Simple Recurrent Network
A Simple Recurrent Network (SRN), also known as an Elman network, is a type of recurrent neural network (RNN) architecture that includes a context layer to maintain a memory of previous inputs, enabling it to process sequential data. It was introduced by Jeffrey Elman in 1990 and is designed to handle temporal dependencies by feeding hidden layer activations back into the network through recurrent connections. This allows it to learn patterns over time, making it suitable for tasks like time-series prediction, natural language processing, and speech recognition.
Developers should learn SRNs when working on projects involving sequential data where past context influences current predictions, such as in language modeling, time-series forecasting, or any application requiring memory of previous states. It's particularly useful for educational purposes to understand the basics of recurrent networks before advancing to more complex architectures like LSTMs or GRUs. However, due to issues like vanishing gradients, it's often replaced by these more advanced RNNs in modern deep learning applications.