• Researchers used a special memristor that memorises events only in the near history.
• Inspired by brains, neural networks are composed of neurons, or nodes, and synapses, the connections between nodes.
• To train a neural network for a task, a neural network takes in a large set of questions and the answers to those questions.
• In this process of what’s called supervised learning, the connections between nodes are weighted more heavily or lightly to minimise the amount of error in achieving the correct answer.
• Once trained, a neural network can then be tested without knowing the answer. For example, a system can process a new photo and correctly identify a human face, because it has learned the features of human faces from other photos in its training set.
• Reservoir computing systems built with memristors can skip most of the expensive training process and still provide the network the capability to remember.
• This is because the most critical component of the system – the reservoir – does not require training.
• When a set of data is inputted into the reservoir, the reservoir identifies important time-related features of the data, and hands it off in a simpler format to a second network.
• This second network then only needs training like simpler neural networks, changing weights of the features and outputs that the first network passed on until it achieves an acceptable level of error.

Use:

• The system can predict words before they are said during conversation, and help predict future outcomes based on the present.

Source:TH