Understanding the neural mechanisms underlying odor representation and memory in the olfactory bulb (OB) is critical for advancing our knowledge of sensory processing and neural plasticity. Inspired by in vivo experimental findings [1, 2], we applied the eligibility propagation (e-prop) learning rule [3] to simulate the cholinergic and GABAergic responses to distinct odors. [1, 2] found differential activation patterns in glomeruli of the OB in response to different odor exposures. The modulation of activity by cholinergic and GABAergic projections from the horizontal diagonal band (HDB) was shown to enhance odor discrimination by either amplifying or suppressing glomerular responses. In our simulation, we utilized a recurrent spiking neural network (SNN) model implemented in the NEST simulator [4], incorporating both regular and adaptive leaky integrate-and-fire (LIF) neurons. The applied e-prop learning rule facilitates synaptic plasticity through local eligibility traces modulated by feedback signals. This approach allowed us to replicate the modulatory effects of cholinergic and GABAergic inputs observed during odor presentation. Our results demonstrate that the application of the e-prop algorithm effectively captures the dynamics of neural odor representation and response in the OB, mirroring the bimodal modulation observed in [1, 2]. These findings underscore the potential of e-prop as a robust tool for modeling neural plasticity and sensory processing in the OB, thus advancing our understanding of the complex neural mechanisms of odor sensory perception.