##The linear-nonlinear-Poisson (LNP) model
The LNP model takes as input a bipolar object and produces spikes over time as output. Broadly speaking, the firing rate (or the likelihood of observing a spike in a given time bin) is modeled as a Poisson distribution where the conditional intensity depends on the input stimulus and the neuron's spike history.
In the linear stage, each RGC spatially filters the bipolar mosaic signal over each temporal sample. The nonlinear stage produces the conditional intensity, usually by the exponential function (although sigmoid functions are sometimes used). A spike is generated in a time bin if the conditional intensity exceeds a random draw from a Poisson distribution.
y(t) ~ Poiss [ f(k*x + k0)]
The firing rate y(t) is proportional to the Poisson distribution with intensity determined by a nonlinear function of the linear filter k over stimulus x with tonic component k0.
