What is the difference between a neural network and a perceptron? Is there any difference between the terms "neural network" and "perceptron"?
 A: Yes, there is - "perceptron" refers to a particular supervised learning model, which was outlined by Rosenblatt in 1957. The perceptron is a particular type of neural network, and is in fact historically important as one of the types of neural network developed. There are other types of neural network which were developed after the perceptron, and the diversity of neural networks continues to grow (especially given how cutting-edge and fashionable deep learning is these days).
A: Perceptron models are contained within the set of neural net models.
A (single layer) perceptron is a single layer neural network that works as a linear binary classifier. Being a single layer neural network it can be trained without the use of more advanced algorithms like back propagation and instead can be trained by "stepping towards" your error in steps specified by a learning rate. When someone says perceptron, I usually think of the single layer version.
If you're talking about a multilayer perceptron, however, then the term is the same as a feed-forward neural network.
A: Perceptron Learning procedure cannot be generalised to hidden layers 
• The perceptron convergence procedure works by ensuring that
every time the weights change, they get closer to every “generously
feasible” set of weights.
– This type of guarantee cannot be extended to more complex
networks in which the average of two good solutions may be a
bad solution.
• So “multi-layer” neural networks do not use the perceptron learning
procedure.
– They should never have been called multi-layer perceptrons.
-Reference Coursera.org 
- Neural net course - Week 3 
A: As @Nick mentioned Preceptron is a neural network with single layer, which use hand-written programs based on common sense to define the features. This features used as input of network and then make binary decision based on that.

[Image & explanation was based on Hinton Slide's in Coursera]
