Spike-timing dependent plasticity (STDP) has traditionally been of great interest to theoreticians, as it seems to provide an answer to the question of how the brain can develop functional structure in response to repeated stimuli. However, despite this high level of interest, convincing demonstrations of this capacity in large, initially random networks have not been forthcoming. Such demonstrations as there are typically rely on constraining the problem artificially. Techniques include employing additional pruning mechanisms or STDP rules that enhance symmetry breaking, simulating networks with low connectivity that magnify competition between synapses, or combinations of the above (see, e.g. [1-3]).
Here, we describe a theory for the stimulus-driven development of feed-forward structures in random networks. The theory explains why the emergence of such structures does not take place in unconstrained systems  and enables us to identify candidate biologically motivated adaptations to the balanced random network model that might facilitate it. Finally, we investigate these candidate adaptations in large-scale simulations.
Partially supported by the Helmholtz Alliance on Systems Biology, the Next-Generation Supercomputer Project of MEXT, EU Grant 15879 (FACETS), DIP F1.2, and BMBF Grant 01GQ0420 to BCCN Freiburg. Access to HPC provided by JUGENE-Grant JINB33. All network simulations were carried out with NEST (http://www.nest-initiative.org).