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Multi-layer development network having in-place learning

  • US 7,711,663 B2
  • Filed: 03/27/2007
  • Issued: 05/04/2010
  • Est. Priority Date: 03/27/2006
  • Status: Active Grant
First Claim
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1. A multi-layer in-place learning developmental network, comprising:

  • an input layer having a plurality of artificial neurons;

    multiple intermediate layers having a plurality of artificial neurons, each intermediate layer having artificial neurons that receive an input vector signal from neurons in previous layers;

    an input vector signal from neurons in the same layer and an input vector signal from neurons in subsequent layers; and

    an output layer having a plurality of artificial neurons, where the neurons in the network implement an in-place learning algorithm that estimates a feature vector of a given neuron by an amnesic average of input vectors weighted by corresponding response of the given neuron, where amnesic is a recursive, incremental computation of the input vector weighted by the response, such that a direction of the feature vector and a variance of signals in the region projected to the feature vector are both recursively estimated with a plasticity scheduling.

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