  We have added a simplified neuromorphic model of Spike Time Dependent
Plasticity (STDP) to the Synapto-dendritic Kernel Adapting Neuron (SKAN). The
resulting neuron model is the first to show synaptic encoding of afferent
signal to noise ratio in addition to the unsupervised learning of spatio
temporal spike patterns. The neuron model is particularly suitable for
implementation in digital neuromorphic hardware as it does not use any complex
mathematical operations and uses a novel approach to achieve synaptic
homeostasis. The neurons noise compensation properties are characterized and
tested on noise corrupted zeros digits of the MNIST handwritten dataset.
Results show the simultaneously learning common patterns in its input data
while dynamically weighing individual afferent channels based on their signal
to noise ratio. Despite its simplicity the interesting behaviors of the neuron
model and the resulting computational power may offer insights into biological
systems.
