  We propose a novel architecture, the event-based GASSOM for learning and
extracting invariant representations from event streams originating from
neuromorphic vision sensors. The framework is inspired by feed-forward cortical
models for visual processing. The model, which is based on the concepts of
sparsity and temporal slowness, is able to learn feature extractors that
resemble neurons in the primary visual cortex. Layers of units in the proposed
model can be cascaded to learn feature extractors with different levels of
complexity and selectivity. We explore the applicability of the framework on
real world tasks by using the learned network for object recognition. The
proposed model achieve higher classification accuracy compared to other
state-of-the-art event based processing methods. Our results also demonstrate
the generality and robustness of the method, as the recognizers for different
data sets and different tasks all used the same set of learned feature
detectors, which were trained on data collected independently of the testing
data.
