cellstate

Cellstate refers to the internal memory component of certain recurrent neural networks, most notably the long short-term memory (LSTM) unit. It acts as a conveyor belt for information, allowing signals to flow through time with limited modification, which helps preserve long-range dependencies in sequences such as language or time series. In an LSTM cell, the cell state is updated at each time step by balancing a forget mechanism and an input mechanism that adds new information.

In operation, the network uses three gates to control the flow of information into and out of the cell state. The forget gate decides how much of the previous cell state to retain, the input gate determines how much of a new candidate state to add, and the output gate modulates what portion of the cell state is exposed to the next layer or time step. The updated cell state is a combination of the retained past state and the new information, while the hidden state is typically derived from the current cell state and further processed by the network. In practice, this separation helps mitigate vanishing gradients and supports learning long-range patterns.

Variants and related units include the standard LSTM, where the cell state and hidden state are distinct, and configurations with peephole connections that allow gates to access the cell state directly. In contrast, gated recurrent units (GRUs) do not maintain a separate cell state; they merge memory into a single hidden state, simplifying the architecture.

Origins and usage: the concept was introduced in the late 1990s to address learning long-range dependencies in sequential data. Cell state remains a central idea in many sequence modeling tasks, including language modeling, machine translation, and time-series forecasting. The term is commonly used in documentation and frameworks to describe the c_t component of LSTM cells.