Associative Memory in Damaged Neural Networks
نویسنده
چکیده
The content-addressability of random patterns stored in neural networks trained with iterative learning rules is studied numerically under the eeects of synaptic damage. The model of damage is random dilution of couplings with some probability after learning. A simple model based on the distribution of stabilities () is proposed, which allows for the calculation of the storage and retrieval properties of the networks as a function of damage. Simulations of random sparse connected networks show that the basins of attraction are indeed optimal at storage ratios 0:42. 2
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