Memory Capacity of a Random Neural Network

11/14/2012
by   Matt Stowe, et al.
0

This paper considers the problem of information capacity of a random neural network. The network is represented by matrices that are square and symmetrical. The matrices have a weight which determines the highest and lowest possible value found in the matrix. The examined matrices are randomly generated and analyzed by a computer program. We find the surprising result that the capacity of the network is a maximum for the binary random neural network and it does not change as the number of quantization levels associated with the weights increases.

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset