A homeostatic gain control mechanism to improve event-driven object recognition

Abstract

We propose a neuromimetic architecture able to perform 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 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 into 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 learnt during the training phase. In order to compare with state-of-the-art methods, we replicated past results on the same dataset as Lagorce et al (2017) and extended results in this study to the widely used N-MNIST dataset.

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