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Article Dans Une Revue ACM Journal on Emerging Technologies in Computing Systems Année : 2015

Toward a sparse self-organizing map for neuromorphic architectures

Résumé

Neuro-biological 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 program- ming 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, com- posed 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 re- acts 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 paper 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.
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Dates et versions

hal-01103430 , version 1 (14-01-2015)

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Laurent Rodriguez, Benoit Miramond, Bertrand Granado. Toward a sparse self-organizing map for neuromorphic architectures. ACM Journal on Emerging Technologies in Computing Systems, 2015, Special issue on Neuromorphic and Brain-Inspired Computing Systems, 11 (4), pp.33. ⟨10.1145/2638559⟩. ⟨hal-01103430⟩
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