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An inference paradigm for extracting neuronal correlations from two-photon imaging data, without requiring intermediate spike deconvolution, provides significant performance gains over existing methods as demonstrated by theoretical analysis, simulation studies, and real-data applications.

A proposed model of optimal inference under cognitive costs accounts for human sequential effects, including subtle patterns of attractive and repulsive influence of past observations, in a binary prediction task across a wide range of stimulus conditions.

During perceptual decision making, maximizing total reward in the long term requires trading reward in the short term for a faster improvement in perceptual representations.

Recurrent neuronal networks learn to predict movement in a self-supervised way using biologically plausible learning rules.