# Neural Network Memory Cell

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A Neural Network Memory Cell is a Neural Network Cell that can be activated by an Attention Mechanism.

## References =

### 2018a

• (Wikipedia, 2018) ⇒ https://en.wikipedia.org/wiki/long_short-term_memory Retrieved:2018-3-27.
• Long short-term memory (LSTM) units (or blocks) are a building unit for layers of a recurrent neural network (RNN). A RNN composed of LSTM units is often called an LSTM network. A common LSTM unit is composed of a cell, an input gate, an output gate and a forget gate. The cell is responsible for "remembering" values over arbitrary time intervals; hence the word "memory" in LSTM. Each of the three gates can be thought of as a "conventional" artificial neuron, as in a multi-layer (or feedforward) neural network: that is, they compute an activation (using an activation function) of a weighted sum. Intuitively, they can be thought as regulators of the flow of values that goes through the connections of the LSTM; hence the denotation "gate". There are connections between these gates and the cell.

The expression long short-term refers to the fact that LSTM is a model for the short-term memory which can last for a long period of time. An LSTM is well-suited to classify, process and predict time series given time lags of unknown size and duration between important events. LSTMs were developed to deal with the exploding and vanishing gradient problem when training traditional RNNs. Relative insensitivity to gap length gives an advantage to LSTM over alternative RNNs, hidden Markov models and other sequence learning methods in numerous applications.

### 2014

Figure 1: Illustration of (a) LSTM and (b) gated recurrent units. (a) $i$, $f$ and $o$ are the input, forget and output gates, respectively. $c$ and $\tilde{c}$ denote the memory cell and the new memory cell content. (b) $r$ and $z$ are the reset and update gates, and $h$ and $\tilde{h}$ are the activation and the candidate activation.