Which neural network is designed to model sequential interactions through a hidden state?

The Recurrent Neural Network (RNN) is specifically designed to process sequences of data by maintaining a hidden state that carries information about previous inputs. This architecture is particularly well-suited for tasks where context and order matter, such as time series prediction, natural language processing, and speech recognition.

RNNs function by taking input data sequentially and updating the hidden state with each time step, allowing them to capture temporal dependencies. This hidden state serves as a memory that helps the network retain information from previous inputs, making it capable of modeling relationships in sequential data effectively.

In contrast, other models like Convolutional Neural Networks (CNNs) are primarily focused on spatial hierarchies in data, such as images, rather than the sequential nature of data. Support Vector Machines (SVMs) are used for classification and regression tasks without considering the order of data points, while Generative Adversarial Networks (GANs) are focused on generating new data samples from a learned distribution and do not specifically deal with sequential interactions. Therefore, RNN is the only neural network among the options provided that is adept at modeling sequences through its unique hidden state mechanism.