Regulatory feedback network

Regulatory feedback networks are neural networks that perform inference using Negative feedback.[1] The feedback is not used to find optimal learning or training weights but to find the optimal activation of nodes. In effect this approach is most similar to a non-parametric method but is different from K-nearest neighbors in that it can be shown to mathematically emulate feedforward neural networks.

Network origins and use

Regulatory feedback networks started as a model to explain brain phenomena found during recognition including network-wide bursting and difficulty with similarity found universally in sensory recognition.[2] This approach can also perform mathematically equivalent classification as feedforward methods and is used as a tool to create and modify networks.[3][4]

See also

References

  1. ↑ Achler T., Omar C., Amir E., “Shedding Weights: More With Less”, IEEE Proc. International Joint Conference on Neural Networks, 2008
  2. ↑ Tsvi Achler (2016-02-08), Neural Phenomena Focus, retrieved 2016-08-29
  3. ↑ fernandez, ed (2016-02-09). "Two Duck-Rabbit Paradigm-Shift Anomalies in Physics and One (maybe) in Machine Learning". Medium. Retrieved 2016-08-29.
  4. ↑ Tsvi Achler (2016-04-29), Technical Video for Optimizing Mind, retrieved 2016-08-29
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