Self-organizing maps (SOM) are a well-known and biologically plausible model of input-driven self-organization that has shown to be effective in a wide range of applications. We want to use SOMs to control the processing cores of a massively parallel digital reconfigurable hardware, taking into account the communication constraints of its underlying network-on-chip (NoC) thanks to bio-inspired principles of structural plasticity. Although the SOM accounts for synaptic plasticity, it doesn't address structural plasticity. Therefore we have developed a model, namely the NP-SOM (network programmable self-organizing map), able to define SOMs with different underlying topologies as the result of a specific configuration of the associated NoC. To gain insights on a future introduction of advanced structural plasticity rules that will induce dynamic topological modifications, we investigate and quantify the effects of different hardware-compatible topologies on the SOM performance. To perform our tests we consider a lossy image compression as an illustrative application.