Using Stochastic Spiking Neural Networks on SpiNNaker to Solve Constraint Satisfaction Problems

@article{FonsecaGuerra2017UsingSS,
  title={Using Stochastic Spiking Neural Networks on SpiNNaker to Solve Constraint Satisfaction Problems},
  author={Gabriel A. Fonseca Guerra and Stephen B. Furber},
  journal={Frontiers in Neuroscience},
  year={2017},
  volume={11}
}
Constraint satisfaction problems (CSP) are at the core of numerous scientific and technological applications. However, CSPs belong to the NP-complete complexity class, for which the existence (or not) of efficient algorithms remains a major unsolved question in computational complexity theory. In the face of this fundamental difficulty heuristics and approximation methods are used to approach instances of NP (e.g., decision and hard optimization problems). The human brain efficiently handles… 
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