scholarly journals Neural coordination during reach-to-grasp

2015 ◽  
Vol 114 (3) ◽  
pp. 1827-1836 ◽  
Author(s):  
Mukta Vaidya ◽  
Konrad Kording ◽  
Maryam Saleh ◽  
Kazutaka Takahashi ◽  
Nicholas G. Hatsopoulos
Keyword(s):  
Cortical Neurons ◽  
Coordination Dynamics ◽  
Reach To Grasp ◽  
Neuronal Ensembles ◽  
Reach And Grasp ◽  
Dimensional State Space ◽  
Catch Up ◽  
Low Dimensional ◽  
Bidirectional Interactions ◽  
New Framework

When reaching to grasp, we coordinate how we preshape the hand with how we move it. To ask how motor cortical neurons participate in this coordination, we examined the interactions between reach- and grasp-related neuronal ensembles while monkeys reached to grasp a variety of different objects in different locations. By describing the dynamics of these two ensembles as trajectories in a low-dimensional state space, we examined their coupling in time. We found evidence for temporal compensation across many different reach-to-grasp conditions such that if one neural trajectory led in time the other tended to catch up, reducing the asynchrony between the trajectories. Granger causality revealed bidirectional interactions between reach and grasp neural trajectories beyond that which could be attributed to the joint kinematics that were consistently stronger in the grasp-to-reach direction. Characterizing cortical coordination dynamics provides a new framework for understanding the functional interactions between neural populations.

scholarly journals Information diversity in individual auditory cortical neurons is associated with functionally distinct coordinated neuronal ensembles

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jermyn Z. See ◽  
Natsumi Y. Homma ◽  
Craig A. Atencio ◽  
Vikaas S. Sohal ◽  
Christoph E. Schreiner
Keyword(s):  
Cortical Neurons ◽  
Single Neuron ◽  
Receptive Fields ◽  
Acoustic Feature ◽  
Neuronal Ensembles ◽  
Neuron Spike ◽  
Information Diversity ◽  
Feature Selectivity ◽  
Stimulus Features ◽  
Time Specific

AbstractNeuronal activity in auditory cortex is often highly synchronous between neighboring neurons. Such coordinated activity is thought to be crucial for information processing. We determined the functional properties of coordinated neuronal ensembles (cNEs) within primary auditory cortical (AI) columns relative to the contributing neurons. Nearly half of AI cNEs showed robust spectro-temporal receptive fields whereas the remaining cNEs showed little or no acoustic feature selectivity. cNEs can therefore capture either specific, time-locked information of spectro-temporal stimulus features or reflect stimulus-unspecific, less-time specific processing aspects. By contrast, we show that individual neurons can represent both of those aspects through membership in multiple cNEs with either high or absent feature selectivity. These associations produce functionally heterogeneous spikes identifiable by instantaneous association with different cNEs. This demonstrates that single neuron spike trains can sequentially convey multiple aspects that contribute to cortical processing, including stimulus-specific and unspecific information.

scholarly journals Multiscale low-dimensional motor cortical state dynamics predict naturalistic reach-and-grasp behavior

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Hamidreza Abbaspourazad ◽  
Mahdi Choudhury ◽  
Yan T. Wong ◽  
Bijan Pesaran ◽  
Maryam M. Shanechi
Keyword(s):  
Neural Control ◽  
Multiple Scales ◽  
Network Activity ◽  
Neural Dynamics ◽  
Population Activity ◽  
Reach And Grasp ◽  
Spatiotemporal Scales ◽  
Motor Cortical ◽  
Low Dimensional ◽  
Low Dimensional Dynamics

AbstractMotor function depends on neural dynamics spanning multiple spatiotemporal scales of population activity, from spiking of neurons to larger-scale local field potentials (LFP). How multiple scales of low-dimensional population dynamics are related in control of movements remains unknown. Multiscale neural dynamics are especially important to study in naturalistic reach-and-grasp movements, which are relatively under-explored. We learn novel multiscale dynamical models for spike-LFP network activity in monkeys performing naturalistic reach-and-grasps. We show low-dimensional dynamics of spiking and LFP activity exhibited several principal modes, each with a unique decay-frequency characteristic. One principal mode dominantly predicted movements. Despite distinct principal modes existing at the two scales, this predictive mode was multiscale and shared between scales, and was shared across sessions and monkeys, yet did not simply replicate behavioral modes. Further, this multiscale mode’s decay-frequency explained behavior. We propose that multiscale, low-dimensional motor cortical state dynamics reflect the neural control of naturalistic reach-and-grasp behaviors.

scholarly journals Grasping Social Apathy: The Role of Reach-To-Grasp Action Kinematics for the Assessment of Social Apathy in Mild Neurocognitive Disorders

