Here we present an approach for modeling sensory processing, termed the Nonlinear Input
Model (NIM), which is based on the hypothesis that the dominant nonlinearities imposed by physiological mechanisms arise
from rectification of a neuron’s inputs. Incorporating such ‘upstream nonlinearities’ within the standard linear-nonlinear (LN)
cascade modeling structure implicitly allows for the identification of multiple stimulus features driving a neuron’s response,
which become directly interpretable as either excitatory or inhibitory.
Because its form is analogous to an integrate-and-fire
neuron receiving excitatory and inhibitory inputs, model fitting can be guided by prior knowledge about the inputs to a
given neuron, and elements of the resulting model can often result in specific physiological predictions.
providing an explicit probabilistic model with a relatively simple nonlinear structure, its parameters can be efficiently
optimized and appropriately regularized. ... ”