Extending Transfer Entropy Improves Identification of Effective Connectivity in a Spiking Cortical Network Model

@inproceedings{Ito2011ExtendingTE,
  title={Extending Transfer Entropy Improves Identification of Effective Connectivity in a Spiking Cortical Network Model},
  author={Shinya Ito and Michael E. Hansen and Randy Heiland and Andrew Lumsdaine and Alan M. Litke and John M. Beggs},
  booktitle={PloS one},
  year={2011}
}
Transfer entropy (TE) is an information-theoretic measure which has received recent attention in neuroscience for its potential to identify effective connectivity between neurons. Calculating TE for large ensembles of spiking neurons is computationally intensive, and has caused most investigators to probe neural interactions at only a single time delay and at a message length of only a single time bin. This is problematic, as synaptic delays between cortical neurons, for example, range from one… CONTINUE READING
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