Traditionally convolutional neural network architectures have been designed by stacking layers on top of each other to form deeper hierarchical networks. The cortex in the brain however does not just stack layers as done in standard convolution neural networks, instead different regions are organized next to each other in a large single sheet of neurons. Biological neurons self organize to form topographic maps, where neurons encoding similar stimuli group together to form logical clusters. Here we propose new self-organization principles that allow for the formation of hierarchical cortical regions (i.e. layers) in a completely unsupervised manner without requiring any predefined architecture. Synaptic connections are dynamically grown and pruned, which allows us to actively constrain the number of incoming and outgoing connections. This way we can minimize the wiring cost by taking into account both the synaptic strength and the connection length. The proposed method uses purely local learning rules in the form of spike-timing-dependent plasticity (STDP) with lateral excitation and inhibition. We show experimentally that these self-organization rules are sufficient for topographic maps and hierarchical layers to emerge. Our proposed Self-Organizing Neural Sheet (SONS) model can thus form traditional neural network layers in a completely unsupervised manner from just a single large pool of unstructured spiking neurons.