Inferring input nonlinearities in neural encoding models.

@article{Ahrens2008InferringIN,
  title={Inferring input nonlinearities in neural encoding models.},
  author={Misha B Ahrens and Liam Paninski and Maneesh Sahani},
  journal={Network},
  year={2008},
  volume={19 1},
  pages={35-67}
}
We describe a class of models that predict how the instantaneous firing rate of a neuron depends on a dynamic stimulus. The models utilize a learnt pointwise nonlinear transform of the stimulus, followed by a linear filter that acts on the sequence of transformed inputs. In one case, the nonlinear transform is the same at all filter lag-times. Thus, this "input nonlinearity" converts the initial numerical representation of stimulus value to a new representation that provides optimal input to… CONTINUE READING

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