Sparse Coding Using the Locally Competitive Algorithm on the TrueNorth Neurosynaptic System

@article{Fair2019SparseCU,
  title={Sparse Coding Using the Locally Competitive Algorithm on the TrueNorth Neurosynaptic System},
  author={Kaitlin L. Fair and Daniel R. Mendat and Andreas G. Andreou and Christopher J. Rozell and Justin K. Romberg and David V. Anderson},
  journal={Frontiers in Neuroscience},
  year={2019},
  volume={13}
}
The Locally Competitive Algorithm (LCA) is a biologically plausible computational architecture for sparse coding, where a signal is represented as a linear combination of elements from an over-complete dictionary. In this paper we map the LCA algorithm on the brain-inspired, IBM TrueNorth Neurosynaptic System. We discuss data structures and representation as well as the architecture of functional processing units that perform non-linear threshold, vector-matrix multiplication. We also present… 
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