Highly connected dynamic artificial neural networks
This work addresses the need for more flexible and adaptable neural network architectures, though it appears incremental as it builds on existing methods without claiming major performance gains.
The authors introduced an object-oriented approach to implement artificial neural networks that are highly connected and dynamic, allowing edges between any layers and easy modifications of nodes, edges, or layers, with methods for feedforward and backpropagation.
An object-oriented approach to implementing artificial neural networks is introduced in this article. The networks obtained in this way are highly connected in that they admit edges between nodes in any layers of the network, and dynamic, in that the insertion, or deletion, of nodes, edges or layers of nodes can be effected in a straightforward way. In addition, the activation functions of nodes need not be uniform within layers, and can also be changed within individual nodes. Methods for implementing the feedforward step and the backpropagation technique in such networks are presented here. Methods for creating networks, for implementing the various dynamic properties and for saving and recreating networks are also described.