Structure of a fully coupled neural network (IMAGE)

Tokyo University of Science

Structure of a fully coupled neural network

Caption

(a) This diagram depicts fully connected neurons or spins, where each element interacts with every other. (b) Although each spin can only take one of two values, the activation function used to update it is based on the sum of all its interactions, with state transitions aimed at decreasing the overall energy of the network. (c) Different types of networks use different mechanisms to handle state transitions. Ising machines are stochastic, unlike Hopfield networks. 

Credit

Takayuki Kawahara from Tokyo University of Science, Japan

Usage Restrictions

Cannot be reused without permission.

License

Original content

Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.