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Single-neuron models are useful not only for studying the emergent properties of neural circuits in large-scale simulations, but also for extracting and summarizing in a principled way the information contained in electrophysiological recordings. Here we demonstrate that, using a convex optimization procedure we previously introduced, a Generalized Integrateand-Fire model can be accurately fitted with a limited amount of data. The model is capable of predicting both the spiking activity and thedoi:10.1371/journal.pcbi.1004275 pmid:26083597 pmcid:PMC4470831 fatcat:n2sna4rjxndujdvaa3mhl2bxne