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- Linear-nonlinear-Poisson_cascade_model abstract "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.".
- Linear-nonlinear-Poisson_cascade_model thumbnail LNPModelDiagram.png?width=300.
- Linear-nonlinear-Poisson_cascade_model wikiPageID "19498707".
- Linear-nonlinear-Poisson_cascade_model wikiPageRevisionID "521984723".
- Linear-nonlinear-Poisson_cascade_model hasPhotoCollection Linear-nonlinear-Poisson_cascade_model.
- Linear-nonlinear-Poisson_cascade_model subject Category:Computational_neuroscience.
- Linear-nonlinear-Poisson_cascade_model subject Category:Stochastic_models.
- Linear-nonlinear-Poisson_cascade_model type Assistant109815790.
- Linear-nonlinear-Poisson_cascade_model type CausalAgent100007347.
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- Linear-nonlinear-Poisson_cascade_model type StochasticModels.
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- Linear-nonlinear-Poisson_cascade_model type Worker109632518.
- Linear-nonlinear-Poisson_cascade_model type YagoLegalActor.
- Linear-nonlinear-Poisson_cascade_model type YagoLegalActorGeo.
- Linear-nonlinear-Poisson_cascade_model comment "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.".
- Linear-nonlinear-Poisson_cascade_model label "Linear-nonlinear-Poisson cascade model".
- Linear-nonlinear-Poisson_cascade_model sameAs m.04n4zdg.
- Linear-nonlinear-Poisson_cascade_model sameAs Q6553384.
- Linear-nonlinear-Poisson_cascade_model sameAs Q6553384.
- Linear-nonlinear-Poisson_cascade_model sameAs Linear-nonlinear-Poisson_cascade_model.
- Linear-nonlinear-Poisson_cascade_model wasDerivedFrom Linear-nonlinear-Poisson_cascade_model?oldid=521984723.
- Linear-nonlinear-Poisson_cascade_model depiction LNPModelDiagram.png.
- Linear-nonlinear-Poisson_cascade_model isPrimaryTopicOf Linear-nonlinear-Poisson_cascade_model.
