Neuromorphic Computing: Toward Dynamical Data Processing
Fabian Alibart
CNRS, Lille, FR
ABSTRACT
While machine-learning approaches have done tremendous progresses these last years, more is expected with the third generation of neural networks that should sustain this evolution. In addition to unsupervised learning and spike-based computing capability, this new generation of computing machines will be intrinsically dynamical systems that will shift our conception of electronic. In this context, investigating new material implementation of neuromorphic concept seems a very attracting direction. In this presentation, I will present our recent efforts toward the development of neuromorphic synapses that present attractive features for both spike-based computing and unsupervised learning. From their basic physics, I will show how their dynamics can be used to implement time-dependent computing functions. I will also extend this idea of dynamical computing to the case of reservoir computing based on organic sensors in order to show how neuromorphic concepts can be applied to a large class of dynamical problems.
