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The first analysis of the information storage capacity of a neurone that has been constrained experimentally - in this case using the distribution of synaptic weights. A surprising result was that optimal learning leads to silencing of the majority of the excitatory synapses in the perception model.
This analysis opens the door to further research based upon distributions of synaptic weights, which we have shown can yield a surprising amount of information about brain function that is difficult to address in other ways.
The new understanding of the importance of silent synapses in optimal learning may prove useful in efficiently implementing neural networks.
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