  We present an end-to-end trainable modular event-driven neural architecture
that uses local synaptic and threshold adaptation rules to perform
transformations between arbitrary spatio-temporal spike patterns. The
architecture represents a highly abstracted model of existing Spiking Neural
Network (SNN) architectures. The proposed Optimized Deep Event-driven Spiking
neural network Architecture (ODESA) can simultaneously learn hierarchical
spatio-temporal features at multiple arbitrary time scales. ODESA performs
online learning without the use of error back-propagation or the calculation of
gradients. Through the use of simple local adaptive selection thresholds at
each node, the network rapidly learns to appropriately allocate its neuronal
resources at each layer for any given problem without using a real-valued error
measure. These adaptive selection thresholds are the central feature of ODESA,
ensuring network stability and remarkable robustness to noise as well as to the
selection of initial system parameters. Network activations are inherently
sparse due to a hard Winner-Take-All (WTA) constraint at each layer. We
evaluate the architecture on existing spatio-temporal datasets, including the
spike-encoded IRIS and TIDIGITS datasets, as well as a novel set of tasks based
on International Morse Code that we created. These tests demonstrate the
hierarchical spatio-temporal learning capabilities of ODESA. Through these
tests, we demonstrate ODESA can optimally solve practical and highly
challenging hierarchical spatio-temporal learning tasks with the minimum
possible number of computing nodes.
