Abstract
We propose a neuromimetic architecture that can perform always-on
pattern recognition. To achieve this, we have extended an existing
event-based algorithm (Lagorce et al., 2017), which introduced novel
spatio-temporal features as a Hierarchy Of Time-Surfaces (HOTS). Built
from asynchronous events captured by a neuromorphic camera, these time
surfaces allow to encode the local dynamics of a visual scene and to
create an efficient event-based pattern recognition architecture.
Inspired by neuroscience, we have extended this method to improve its
performance. First, we add a homeostatic gain control on the activity of
neurons to improve the learning of spatio-temporal patterns (Grimaldi et
al., 2021). We also provide a new mathematical formalism that allows an
analogy to be drawn between the HOTS algorithm and Spiking Neural
Networks (SNN). Following this analogy, we transform the offline pattern
categorization method into an online and event-driven layer. This
classifier uses the spiking output of the network to define new time
surfaces and we then perform the online classification with a
neuromimetic implementation of a multinomial logistic regression. These
improvements not only consistently increase the performance of the
network, but also bring this event-driven pattern recognition algorithm
fully online. The results have been validated on different datasets:
PokerDVS (Serrano-Gotarredona and Linares-Barranco, 2015), N-MNIST
(Orchard et al., 2015a) and DVS Gesture (Amir et al., 2017). This
demonstrates the efficiency of this bio-realistic SNN for ultra-fast
object categorization through an event-by-event decision making process.