  Several learning rules for synaptic plasticity, that depend on either spike
timing or internal state variables, have been proposed in the past imparting
varying computational capabilities to Spiking Neural Networks. Due to design
complications these learning rules are typically not implemented on
neuromorphic devices leaving the devices to be only capable of inference. In
this work we propose a unidirectional post-synaptic potential dependent
learning rule that is only triggered by pre-synaptic spikes, and easy to
implement on hardware. We demonstrate that such a learning rule is functionally
capable of replicating computational capabilities of pairwise STDP. Further
more, we demonstrate that this learning rule can be used to learn and classify
spatio-temporal spike patterns in an unsupervised manner using individual
neurons. We argue that this learning rule is computationally powerful and also
ideal for hardware implementations due to its unidirectional memory access.

Neural and Evolutionary Computing