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Deneve et al. propose a recurrent network which is able to fit a template to (Poisson-)noisy input activity, implementing an estimator of the original input. The authors show analytically and in simulations that the network is able to approximate a maximum likelihood estimator. The network’s dynamics are governed by divisive normalization and the neural input tuning curves are hard-wired.