Corpus ID: 1399102

Unsupervised learning models of primary cortical receptive fields and receptive field plasticity

@inproceedings{Saxe2011UnsupervisedLM,
  title={Unsupervised learning models of primary cortical receptive fields and receptive field plasticity},
  author={Andrew M. Saxe and M. Bhand and Ritvik Mudur and B. Suresh and A. Ng},
  booktitle={NIPS},
  year={2011}
}
  • Andrew M. Saxe, M. Bhand, +2 authors A. Ng
  • Published in NIPS 2011
  • Mathematics, Computer Science
  • The efficient coding hypothesis holds that neural receptive fields are adapted to the statistics of the environment, but is agnostic to the timescale of this adaptation, which occurs on both evolutionary and developmental timescales. In this work we focus on that component of adaptation which occurs during an organism's lifetime, and show that a number of unsupervised feature learning algorithms can account for features of normal receptive field properties across multiple primary sensory… CONTINUE READING
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