= Linear-nonlinear-Poisson cascade model
{wiki=Linear-nonlinear-Poisson_cascade_model}
The Linear-Nonlinear-Poisson (LNP) cascade model is a framework used in computational neuroscience to describe how sensory neurons process information. It captures the relationship between the stimuli (inputs) that a neuron receives and its firing rate (output), providing insights into the underlying mechanisms of neural coding. Here's a breakdown of the components of the LNP model: 1. **Linear Component**: The first stage of the model involves a linear transformation of the input stimulus.
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