2021 ◽  
pp. 1-14
Author(s):  
Valeria Manera ◽  
Guenda Galperti ◽  
Erika Rovini ◽  
Radia Zeghari ◽  
Gianmaria Mancioppi ◽  
...  
Keyword(s):  
Neurocognitive Disorders ◽  
Reach To Grasp ◽  
Future Studies ◽  
Clinical Scales ◽  
Non Invasive ◽  
Reach And Grasp ◽  
The Social ◽  
Self Reports ◽  
Individual Condition

Background: Social apathy, a reduction in initiative in proposing or engaging in social activities or interactions, is common in mild neurocognitive disorders (MND). Current apathy assessment relies on self-reports or clinical scales, but growing attention is devoted to defining more objective, measurable and non-invasive apathy proxies. Objective: In the present study we investigated the interest of recording action kinematics in a social reach-to-grasp task for the assessment of social apathy. Methods: Thirty participants took part in the study: 11 healthy controls (HC; 6 females, mean age = 68.3±10.5 years) and 19 subjects with MND (13 females, mean age = 75.7±6.3 years). Based on the Diagnostic Criteria for Apathy, MND subjects were classified as socially apathetic (A-MND, N = 9) versus non-apathetic (NA-MND, N = 10). SensRing, a ring-shaped wearable sensor, was placed on their index finger, and subjects were asked to reach and grasp a can to place it into a cup (individual condition) and pass it to a partner (social condition). Results: In the reach-to-grasp phase of the action, HC and NA-MND showed different acceleration and velocity profiles in the social versus individual condition. No differences were found for A-MND. Conclusion: Previous studies showed the interest of recording patients’ level of weekly motor activity for apathy assessment. Here we showed that a 10-min reach-to-grasp task may provide information to differentiate socially apathetic and non-apathetic subjects with MND, thus providing a tool easily usable in the clinical practice. Future studies with a bigger sample are needed to better characterize these findings.

scholarly journals Electromyography Classification during Reach-to-Grasp Motion using Manifold Learning

2020 ◽  
Author(s):  
Elnaz Lashgari ◽  
Uri Maoz
Keyword(s):  
Dimensionality Reduction ◽  
Cost Effective ◽  
Human Hand ◽  
Reach To Grasp ◽  
K Nearest Neighbors ◽  
Laplacian Eigenmaps ◽  
Non Invasive ◽  
Reduction Methods ◽  
Machine Interface ◽  
Low Dimensional

AbstractElectromyography (EMG) is a simple, non-invasive, and cost-effective technology for sensing muscle activity. However, EMG is also noisy, complex, and high-dimensional. It has nevertheless been widely used in a host of human-machine-interface applications (electrical wheelchairs, virtual computer mice, prosthesis, robotic fingers, etc.) and in particular to measure reaching and grasping motions of the human hand. Here, we developd a more automated pipeline to predict object weight in a reach-and-grasp task from an open dataset relying only on EMG data. In that we shifted the focus from manual feature-engineering to automated feature-extraction by using raw (filtered) EMG signals and thus letting the algorithms select the features. We further compared intrinsic EMG features, derived from several dimensionality-reduction methods, and then ran some classification algorithms on these low-dimensional representations. We found that the Laplacian Eigenmap algorithm generally outperformed other dimensionality-reduction methods. What is more, optimal classification accuracy was achieved using a combination of Laplacian Eigenmaps (simple-minded) and k-Nearest Neighbors (88% for 3-way classification). Our results, using EMG alone, are comparable to others in the literature that used EMG and EEG together. They also demonstrate the usefulness of dimensionality reduction when classifying movement based on EMG signals and more generally the usefulness of EMG for movement classification.

CNN-SINDy Based Reduced Order Modeling of Unsteady Flow Fields

2019 ◽  
Author(s):  
Takaaki Murata ◽  
Kai Fukami ◽  
Koji Fukagata
Keyword(s):  
Temporal Evolution ◽  
Flow Fields ◽  
Cylinder Wake ◽  
Order Model ◽  
Reduced Order ◽  
Energy Reconstruction ◽  
Low Dimensional ◽  
Proper Orthogonal ◽  
Reconstruction Rate ◽  
New Framework

Abstract We present a new framework of nonlinear reduced order model to extract low-dimensional modes and to predict their temporal evolutions. Autoencoder-type Convolutional Neural Network (CNN) which can learn nonlinearity of data is used to extract low-dimensional modes. For obtaining the temporal evolution of a mapped data by CNN, Sparse Identification of Nonlinear Dynamics (SINDy) is performed. The proposed method is applied to a circular cylinder wake at ReD = 100. The CNN trained using fluctuation components of velocity vector u, v shows better results than the snapshot Proper Orthogonal Decomposition in terms of the energy reconstruction rate. Although time-evolving flow fields reproduced by SINDy equation also show reasonable agreement with the reference direct numerical simulation, the errors of CNN and SINDy are accumulated through integral computation. The error of CNN can be reduced by devising a better network structure; however, the error of SINDy depends on the waveform of latent vector.

