A theoretical framework derives analytical approximations for mutual information between data patterns and synaptic connections in Hebbian autoassociative networks using log-normal pattern distributions.
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Quantifying information stored in synaptic connections rather than in firing activities of neural networks
A theoretical framework derives analytical approximations for mutual information between data patterns and synaptic connections in Hebbian autoassociative networks using log-normal pattern distributions.