scholarly journals Modeling task influences for saccade sequence and visual relevance prediction

2019 ◽  
Vol 19 (10) ◽  
pp. 106c ◽  
Author(s):  
David Berga ◽  
Calden Wloka ◽  
John K Tsotsos
Keyword(s):  
Modeling Task ◽  
Relevance Prediction

Modeling Task fMRI Data via Deep Convolutional Autoencoder

2017 ◽  
pp. 411-424 ◽  
Author(s):  
Heng Huang ◽  
Xintao Hu ◽  
Milad Makkie ◽  
Qinglin Dong ◽  
Yu Zhao ◽  
...  
Keyword(s):  
Fmri Data ◽  
Convolutional Autoencoder ◽  
Modeling Task

scholarly journals Modeling task-specific manifestations of serotonin in basal ganglia using risk-based decision making

2014 ◽  
Vol 15 (S1) ◽  
Author(s):  
B Pragathi Priyadharsini ◽  
V Srinivasa Chakravarthy ◽  
Balaraman Ravindran ◽  
Ahmed A Moustafa
Keyword(s):  
Decision Making ◽  
Basal Ganglia ◽  
Modeling Task

scholarly journals Modeling task-based fMRI data via deep belief network with neural architecture search

2020 ◽  
Vol 83 ◽  
pp. 101747 ◽  
Author(s):  
Ning Qiang ◽  
Qinglin Dong ◽  
Wei Zhang ◽  
Bao Ge ◽  
Fangfei Ge ◽  
...  
Keyword(s):  
Deep Belief Network ◽  
Fmri Data ◽  
Belief Network ◽  
Neural Architecture ◽  
Modeling Task

Some cautionary remarks on the “localist model” concept

2000 ◽  
Vol 23 (4) ◽  
pp. 478-478
Author(s):  
Richard M. Golden
Keyword(s):  
Connectionist Model ◽  
Model Concept ◽  
Definition Of ◽  
Modeling Task

The notion of a “familiar example” used in Page's definition of a “localist model” is shown to be meaningful only with respect to the types of tasks faced by the connectionist model. It is also shown that the modeling task ultimately dictates which choice of model: “localist” or “distributed” is most appropriate.

The Discrete Event Control of Robotic Assembly Tasks

1995 ◽  
Vol 117 (3) ◽  
pp. 384-393 ◽  
Author(s):  
B. J. McCarragher ◽  
H. Asada
Keyword(s):  
Discrete Event ◽  
Experimental Results ◽  
Robotic Assembly ◽  
New Approach ◽  
Discrete Event Dynamic System ◽  
Discrete Event Control ◽  
Event Dynamic ◽  
Assembly Tasks ◽  
Modeling Task ◽  
Task Level

A new approach to process modeling, task synthesis, and motion control for robotic assembly is presented. Assembly is modeled as a discrete event dynamic system using Petri nets, incorporating both discrete and continuous aspects of the process. The discrete event modelling facilitates a new, task-level approach to the control of robotic assembly. To accomplish a desired trajectory a discrete event controller is developed. The controller issues velocity commands that direct the system toward the next desired contact state, while maintaining currently desired contacts and avoiding unwanted transitions. Experimental results are given for a dual peg-in-the-hole example. The experimental results not only demonstrate highly successful insertion along the optimal trajectory, but also demonstrate the ability to detect, recognize and recover from errors and unwanted situations.

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