7 Analysis of neural networks: a random matrix approach System Model The network is fed by a set of T input data vectors x [...rE RPxT and is trained to map corresponding vectors Y =,...RxT hidden layer of n neurons with non-linear activation function.The training phase is particular in that only the hidden layer-to-sink connectivity matrix Be RTx is learnt while the input-to-hidden layer connectivity matrix W E RpxT is static but randomly selected n neurons Y=[1,,r] X=[x1,,xT] 7 o(Wxt) Y≈Y?7 7 Analysis of neural networks: a random matrix approach System Model The network is fed by a set of T input data vectors and is trained to map corresponding vectors hidden layer of n neurons with non-linear activation function . The training phase is particular in that only the hidden layer-to-sink connectivity matrix is learnt while the input-to-hidden layer connectivity matrix is static but randomly selected