These are that learning should be spread over a number of simple processing units, that learning should modify some attribute of the connections between the units or the units themselves, and that processing should be local. Local processing means that, ignoring synchronising signals, it should be possible for each neuron to process individually using the stimuli available to it, and to learn by modifying only itself or the attributes of connections directly attached to it. There are several successful algorithms that modify all the parameters of a net at once in order to effect learning, and can only be formulated to act in this fashion. As useful as these algorithms may be they are not neural. In back propagation neural nets a neuron has a simple structure.
Synapses bring signal values to the input of the neuron.
These values are real numbers and are summed at this node. The output is formed by applying a sigmoidal activation function to the sum. The signals they carry are real numbers representing input values to the network or excitation values of neurons. A synapse has a weight associated with it that scales the signal passing through it. These weights are the only part of the model that is typically modified while training the network.