Imperial College London

Professor Yiannis Demiris

Faculty of EngineeringDepartment of Electrical and Electronic Engineering

Professor of Human-Centred Robotics, Head of ISN
 
 
 
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Contact

 

+44 (0)20 7594 6300y.demiris Website

 
 
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Location

 

1014Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Demiris:2006:10.1016/j.neunet.2006.02.005,
author = {Demiris, Y and Simmons, G},
doi = {10.1016/j.neunet.2006.02.005},
journal = {Neural Networks},
pages = {272--284},
title = {Perceiving the unusual: temporal properties of hierarchical motor representations for action perception},
url = {http://dx.doi.org/10.1016/j.neunet.2006.02.005},
volume = {19},
year = {2006}
}

RIS format (EndNote, RefMan)

TY  - JOUR
AB - Recent computational approaches to action imitation have advocated the use of hierarchical representations in the perception and imitation of demonstrated actions. Hierarchical representations present several advantages, with the main one being their ability to process information at multiple levels of detail. However, the nature of the hierarchies in these approaches has remained relatively unsophisticated, and their relation with biological evidence has not been investigated in detail, in particular with respect to the timing of movements. Following recent neuroscience work on the modulation of the premotor mirror neuron activity during the observation of unpredictable grasping movements, we present here an implementation of our HAMMER architecture using the minimum variance model for implementing reaching and grasping movements that have biologically plausible trajectories. Subsequently, we evaluate the performance of our model in matching the temporal dynamics of the modulation of cortical excitability during the passive observation of normal and unpredictable movements of human demonstrators.
AU - Demiris,Y
AU - Simmons,G
DO - 10.1016/j.neunet.2006.02.005
EP - 284
PY - 2006///
SN - 0893-6080
SP - 272
TI - Perceiving the unusual: temporal properties of hierarchical motor representations for action perception
T2 - Neural Networks
UR - http://dx.doi.org/10.1016/j.neunet.2006.02.005
VL - 19
ER -