Statistical issues in the analysis of neuronal data.

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Kernel bandwidth optimization in spike rate estimation (2009, methodology): “In the limit of a large number of spike trains, however, mixed spikes are statistically independent and the superimposed sequence can be approximated as a single inhomogeneous Poisson point process (Cox 1962; Snyder 1975; Daley and Vere-Jones 1988; Kass et al. 2005).”
How advances in neural recording affect data analysis (2011, background): “These techniques generally ask how information about the external world is encoded in the spiking of neuron”
A Method for Selecting the Bin Size of a Time Histogram (2007, background): “However, spikes accumulated from a large number of spike trains are in the majority mutually independent and can be regarded as being derived from a time-dependent Poisson point process (Snyder, 1975; Daley & Vere-Jones, 1988; Kass et al., 2005).”
Self‐consistent method for density estimation (2009, background): “This may happen, for example, when determining a probability distribution from a finite Monte Carlo sample (Binder, 1986), rounding off the shape of a galaxy from a collection of stars (Ripley and Sutherland, 1990) or assessing the instantaneous firing rate of a neuron from a discrete set of action ”