@article{8017,
  abstract     = {nhibitory neurons, although relatively few in number, exert powerful control over brain circuits. They stabilize network activity in the face of strong feedback excitation and actively engage in computations. Recent studies reveal the importance of a precise balance of excitation and inhibition in neural circuits, which often requires exquisite fine-tuning of inhibitory connections. We review inhibitory synaptic plasticity and its roles in shaping both feedforward and feedback control. We discuss the necessity of complex, codependent plasticity mechanisms to build nontrivial, functioning networks, and we end by summarizing experimental evidence of such interactions.},
  author       = {Hennequin, Guillaume and Agnes, Everton J. and Vogels, Tim P},
  issn         = {0147-006X},
  journal      = {Annual Review of Neuroscience},
  number       = {1},
  pages        = {557--579},
  publisher    = {Annual Reviews},
  title        = {{Inhibitory plasticity: Balance, control, and codependence}},
  doi          = {10.1146/annurev-neuro-072116-031005},
  volume       = {40},
  year         = {2017},
}

@article{8029,
  abstract     = {Neural network modeling is often concerned with stimulus-driven responses, but most of the activity in the brain is internally generated. Here, we review network models of internally generated activity, focusing on three types of network dynamics: (a) sustained responses to transient stimuli, which provide a model of working memory; (b) oscillatory network activity; and (c) chaotic activity, which models complex patterns of background spiking in cortical and other circuits. We also review propagation of stimulus-driven activity through spontaneously active networks. Exploring these aspects of neural network dynamics is critical for understanding how neural circuits produce cognitive function.},
  author       = {Vogels, Tim P and Rajan, Kanaka and Abbott, L.F.},
  issn         = {0147-006X},
  journal      = {Annual Review of Neuroscience},
  number       = {1},
  pages        = {357--376},
  publisher    = {Annual Reviews},
  title        = {{Neural network dynamics}},
  doi          = {10.1146/annurev.neuro.28.061604.135637},
  volume       = {28},
  year         = {2005},
}