Q: What are neural network models?

Roger Stanton: Neural network models, by their standard definition, are simplified models of our biological neurons. Neural network models allow cognitive scientists to simulate intelligent behavior.

Q: How were neural network models first developed by scientists?

Roger Stanton: In computer science, as well as a host of related fields, these models were inspired by the central nervous systems of animals, which are capable of pattern recognition and machine learning. They have been generally presented as systems of interconnected “neurons,” known for computing values of the entity by feeding the given information into the network.

Q: What is an example of a neural network model?

Roger Stanton: In handwriting recognition, a certain set of these input neurons can be activated by an input image’s pixels that represent a digit or letter. The neurons’ activations are passed on, and then weighted and transformed by a function as created by the designer of the network. Finally, the output neuron will be activated to ascertain which of the characters are read.

Q: Where have neural network models been used?

Roger Stanton: Neural network models have been implemented in order to solve a number of tasks that would be difficult to solve with the utilization of ordinary programming, including speech recognition and computer vision.

Q: In your opinion, what is the most attractive feature of neural network models?

Roger Stanton: Neural network models are heralded for their ability to offer numerous options in terms of modifying training algorithms. As a general rule, default values are set to provide excellent results for a wide range of problems, allowing a researcher to start experimentation quickly and with fewer challenges on the front end. As a researcher gains more knowledge and experience, it becomes more natural to customize the algorithms as a way to improve the accuracy, speed and performance of these neural network models.