What are "memory cells" in the context of Recurrent Neural Networks (RNNs)?

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In Recurrent Neural Networks (RNNs), memory cells are indeed components that maintain a state over time. This characteristic allows RNNs to effectively process sequences of data by remembering information from previous time steps while being able to use that information for current and future computations.

Memory cells are crucial for tasks that involve sequential data, such as natural language processing, speech recognition, and time series prediction. By maintaining an internal state, RNNs can capture temporal dependencies and patterns, making them fundamentally different from traditional feedforward neural networks, which do not have this capability to retain information across different inputs.

The other options relate to different concepts; for instance, components that store training data refer to data storage mechanisms, neurons processing visual information pertains more to convolutional neural networks (CNNs), and layers determining the depth of the network are aspects of neural network architecture rather than specific to memory functionality in RNNs.

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