Quantifying variability in neural responses and its application for the validation of model predictions.

@article{Hsu2004QuantifyingVI,
  title={Quantifying variability in neural responses and its application for the validation of model predictions.},
  author={Anne Hsu and Alexander Borst and Fr{\'e}d{\'e}ric E. Theunissen},
  journal={Network},
  year={2004},
  volume={15 2},
  pages={91-109}
}
A rate code assumes that a neuron's response is completely characterized by its time-varying mean firing rate. This assumption has successfully described neural responses in many systems. The noise in rate coding neurons can be quantified by the coherence function or the correlation coefficient between the neuron's deterministic time-varying mean rate and noise corrupted single spike trains. Because of the finite data size, the mean rate cannot be known exactly and must be approximated. We… CONTINUE READING
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