We introduce Sapinet -- a spike timing (event)-based multilayer neural network for \textit{learning in the wild} -- that is: one-shot online learning of multiple inputs without catastrophic forgetting, and without the need for data-specific hyperparameter retuning. Key features of Sapinet include data regularization, model scaling, data classification, and denoising. The model also supports stimulus similarity mapping. We propose a systematic method to tune the network for performance. We studied the model performance on different levels of odor similarity, gaussian and impulse noise. Sapinet achieved high classification accuracies on standard machine olfaction datasets without the requirement of fine tuning for a specific dataset.