Olfactory stimuli are represented in a high-

dimensional 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.