SenseLab
Computational model
  Data
Nonlinear neuronal computation based on physiologically plausible inputs (McFarland et al. 2013)
"... 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. Furthermore, by providing an explicit probabilistic model with a relatively simple nonlinear structure, its parameters can be efficiently optimized and appropriately regularized. ... ”
  • Neuron or other electrically excitable cell Show Other
  • McFarland JM, Cui Y, Butts DA (2013) Show Other
  • McFarland, James M [jmmcfarl at umd.edu] Show Other
False
False
Other categories referring to Nonlinear neuronal computation based on physiologically plausible inputs (McFarland et al. 2013)
Revisions: 5
Last Time: 6/2/2014 11:53:37 AM
Reviewer: Tom Morse - MoldelDB admin
Owner: Tom Morse - MoldelDB admin