, designed to mimic the documented responses of cortical neurons. We find that the biomimetic approach leads to orientation decoding performance that approaches the limit set by optimal decoders and is actually more robust to changes in other stimulus features. Finally, we show that orientation signals, measured from single units in non-human primate cortex (2 macaque monkeys, 1 female), follow a time course consistent with that of their counterparts in the nerve. We conclude that a spatial code is fast and accurate enough to support object manipulation.

scholarly journals Fast and accurate edge orientation processing during object manipulation

2017 ◽  
Author(s):  
J. Andrew Pruszynski ◽  
J. Randall Flanagan ◽  
Roland S. Johansson
Keyword(s):  
Object Manipulation ◽  
Spatial Processing ◽  
Processing Capacity ◽  
Critical Aspect ◽  
Dexterous Manipulation ◽  
Tactile Information ◽  
Edge Orientation ◽  
Manipulation Task ◽  
Tactile System ◽  
Better Than

AbstractQuickly and accurately extracting information about a touched object’s orientation is a critical aspect of dexterous object manipulation. However, the speed and acuity of tactile edge orientation processing with respect to the fingertips as reported in previous perceptual studies appear inadequate in these respects. Here we directly establish the tactile system’s capacity to process edge-orientation information during dexterous manipulation. Participants extracted tactile information about edge orientation very quickly, using it within 200 ms of first touching the object. Participants were also strikingly accurate. With edges spanning the entire fingertip, edge-orientation resolution was better than 3° in our object manipulation task, which is several times better than reported in previous perceptual studies. Performance remained impressive even with edges as short as 2 mm, consistent with our ability to precisely manipulate very small objects. Taken together, our results radically redefine the spatial processing capacity of the tactile system.

scholarly journals Fast and accurate edge orientation processing during object manipulation

eLife ◽  
2018 ◽  
Vol 7 ◽  
Author(s):  
J Andrew Pruszynski ◽  
J Randall Flanagan ◽  
Roland S Johansson
Keyword(s):  
Object Manipulation ◽  
Spatial Processing ◽  
Processing Capacity ◽  
Critical Aspect ◽  
Dexterous Manipulation ◽  
Tactile Information ◽  
Edge Orientation ◽  
Manipulation Task ◽  
Tactile System ◽  
Better Than

Quickly and accurately extracting information about a touched object’s orientation is a critical aspect of dexterous object manipulation. However, the speed and acuity of tactile edge orientation processing with respect to the fingertips as reported in previous perceptual studies appear inadequate in these respects. Here we directly establish the tactile system’s capacity to process edge-orientation information during dexterous manipulation. Participants extracted tactile information about edge orientation very quickly, using it within 200 ms of first touching the object. Participants were also strikingly accurate. With edges spanning the entire fingertip, edge-orientation resolution was better than 3° in our object manipulation task, which is several times better than reported in previous perceptual studies. Performance remained impressive even with edges as short as 2 mm, consistent with our ability to precisely manipulate very small objects. Taken together, our results radically redefine the spatial processing capacity of the tactile system.

OFFLINE YORÙBÁ HANDWRITTEN WORD RECOGNITION USING GEOMETRIC FEATURE EXTRACTION AND SUPPORT VECTOR MACHINE CLASSIFIER

2020 ◽  
Vol 5 (2) ◽  
pp. 504
Author(s):  
Matthias Omotayo Oladele ◽  
Temilola Morufat Adepoju ◽  
Olaide ` Abiodun Olatoke ◽  
Oluwaseun Adewale Ojo
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Word Recognition ◽  
Support Vector Machine Classifier ◽  
Recognition Accuracy ◽  
Recognition System ◽  
Support Vector ◽  
Geometric Features ◽  
Total Length ◽  
Yoruba Language

