These results were very encouraging, but these networks displayed no learning. One had to figure out the weights and connect up the neurons in the appropriate manner to perform the desired computation. Thus there is no real advantage over any conventional digital logic circuit. Their main importance was that they showed that networks of simple neuron-like elements could compute. The next major advance was the perceptron, introduced by Frank Rosenblatt in his 1958 paper.
Weights can be positive or negative.
There is no absolute inhibitory synapse. Most importantly, there was a learning rule. The i th perceptron receives its input from n input units, which do nothing but pass on the input from the outside world. Note that they don't interact with each other - they receive inputs only from the outside. They exist just to provide an output which is equal to the external input to the net.