Dealing with high-throughput information systems is becoming an everyday problem in many fields of science, as technological advances improve our ability to gather data. In particular, the information encoding problem in high-dimensional spaces is a crucial aspect to consider. In fact, biological systems are known to be very efficient at encoding and processing high-dimensional information. Here we propose a biologically-based solution that mimics the neural processing performed by the Antennal Lobe of insects. Based on our understanding of this system, our model exploits plausible neural mechanisms to transform the massive and high-dimensional spatial and temporal input of the olfactory receptor neurons into a neural population encoding based on synchrony and frequency, consistent with known physiology. We demonstrate the capabilities of our Antennal Lobe model in the context of a classification task of different olfactory stimuli of varying concentrations. We show that the generated neural representation conveys both the identity and the concentration of each stimuli.