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A modular neural network is one that is composed of more than one neural network model connected by some intermediary. Modular neural networks can allow for sophisticated use of more basic neural network systems managed and handled in conjunction.
Work on modular neural networks dates all the way back to the 1980s and has been explored in several ways. There is the concept of ensemble learning, where a collection of "simple" or "weak" learners can outpace one deep learning model. Important principles in modular neural network development include the "divide and conquer" principal, which breaks large problems down into more viable parts, and diversity promotion, which some experts describe as a biologically-based model where different types of neural networks cooperate, each fulfilling a different role or function.
Experts may also talk about tightly and loosely coupled modular neural network models depending on the specific relationship of the network components.
In general, modular neural networks allow engineers to broaden the possibilities of using these technologies to push the boundaries of what neural networks can accomplish.