Generating temporally precise sequences of spikes in response to spatio-temporal patterns of synaptic input is a fundamental process of neural activity: an important example would include coincidence detecting neurons in the primary auditory cortex, that fire in response to synchronous, temporally precise incoming spike trains. This is a non-trivial task however, since typically a neuron receives on the order of 10000 synaptic connections: only a small subset of this input can be considered to convey some meaningful task relevant signal, whilst the rest can be considered noise.

By approximating this random background activity as Gaussian white noise, we investigate how networks of noisy neurons can learn to reliably encode spatio-temporal input spiking patterns as temporally precise output sequences of spikes, specifically through reward modulated spike-timing-dependent plasticity.