Learning rule for associative memory in recurrent neural networks | IEEE Conference Publication | IEEE Xplore

Learning rule for associative memory in recurrent neural networks


Abstract:

We present a new learning rule for intralayer connections in neural networks. The rule is based on Hebbian learning principles and is derived from information theoretic c...Show More

Abstract:

We present a new learning rule for intralayer connections in neural networks. The rule is based on Hebbian learning principles and is derived from information theoretic considerations. A simple network trained using the rule is shown to have associative memory like properties. The network acts by building connections between correlated data points, under constraints.
Date of Conference: 12-17 July 2015
Date Added to IEEE Xplore: 01 October 2015
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Conference Location: Killarney, Ireland

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