@article{Grant_etal17nn,
  title = {Biologically plausible learning in neural networks with modulatory feedback},
  author = {W. S. Grant and J. Tanner and L. Itti},
  abstract = {Although Hebbian learning has long been a key component in understanding neural plasticity, it has not yet been successful in modeling modulatory feedback connections, which make up a significant portion of connections in the brain. We develop a new learning rule designed around the complications of learning modulatory feedback and composed of three simple concepts grounded in physiologically plausible evidence. Using border ownership as a prototypical example, we show that a Hebbian learning rule fails to properly learn modulatory connections, while our proposed rule correctly learns a stimulus-driven model. To the authors' knowledge, this is the first time a border ownership network has been learned. Additionally, we show that the rule can be used as a drop-in replacement for a Hebbian learning rule to learn a biologically consistent model of orientation selectivity, a network which lacks any modulatory connections. Our results predict that the mechanisms we use are integral for learning modulatory connections in the brain and furthermore that modulatory connections have a strong dependence on inhibition.},
  journal = {Neural Networks},
  volume = {88},
  pages = {32-48},
  month = {Jan},
  year = {2017},
  if = {2016 impact factor: 5.287},
  type = {mod},
  file = {http://ilab.usc.edu/publications/doc/Grant_etal17nn.pdf},
}