This means the network maintains partial state. By maintaining state, recurrent neural networks can predict values that depend in some way on previous input values. For example, recurrent neural ...

in which information travels in only one direction from input to output. A more widely used type of network is the recurrent neural network, in which data can flow in multiple directions.

The key in feedforward networks is that they always push the input/output forward, never backward, as occurs in a recurrent neural network, discussed next. Recurrent neural networks, or RNNs ...

including recurrent neural networks and feed-forward neural networks, but these are less useful for identifying things like images, which is the example we’re going to use below.) So how do ...

This important study demonstrates the significance of incorporating biological constraints in training neural networks to develop models that make accurate predictions under novel conditions. By ...