Yorùbá language is one of the three main languages that is been spoken in Nigeria. It is a tonal language that carries an accent on the vowel alphabets. There are twenty-five (25) alphabets in Yorùbá language with one of the alphabets a digraph (GB). Due to the difficulty in typing handwritten Yorùbá documents, there is a need to develop a handwritten recognition system that can convert the handwritten texts to digital format. This study discusses the offline Yorùbá handwritten word recognition system (OYHWR) that recognizes Yorùbá uppercase alphabets. Handwritten characters and words were obtained from different writers using the paint application and M708 graphics tablets. The characters were used for training and the words were used for testing. Pre-processing was done on the images and the geometric features of the images were extracted using zoning and gradient-based feature extraction. Geometric features are the different line types that form a particular character such as the vertical, horizontal, and diagonal lines. The geometric features used are the number of horizontal lines, number of vertical lines, number of right diagonal lines, number of left diagonal lines, total length of all horizontal lines, total length of all vertical lines, total length of all right slanting lines, total length of all left-slanting lines and the area of the skeleton. The characters are divided into 9 zones and gradient feature extraction was used to extract the horizontal and vertical components and geometric features in each zone. The words were fed into the support vector machine classifier and the performance was evaluated based on recognition accuracy. Support vector machine is a two-class classifier, hence a multiclass SVM classifier least square support vector machine (LSSVM) was used for word recognition. The one vs one strategy and RBF kernel were used and the recognition accuracy obtained from the tested words ranges between 66.7%, 83.3%, 85.7%, 87.5%, and 100%. The low recognition rate for some of the words could be as a result of the similarity in the extracted features.

scholarly journals Effects of Collagen–Glycosaminoglycan Mesh on Gene Expression as Determined by Using Principal Component Analysis-Based Unsupervised Feature Extraction

Polymers ◽  
2021 ◽  
Vol 13 (23) ◽  
pp. 4117
Author(s):  
Y-h. Taguchi ◽  
Turki Turki
Keyword(s):  
Gene Expression ◽  
Principal Component Analysis ◽  
Feature Extraction ◽  
Cell Lines ◽  
Time Course ◽  
Expression Profiles ◽  
Principal Component ◽  
Component Analysis ◽  
Altered Expression ◽  
Unsupervised Feature Extraction

The development of the medical applications for substances or materials that contact cells is important. Hence, it is necessary to elucidate how substances that surround cells affect gene expression during incubation. In the current study, we compared the gene expression profiles of cell lines that were in contact with collagen–glycosaminoglycan mesh and control cells. Principal component analysis-based unsupervised feature extraction was applied to identify genes with altered expression during incubation in the treated cell lines but not in the controls. The identified genes were enriched in various biological terms. Our method also outperformed a conventional methodology, namely, gene selection based on linear regression with time course.

scholarly journals Hand forces and placement are modulated and covary during anticipatory control of bimanual manipulation

2019 ◽  
Vol 121 (6) ◽  
pp. 2276-2290
Author(s):  
Trevor Lee-Miller ◽  
Marco Santello ◽  
Andrew M. Gordon
Keyword(s):  
General Feature ◽  
Center Of Mass ◽  
Object Manipulation ◽  
Anticipatory Control ◽  
Dexterous Manipulation ◽  
Specific Feedback ◽  
Lift Off ◽  
Centers Of Mass ◽  
High Level ◽  
Hand Forces

Dexterous object manipulation relies on the feedforward and feedback control of kinetics (forces) and kinematics (hand shaping and digit placement). Lifting objects with an uneven mass distribution involves the generation of compensatory moments at object lift-off to counter object torques. This is accomplished through the modulation and covariation of digit forces and placement, which has been shown to be a general feature of unimanual manipulation. These feedforward anticipatory processes occur before performance-specific feedback. Whether this adaptation is a feature unique to unimanual dexterous manipulation or general across unimanual and bimanual manipulation is not known. We investigated the generation of compensatory moments through hand placement and force modulation during bimanual manipulation of an object with variable center of mass. Participants were instructed to prevent object roll during the lift. Similar to unimanual grasping, we found modulation and covariation of hand forces and placement for successful performance. Thus this motor adaptation of the anticipatory control of compensatory moment is a general feature across unimanual and bimanual effectors. Our results highlight the involvement of high-level representation of manipulation goals and underscore a sensorimotor circuitry for anticipatory control through a continuum of force and placement modulation of object manipulation across a range of effectors. NEW & NOTEWORTHY This is the first study, to our knowledge, to show that successful bimanual manipulation of objects with asymmetrical centers of mass is performed through the modulation and covariation of hand forces and placements to generate compensatory moments. Digit force-to-placement modulation is thus a general phenomenon across multiple effectors, such as the fingers of one hand, and both hands. This adds to our understanding of integrating low-level internal representations of object properties into high-level task representations.

