Unsupervised Bayesian Ising Approximation for revealing the neural dictionary in songbirds

@article{Hernandez2019UnsupervisedBI,
  title={Unsupervised Bayesian Ising Approximation for revealing the neural dictionary in songbirds},
  author={Dami'an G. Hern'andez and S. Sober and I. Nemenman},
  journal={bioRxiv},
  year={2019}
}
The problem of deciphering how low-level patterns (action potentials in the brain, amino acids in a protein, etc.) drive high-level biological features (sensorimotor behavior, enzymatic function) represents the central challenge of quantitative biology. The lack of general methods for doing so from the size of datasets that can be collected experimentally severely limits our understanding of the biological world. For example, in neuroscience, some sensory and motor codes have been shown to… Expand

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