Prerequisites for integrating unsupervised and reinforcement learning in a single network of spiking neurons

Most artificial neural network architectures learn either via unsupervised or reinforcement learning but rarely via both. However, the brain effectively integrates both types of learning. We describe which prerequisites are necessary in a spiking network architecture in order to integrate both learning mechanisms and present a network which meets these requirements. In a nut shell, the network has a winner-take-all type output layer resembling the motor output and an excitatory feedback layer which extends the firing of the input layer until after the end of external stimulation resembling the function of the hippocampus.