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Author Rodriguez, Laurent ♦ Miramond, Benot ♦ Granado, Bertrand
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Copyright Year ©2015
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Neuromorphic architectures ♦ Cortical plasticity ♦ Self-organizing maps
Abstract Neurobiological systems have often been a source of inspiration for computational science and engineering, but in the past their impact has also been limited by the understanding of biological models. Today, new technologies lead to an equilibrium situation where powerful and complex computers bring new biological knowledge of the brain behavior. At this point, we possess sufficient understanding to both imagine new brain-inspired computing paradigms and to sustain a classical paradigm which reaches its end programming and intellectual limitations. In this context, we propose to reconsider the computation problem first in the specific domain of mobile robotics. Our main proposal consists in considering computation as part of a global adaptive system, composed of sensors, actuators, a source of energy and a controlling unit. During the adaptation process, the proposed brain-inspired computing structure does not only execute the tasks of the application but also reacts to the external stimulation and acts on the emergent behavior of the system. This approach is inspired by cortical plasticity in mammalian brains and suggests developing the computation architecture along the system's experience. This article proposes modeling this plasticity as a problem of estimating a probability density function. This function would correspond to the nature and the richness of the environment perceived through multiple modalities. We define and develop a novel neural model solving the problem in a distributed and sparse manner. And we integrate this neural map into a bio-inspired hardware substrate that brings the plasticity property into parallel many-core architectures. The approach is then called Hardware Plasticity. The results show that the self-organization properties of our model solve the problem of multimodal sensory data clusterization. The properties of the proposed model allow envisaging the deployment of this adaptation layer into hardware architectures embedded into the robot's body in order to build intelligent controllers.
ISSN 15504832
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2015-04-01
Publisher Place New York
e-ISSN 15504840
Journal ACM Journal on Emerging Technologies in Computing Systems (JETC)
Volume Number 11
Issue Number 4
Page Count 25
Starting Page 1
Ending Page 25

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Source: ACM Digital Library