A Timescale Invariant STDP-Based Spiking Deep Network for Unsupervised Online Feature Extraction from Event-Based Sensor Data

We introduce a deep spiking convolutional neural network of integrate-and-fire (IF) neurons, which extracts hierarchical features from a stream of event-based vision data in an unsupervised fashion and online. Our network operates with a simple spike-timing dependent plasticity (STDP) rule, which does not require the definition of an input timescale. We demonstrate how our network is able to learn translational invariant features from the event-based N-MNIST dataset, while preserving dynamic information in the data. We demonstrate how these features can be used to perform online classification. Our network is the first neuromorphic system which can be used to learn complex hierarchical features unsupervised from a continuous stream of address-event-representation (AER) data, operating outside of a database framework and on multiple timescales. Additionally, all the mechanisms we use are simple and generic. This could open the possibility to implement our system on current neuromorphic hardware, to build a real-world fully adaptive event-based vision system.