Hebbian Learning of Bayes Optimal Decisions

@inproceedings{Nessler2008HebbianLO,
  title={Hebbian Learning of Bayes Optimal Decisions},
  author={Bernhard Nessler and Michael Pfeiffer and Wolfgang Maass},
  booktitle={NIPS},
  year={2008}
}
Uncertainty is omnipresent when we perceive or interact with our environment, and the Bayesian framework provides computational methods for dealing with it. Mathematical models for Bayesian decision making typically require datastructures that are hard to implement in neural networks. This article shows that even the simplest and experimentally best supported type of synaptic plasticity, Hebbian learning, in combination with a sparse, redundant neural code, can in principle learn to infer… CONTINUE READING
11 Citations
15 References
Similar Papers

Citations

Publications citing this paper.
Showing 1-10 of 11 extracted citations

References

Publications referenced by this paper.
Showing 1-10 of 15 references

Journal version

  • B. Nessler, M. Pfeiffer, W. Maass
  • PREPARATION
  • 2009
3 Excerpts

Shadlen . Probabilistic reasoning by neurons

  • N. M.
  • Nature
  • 2007

On discriminative vs

  • A. Y. Ng, M. I. Jordan
  • generative classifiers. NIPS, 14:841–848
  • 2002
1 Excerpt

Similar Papers

Loading similar papers…