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.