weights determine an organizational topology for a network and allow units to
send activation to each other. In a very rough sense, connection weights in
ANNs can be regarded as analogous to neural synapses.
Input units code
the problem being presented to the network. Output units code the network’s
response to the input problem. Hidden units perform essential intermediate
computations. Psychologically, the matrix of connection weights can be
regarded as a network’s long-term memory. In cascade-correlation, there are
cross-connections that bypass hidden units.