Visualization Techniques in SNN Simulators

Hammouda Elbez , Kamel Benhaoua , Philippe Devienne (1) , Pierre Boulet (1)

1 CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189

Neural networks are one of the most well-known artificial intelligence technique. These networks have known a huge evolution since their first proposal, represented by the three known generations. On the other hand, neuromorphic architectures are a very promising way to overcome the limitation of the Von Neumann architecture and the end of Moore's law. Neuromorphic architectures can lead to a huge energy consumption decrease mostly due to the colocation of computation and storage. These architectures implement spiking neural networks (SNNs), the third generation of artificial neural networks that model very finely biological neural networks. One of the main problematics that prevents us from optimizing and getting the best performance of SNNs and as a result the neuromorphic architectures development and production, is the lack of a clear and complete understanding of their behavior, especially what makes learning efficient. One of the approaches to answer that is analyzing by visualization of simulation traces of such networks and architectures. In this paper, we propose a comparison of the visualization techniques proposed by SNN simulators for analysis purposes.

Mots clés: Neural Network; Data Science; Visualization; Simulation; Analysis; Neuromorphic Architectures; SNN

Domaines: Informatique [cs]; Intelligence artificielle [cs.AI]

Hammouda Elbez, Kamel Benhaoua, Philippe Devienne, Pierre Boulet. Visualization Techniques in SNN Simulators. 3rd International Conference on Multimedia Information Processing, CITIM’2018, Oct 2018, Mascara, Algeria. ⟨hal-02887481⟩