Olfactory stimuli are represented in a highdimensional space by neural networks of the olfactory system. A great deal of research in olfaction has focused on this representation within the first processing stage, the olfactory bulb (vertebrates) or antennal lobe (insects) glomeruli. In particular the mapping of chemical stimuli onto olfactory glomeruli and the relation of this mapping to perceptual qualities have been investigated. While a number of studies have illustrated the importance of inhibitory networks within the olfactory bulb or the antennal lobe for the shaping and processing of olfactory information, it is not clear how exactly these inhibitory networks are organized to provide filtering and contrast enhancement capabilities. In this work the aim is to study the topology of the proposed networks by using software simulations and hardware implementation. While we can study the dependence of the activity on each parameter of the theoretical models with the simulations, it is important to understand whether the models can be used in robotic applications for real-time odor recognition. We present the results of a linear simulation, a spiking simulation with I&F neurons and a real-time hardware emulation using neuromorphic VLSI chips. We used an input data set of neurophysiological recordings from olfactory receptive neurons of insects, especially Drosophila.