Author name cluster

H. Wang

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5 papers

2 author rows

YNIMG Journal 2018 Journal Article

Oscillatory networks of high-level mental alignment: A perspective-taking MEG study

R.A. Seymour
H. Wang
G. Rippon
K. Kessler

Mentally imagining another's perspective is a high-level social process, reliant on manipulating internal representations of the self in an embodied manner. Recently Wang et al. (2016) showed that theta-band (3–7 Hz) brain oscillations within the right temporo-parietal junction (rTPJ) and brain regions coding for motor/body schema contribute to the process of perspective-taking. Using a similar paradigm, we set out to unravel the extended functional brain network in detail. Increasing the angle between self and other perspective was accompanied by longer reaction times and increases in theta power within rTPJ, right lateral prefrontal cortex (PFC) and right anterior cingulate cortex (ACC). Using Granger-causality, we showed that lateral PFC and ACC exert top-down influence over rTPJ, indicative of executive control processes required for managing conflicts between self and other perspectives. Finally, we quantified patterns of whole-brain phase coupling in relation to the rTPJ. Results suggest that rTPJ increases its theta-band phase synchrony with brain regions involved in mentalizing and regions coding for motor/body schema; whilst decreasing synchrony to visual regions. Implications for neurocognitive models are discussed, and it is proposed that rTPJ acts as a ‘hub’ to route bottom-up visual information to internal representations of the self during perspective-taking, co-ordinated by theta-band oscillations.

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YNIMG Journal 2017 Journal Article

The Virtual Epileptic Patient: Individualized whole-brain models of epilepsy spread

V.K. Jirsa
T. Proix
D. Perdikis
M.M. Woodman
H. Wang
J. Gonzalez-Martinez
C. Bernard
C. Bénar

Individual variability has clear effects upon the outcome of therapies and treatment approaches. The customization of healthcare options to the individual patient should accordingly improve treatment results. We propose a novel approach to brain interventions based on personalized brain network models derived from non-invasive structural data of individual patients. Along the example of a patient with bitemporal epilepsy, we show step by step how to develop a Virtual Epileptic Patient (VEP) brain model and integrate patient-specific information such as brain connectivity, epileptogenic zone and MRI lesions. Using high-performance computing, we systematically carry out parameter space explorations, fit and validate the brain model against the patient's empirical stereotactic EEG (SEEG) data and demonstrate how to develop novel personalized strategies towards therapy and intervention.

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ICRA Conference 2003 Conference Paper

A passive robot system for measuring spacesuit joint damping parameters

H. Wang
X. H. Gao
Minghe Jin
L. B. Du
Jingdong Zhao
H. Y. Hu
Hegao Cai
T. Q. Li

This paper presents a novel passive robot system with 6 DOF force/torque sensor for measuring spacesuit joint damping parameters. Based on its special mechanical structure, a 3 DOF model of flexible IVA (intra vehicular activity) spacesuit joint has been built. Experimental results prove the effectiveness of the measuring principle. Potential application of the measuring system is discussed.

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I&C Journal 1999 Journal Article

Multireceiver Authentication Codes: Models, Bounds, Constructions, and Extensions

R. Safavi-Naini
H. Wang

Multireceiver authentication codes allow one sender to construct an authenticated message for a group of receivers such that each receiver can verify authenticity of the received message. In this paper, we give a formal definition of multireceiver authentication codes, derive information theoretic and combinatorial lower bounds on their performance, and give new efficient and flexible constructions for such codes. Finally, we extend the basic model to the case that multiple messages are sent and the case that the sender can be any member of the group.

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NeurIPS Conference 1990 Conference Paper

A Multiscale Adaptive Network Model of Motion Computation in Primates

H. Wang
Bimal Mathur
Christof Koch

We demonstrate a multiscale adaptive network model of motion computation in primate area MT. The model consists of two stages: (l) local velocities are measured across multiple spatio-temporal channels, and (2) the optical flow field is computed by a network of direction(cid: 173) selective neurons at multiple spatial resolutions. This model embeds the computational efficiency of Multigrid algorithms within a parallel network as well as adaptively computes the most reliable estimate of the flow field across different spatial scales. Our model neurons show the same nonclassical receptive field properties as Allman's type I MT neurons. Since local velocities are measured across multiple channels, various channels often provide conflicting measurements to the network. We have incorporated a veto scheme for conflict resolution. This mechanism provides a novel explanation for the spatial frequency dependency of the psychophysical phenomenon called Motion Capture. 1 MOTIVATION We previously developed a two-stage model of motion computation in the visual system of primates (Le. magnocellular pathway from retina to V1 and MT; Wang, Mathur & Koch, 1989). This algorithm has these deficiencies: (1) the issue of optimal spatial scale for velocity measurement, and (2) the issue optimal spatial scale for the smoothness of motion field. To address these deficiencies, we have implemented a multi-scale motion network based on multigrid algorithms. All methods of estimating optical flow make a basic assumption about the scale of the velocity relative to the spatial neighborhood and to the temporal discretization step of delay. Thus, if the velocity of the pattern is much larger than the ratio of the spatial to temporal sampling step, an incorrect velocity value will be obtained (Battiti, Amaldi & Koch, 1991). Battiti et al. proposed a coarse-to-fine strategy for adaptively detennining

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