We present a spiking network model that transforms odor-dependent variable-latency olfactory bulb responses into a cortical ensemble code. In the model, which captures basic circuit properties of piriform cortex, the impact of the earliest-activated bulb inputs on the cortical response is amplified by diffuse recurrent collateral excitation which then recruits strong feedback inhibition that stabilizes cortical activity and decreases the impact of later-responding glomeruli. Because the sequence of olfactory bulb activity for a particular odor is preserved across concentration, the ensemble of activated cortical neurons is robust to concentration changes. Nevertheless, odor concentration is represented by the latency and synchrony of the ensemble response. Using decoding techniques, we show that the ensemble-based coding scheme that arises in the cortical model supports concentration-invariant odor recognition.