A robust bio-inspired approach to event-driven object recognition

Abstract

We propose a neuromimetic architecture able to perform online pattern recognition. To achieve this, we extended the existing event-based algorithm from Lagorce et al (2017) which introduced novel spatio-temporal features: time-surfaces. Built from asynchronous events acquired by a neuromorphic camera, these time surfaces allow to code the local dynamics of a visual scene and to create an efficient hierarchical event-based pattern recognition architecture. Inspired by biological findings and the efficient coding hypothesis, our main contribution is to integrate homeostatic regulation to the Hebbian learning rule. Indeed, in order to be optimally informative, average neural activity within a layer should be equally balanced across neurons. We used that principle to regularize neurons within the same layer by setting a gain depending on their past activity and such that they emit spikes with balanced firing rates. The efficiency of this technique was first demonstrated through a robust improvement in spatio-temporal patterns which were learned during the training phase. We validated classification performance with the widely used N-MNIST dataset reaching 87.3 percent accuracy with homeostasis compared to 72.5 percent accuracy without homeostasis. Finally, by studying the impact of input jitter on classification highlights resilience of this method. We expect to extend this fully event-driven approach to more naturalistic tasks, notably for ultra-fast object categorization.

Tomorrow Antoine Grimaldi will present our joint work on "A robust bio-inspired approach to event-driven object recognition" at #cosyne2021 check-out the poster now https://t.co/DUNQPcv1mx or meet him tomorrow during the poster session ! pic.twitter.com/wKTJPZbR6B

— @laurentperrinet@neuromatch.social (@laurentperrinet) February 25, 2021

• see the poster online on the Hopin platform
• see a follow-up in:
  Antoine Grimaldi, Victor Boutin, Sio-Hoi Ieng, Laurent U Perrinet, Ryad Benosman (2021). A homeostatic gain control mechanism to improve event-driven object recognition. Content-Based Multimedia Indexing (CBMI) 2021.

  Preprint PDF Cite Video DOI

• read also the follow-up paper :
  Antoine Grimaldi, Victor Boutin, Sio-Hoi Ieng, Ryad Benosman, Laurent U Perrinet (2023). A Robust Event-Driven Approach to Always-on Object Recognition. In revision.

  PDF Cite DOI URL

• Antoine Grimaldi and Laurent Perrinet received funding from the European Union ERA-NET CHIST-ERA 2018 research and innovation program under grant agreement No ANR-19-CHR3-0008-03.

Antoine Grimaldi

Phd candidate in Computational Neuroscience

During my PhD, I am focusing on Ultra-fast vision using Spiking Neural Networks.

Victor Boutin

Post-doc @ Serre Lab, BRown University.

Phd in Computational Neuroscience

Laurent U Perrinet

Researcher in Computational Neuroscience

My research interests include Machine Learning and computational neuroscience applied to Vision.

Ryad Benosman