Evidence for a specific internal representation of motion-force relationships during object manipulation

2003 ◽  
Vol 88 (1) ◽  
pp. 60-72 ◽  
Author(s):  
Christopher D. Mah ◽  
Ferdinando A. Mussa-Ivaldi
Keyword(s):  
Internal Representation ◽  
Object Manipulation

Object Manipulation and Creative Behavior

1973 ◽  
Author(s):  
J. Barsaloux ◽  
T. J. Bouchard ◽  
S. Bush
Keyword(s):  
Object Manipulation ◽  
Creative Behavior

Object Manipulation in Non-Human Animals

2012 ◽  
Author(s):  
Daniel J. Weiss ◽  
Kate Chapman
Keyword(s):  
Object Manipulation

Symmetries in Action: On the Interactive Nature of Planning Constraints for Bimanual Object Manipulation

2013 ◽  
Author(s):  
John M. Huhn ◽  
Kimberly A. Schimpf ◽  
Robrecht P. R. D. van der Wel
Keyword(s):  
Object Manipulation ◽  
Interactive Nature

Encapsulation and the C++ class

2018 ◽  
Author(s):  
Joseph F. Boudreau ◽  
Eric S. Swanson
Keyword(s):  
Program Design ◽  
Internal Representation ◽  
Floating Point ◽  
Complex Class ◽  
Numerical Examples ◽  
Important Notion ◽  
Standard Library

Built-in datatypes and C++ classes are introduced in this chapter, and discussed in relation to the important notion of encapsulation, which refers to the separation between the internal representation of the datatype and the operations to which it responds. Encapsulation later becomes an important consideration in the design of custom C++ classes that programmers develop themselves. It is illustrated with built-in floating-point datatypes float and double and with the complex class from the C++ standard library. While a sophisticated programmer is aware of the internal representation of data and its resulting limitations, encapsulation allows one to consider these as details and frees one to think at a higher level of program design. Some simple numerical examples are discussed in the text and in the exercises.

Development of novel hybrid flexure-based microgrippers for precision micro-object manipulation

2009 ◽  
Vol 80 (6) ◽  
pp. 065106 ◽  
Author(s):  
Mohd Nashrul Mohd Zubir ◽  
Bijan Shirinzadeh ◽  
Yanling Tian
Keyword(s):  
Object Manipulation

scholarly journals Spatial Memory in a Spiking Neural Network with Robot Embodiment

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2678
Author(s):  
Sergey A. Lobov ◽  
Alexey I. Zharinov ◽  
Valeri A. Makarov ◽  
Victor B. Kazantsev
Keyword(s):  
Neural Network ◽  
Spatial Memory ◽  
Internal Representation ◽  
Learning Curves ◽  
Global Network ◽  
Spiking Neural Network ◽  
Interference Phenomenon ◽  
Brain Functioning ◽  
Information Characteristics ◽  
Catastrophic Interference

Cognitive maps and spatial memory are fundamental paradigms of brain functioning. Here, we present a spiking neural network (SNN) capable of generating an internal representation of the external environment and implementing spatial memory. The SNN initially has a non-specific architecture, which is then shaped by Hebbian-type synaptic plasticity. The network receives stimuli at specific loci, while the memory retrieval operates as a functional SNN response in the form of population bursts. The SNN function is explored through its embodiment in a robot moving in an arena with safe and dangerous zones. We propose a measure of the global network memory using the synaptic vector field approach to validate results and calculate information characteristics, including learning curves. We show that after training, the SNN can effectively control the robot’s cognitive behavior, allowing it to avoid dangerous regions in the arena. However, the learning is not perfect. The robot eventually visits dangerous areas. Such behavior, also observed in animals, enables relearning in time-evolving environments. If a dangerous zone moves into another place, the SNN remaps positive and negative areas, allowing escaping the catastrophic interference phenomenon known for some AI architectures. Thus, the robot adapts to changing world.

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