A Hebbian/Anti-Hebbian network derived from online non-negative matrix factorization can cluster and discover sparse features

@article{Pehlevan2014AHN,
  title={A Hebbian/Anti-Hebbian network derived from online non-negative matrix factorization can cluster and discover sparse features},
  author={Cengiz Pehlevan and Dmitri B. Chklovskii},
  journal={2014 48th Asilomar Conference on Signals, Systems and Computers},
  year={2014},
  pages={769-775}
}
Despite our extensive knowledge of biophysical properties of neurons, there is no commonly accepted algorithmic theory of neuronal function. Here we explore the hypothesis that single-layer neuronal networks perform online symmetric nonnegative matrix factorization (SNMF) of the similarity matrix of the streamed data. By starting with the SNMF cost function we derive an online algorithm, which can be implemented by a biologically plausible network with local learning rules. We demonstrate that… CONTINUE READING
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