Abstract
We propose a neuromimetic architecture able to perform always-on pattern
recognition. To achieve this, we extended an existing event-based
algorithm [1], which introduced novel spatio-temporal features as a
Hierarchy Of Time-Surfaces (HOTS). 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 event-based
pattern recognition architecture. Inspired by neuroscience, we extended
this method to increase its performance. Our first contribution was to
add a homeostatic gain control on the activity of neurons to improve the
learning of spatio-temporal patterns [2]. A second contribution is
to draw an analogy between the HOTS algorithm and Spiking Neural
Networks (SNN). Following that analogy, our last contribution is to
modify the classification layer and remodel the offline pattern
categorization method previously used into an online and event-driven
one. This classifier uses the spiking output of the network to define
novel time surfaces and we then perform online classification with a
neuromimetic implementation of a multinomial logistic regression. Not
only do these improvements increase consistently the performances of the
network, they also make this event-driven pattern recognition algorithm
online and bio-realistic. Results were validated on different datasets:
DVS barrel [3], Poker-DVS [4] and N-MNIST [5]. We foresee to
develop the SNN version of the method and to extend this fully
event-driven approach to more naturalistic tasks, notably for always-on,
ultra-fast object categorization.