scholarly journals The geometry of masking in neural populations

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Dario L. Ringach
Keyword(s):  
Visual Cortex ◽  
Cortical Neurons ◽  
Dimensional Space ◽  
Geometric Analysis ◽  
Geometric Approach ◽  
Population Level ◽  
Contextual Modulation ◽  
Neural Populations ◽  
Early Visual Cortex ◽  
Low Dimensional

Abstract The normalization model provides an elegant account of contextual modulation in individual neurons of primary visual cortex. Understanding the implications of normalization at the population level is hindered by the heterogeneity of cortical neurons, which differ in the composition of their normalization pools and semi-saturation constants. Here we introduce a geometric approach to investigate contextual modulation in neural populations and study how the representation of stimulus orientation is transformed by the presence of a mask. We find that population responses can be embedded in a low-dimensional space and that an affine transform can account for the effects of masking. The geometric analysis further reveals a link between changes in discriminability and bias induced by the mask. We propose the geometric approach can yield new insights into the image processing computations taking place in early visual cortex at the population level while coping with the heterogeneity of single cell behavior.

Task-Independent Cognitive State Transition Detection From Cortical Neurons During 3-D Reach-to-Grasp Movements

2015 ◽  
Vol 23 (4) ◽  
pp. 676-682 ◽  
Author(s):  
Xiaoxu Kang ◽  
Sridevi V. Sarma ◽  
Sabato Santaniello ◽  
Marc Schieber ◽  
Nitish V. Thakor
Keyword(s):  
Cortical Neurons ◽  
State Transition ◽  
Cognitive State ◽  
Reach To Grasp

Botnets

2014 ◽  
pp. 204-223 ◽  
Author(s):  
Hamad Binsalleeh
Keyword(s):  
Defense Mechanisms ◽  
Personal Information ◽  
Theoretical Background ◽  
Ddos Attacks ◽  
Botnet Detection ◽  
Reporting Framework ◽  
Financial Benefits ◽  
Catch Up ◽  
Insight Into ◽  
New Framework

Recent malicious attempts are intended to get financial benefits through a large pool of compromised hosts, which are called software robots or simply bots. A group of bots, referred to as a botnet, is remotely controllable by a server and can be used for sending spam emails, stealing personal information, and launching DDoS attacks. Growing popularity of botnets compels to find proper countermeasures, but existing defense mechanisms hardly catch up with the speed of botnet technologies. Bots are constantly and automatically changing their signatures to successfully avoid the detection. Therefore, it is necessary to analyze the weaknesses of existing defense mechanisms to find the gap and then design new framework of botnet detection that integrates effective approaches. To get a deep insight into the inner-working of botnets and to understand their architecture, the authors analyze some sophisticated sample botnets. In this chapter, they propose a comprehensive botnet analysis and reporting framework that is based on sound theoretical background.

scholarly journals Climate Regime Variability for Past and Present Time Slices Simulated by the Fast Ocean Atmosphere Model

2009 ◽  
Vol 22 (1) ◽  
pp. 58-70 ◽  
Author(s):  
Dörthe Handorf ◽  
Klaus Dethloff ◽  
Andrew G. Marshall ◽  
Amanda Lynch
Keyword(s):  
Climate Model ◽  
Low Frequency ◽  
Empirical Orthogonal Functions ◽  
Climate Regime ◽  
Orthogonal Functions ◽  
Medium Resolution ◽  
Ocean Atmosphere ◽  
Dimensional State Space ◽  
Atmosphere Model ◽  
Low Dimensional

Abstract This paper presents an analysis of Northern Hemisphere climate regime variability for three different time slices, simulated by the Fast Ocean Atmosphere Model (FOAM). The three time slices are composed of present-day conditions, the mid-Holocene, and the Last Glacial Maximum (LGM). Climate regimes have been determined by analyzing the structure of a spherical probability density function in a low-dimensional state space spanned by the three leading empirical orthogonal functions. This study confirms the ability of the FOAM medium-resolution climate model to reproduce low-frequency climate variability in the form of regime-like behavior. Three to four regimes have been detected for each time slice. Compared with present-day conditions, new climate regimes appeared for the LGM. For the mid-Holocene, which had slightly different boundary conditions and external forcings than the present-day simulation, the frequency of occurrence of the regimes was altered while only slight changes were found in the structure of some regimes.

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