Linear-nonlinear-Poisson Cascade Model
The linear-nonlinear-Poisson (LNP) cascade model is a simplified functional model of neural spike responses. It has been successfully used to describe the response characteristics of neurons in early sensory pathways, especially the visual system. The LNP model is generally implicit when using reverse correlation or the spike-triggered average to characterize neural responses with white-noise stimuli.
There are three stages of the LNP cascade model. The first stage consists of a linear filter, or linear receptive field, which describes how the neuron integrates stimulus intensity over space and time. The output of this filter then passes through a nonlinear function, which gives the neuron's instantaneous spike rate as its output. Finally, the spike rate is used to generate spikes according to an inhomogeneous Poisson process.
The linear filtering stage performs dimensionality reduction, reducing the high-dimensional spatio-temporal stimulus space to a low-dimensional feature space, within which the neuron computes its response. The nonlinearity converts the filter output to a (non-negative) spike rate, and accounts for nonlinear phenomena such as spike threshold (or rectification) and response saturation. The Poisson spike generator converts the continuous spike rate to a series of spike times, under the assumption that the probability of a spike depends only on the instantaneous spike rate.
Read more about Linear-nonlinear-Poisson Cascade Model: Estimation, Related Models, See Also
Famous quotes containing the words cascade and/or model:
“End of tomorrow.
Dont try to start the car or look deeper
Into the eternal wimpling of the sky: luster
On luster, transparency floated onto the topmost layer
Until the whole thing overflows like a silver
Wedding cake or Christmas tree, in a cascade of tears.”
—John Ashbery (b. 1927)
“Id like to be the first model who becomes a woman.”
—Lauren Hutton (b. 1944)