Dendritic origin of late events in optical recordings from salamander olfactory bulb

1992 ◽  
Vol 68 (3) ◽  
pp. 786-806 ◽  
Author(s):  
A. R. Cinelli ◽  
B. M. Salzberg
Keyword(s):  
Olfactory Bulb ◽  
Time Course ◽  
Slow Component ◽  
Field Potential ◽  
Olfactory Nerve ◽  
Nerve Fibers ◽  
Dependent Manner ◽  
Gamma Aminobutyric Acid ◽  
Direct Stimulation ◽  
Compound Action

1. Optical recordings of membrane-potential changes were used to characterize the origin and properties of the electrical signals from the dendritic level in slices of the salamander olfactory bulb. 2. The optical events were correlated with field-potential waves recorded simultaneously. Both responses exhibited patterns similar to those found in other species. 3. Orthodromic stimulation evoked a compound action potential in the olfactory nerve fibers, followed by two additional principal waves (N1 and N2). These field-potential waves reflected excitatory postsynaptic potentials at the primary mitral/tufted and granule cell dendrites, respectively. 4. Extrinsic optical signals from horizontal slices stained with the pyrazo-oxonal dye RH-155 showed a characteristic sequence of depolarizing and hyperpolarizing events. All of the signals exhibited a wavelength dependence expected for this dye and were abolished in the presence of high K+ in the bath. 5. According to their time courses, depolarizing responses under normal recording conditions were divided into two components, fast and slow. Orthodromic stimuli evoked a fast presynaptic response that represents synchronous compound action potentials from olfactory nerve fibers. At subglomerular levels, additional fast responses could often be recorded at the peri/subglomerular level and in the mitral/tufted somata region. These postsynaptic responses partially coincided with the rising phase of a different depolarizing signal, a slow component characterized by its prolonged time course. 6. With orthodromic stimulation, this slow signal attained its largest amplitude in the zone between the glomeruli and the superficial part of the external plexiform layer (EPL). Antidromic stimuli evoked a signal with some similarities to the one evoked orthodromically, but originating in deeper EPL regions. 7. Slow components were characterized by their Ca dependence. Low Ca2+ medium, or calcium channel blockers, suppressed this optical component, whether evoked orthodromically, antidromically, or by direct stimulation. In addition, Ba2+ (2.5–3.6 mM) in the bath did not abolish these responses, suggesting that they do not reflect a glial depolarization in response to elevated extracellular K+ concentration ([K+]o). 8. Locally applied stimuli next to the glomerular layer elicited these signals in 5–10 microM tetrodotoxin (TTX) or in low extracellular Na+ concentration ([Na+]o) medium, but antidromic or orthodromic stimuli failed to evoke the response under these conditions. The sizes of the responses to local stimuli remained constant, but an increase in their duration was observed in either TTX or low [Na+]o. 9. gamma-Aminobutyric acid (GABA) and baclofen reduced the size of the slow components in a dose-dependent manner.(ABSTRACT TRUNCATED AT 400 WORDS)

Feature Extraction I: Geometric Features and Transformations

2021 ◽  
pp. 165-194
Author(s):  
Abhijit S. Pandya ◽  
Robert B. Macy
Keyword(s):  
Feature Extraction ◽  
Geometric Features

scholarly journals Feature Extraction from 3D Point Cloud Data Based on Discrete Curves

2013 ◽  
Vol 2013 ◽  
pp. 1-19 ◽  
Author(s):  
Yi An ◽  
Zhuohan Li ◽  
Cheng Shao
Keyword(s):  
Feature Extraction ◽  
Autonomous Navigation ◽  
Point Cloud ◽  
Geometric Features ◽  
3D Point Cloud ◽  
Point Cloud Data ◽  
Principal Curvatures ◽  
Cloud Data ◽  
Discrete Curves ◽  
Threshold Setting

Reliable feature extraction from 3D point cloud data is an important problem in many application domains, such as reverse engineering, object recognition, industrial inspection, and autonomous navigation. In this paper, a novel method is proposed for extracting the geometric features from 3D point cloud data based on discrete curves. We extract the discrete curves from 3D point cloud data and research the behaviors of chord lengths, angle variations, and principal curvatures at the geometric features in the discrete curves. Then, the corresponding similarity indicators are defined. Based on the similarity indicators, the geometric features can be extracted from the discrete curves, which are also the geometric features of 3D point cloud data. The threshold values of the similarity indicators are taken from[0,1], which characterize the relative relationship and make the threshold setting easier and more reasonable. The experimental results demonstrate that the proposed method is efficient and reliable.

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