Maximum likelihood estimation of cascade point-process neural encoding models

By Liam Paninski. Network, 2004

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Spatio-temporal correlations and visual signalling in a complete neuronal population (2008, background): “The statistics of such patterns have been studied in a variety of brain areas, and their significance in the processing and representation of sensory information has been debated extensively [1, 2, 3, 4, 5, 6, 7, 8, 9].”
How MT cells analyze the motion of visual patterns (2006, methodology): “A model of this form guarantees that the regression solution used for the linear weights is also the solution that maximizes the likelihoo”
Probabilistic population codes for Bayesian decision making (2008, methodology): “We use LNP neurons because they provide a good approximation to real neurons, while producing spikes with realistic count statistics close to the exponential family (Paninski, 2004; Plesser and Gerstner, 2000).”
Stochastic variational learning in recurrent spiking networks (2014, methodology): “In a network without hidden neurons as illustrated in Figures 2A,B , the data log-likelihood (Equation 8) reduces to a simple form In Paninski (2004) and Pillow et al. (2004) it is shown that the optimization problem deﬁned by the log-likelihood (Equation 9) is convex.”