The Connectionist approach describes intelligent behavior as activation of networks of connections which map between input from and output to the environment. A brain receives as input a set of sensory activation levels. There are a number of fundamental differences between computers and brains, between symbolic and connectionist approaches to understanding intelligent behavior. Brains are rather slow to settle into a stable pattern of activation. In digital computers information is locally packaged in discrete symbols, but in brains information is distributed continuously across networks.
Brains are better at learning than are digital computers.
Learning is the adaptive change in behavior in response to feedback from the environment. Digital computers are difficult to program to be responsive to feedback, but brains are naturally responsive to feedback. For a given input pattern of activation, network connectivity can be changed by the discrepancy between the actual and desired output patterns of activation. Digital computers and brains seem to behave intelligently in fundamentally different ways, but these are the only intelligent machines we know about. There may be other possibilities beyond out present knowledge.