Action recognition using a bio-inspired feedforward spiking network

Maria Jose Escobar, Guillaume S. Masson, Thierry Vieville, Pierre Kornprobst

Research output: Contribution to journalArticle

50 Citations (Scopus)


We propose a bio-inspired feedforward spiking network modeling two brain areas dedicated to motion (V1 and MT), and we show how the spiking output can be exploited in a computer vision application: action recognition. In order to analyze spike trains, we consider two characteristics of the neural code: mean firing rate of each neuron and synchrony between neurons. Interestingly, we show that they carry some relevant information for the action recognition application. We compare our results to Jhuang et al. (Proceedings of the 11th international conference on computer vision, pp. 1-8, 2007) on the Weizmann database. As a conclusion, we are convinced that spiking networks represent a powerful alternative framework for real vision applications that will benefit from recent advances in computational neuroscience. © 2009 Springer Science+Business Media, LLC.
Original languageEnglish
Pages (from-to)284-301
Number of pages18
JournalInternational Journal of Computer Vision
Publication statusPublished - 1 May 2009
Externally publishedYes

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