Membrane-Dependent Neuromorphic Learning Rule for Unsupervised Spike Pattern Detection
This work addresses the challenge of enabling learning on neuromorphic devices for applications in neuromorphic computing, though it is incremental as it builds on existing STDP methods.
The authors tackled the problem of implementing synaptic plasticity learning rules on neuromorphic hardware by proposing a unidirectional post-synaptic potential dependent rule triggered by pre-synaptic spikes, which is easy to implement and can replicate pairwise STDP capabilities and learn spatio-temporal spike patterns unsupervised.
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.