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% accuracy with homeostasis compared to 72.5% 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.

Publication
Computational and Systems Neuroscience (Cosyne) 2021

Antoine Grimaldi
Antoine Grimaldi
Phd candidate in Computational Neuroscience

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

Victor Boutin
Victor Boutin
Phd in Computational Neuroscience

During my PhD, I focused on predictive coding in bio-inspired neural networks.

Laurent U Perrinet
Laurent U Perrinet
Researcher in Computational Neuroscience

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

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