LSTMs and GRUs are the most common recurrent neural network architectures used to solve temporal sequence problems. The two architectures have differing data flows dealing with a common component called the cell state also referred to as the memory. We attempt to enhance the memory by presenting a biologically inspired modification that we call the Mother Compact Recurrent Memory MCRM. MCRMs are a type of a nested LSTM-GRU architecture where the cell state is the GRU's hidden state. The relationship between the womb and the fetus is analogous to the relationship between the LSTM and GRU inside MCRM in that the fetus is connected to its womb through the umbilical cord. The umbilical cord consists of two arteries and one vein. The two arteries are considered as an input to the fetus which is analogous to the concatenation of the forget gate and input gate from the LSTM. The vein is the output from the fetus which plays the role of the hidden state of the GRU. Because MCRMs has this type of nesting, MCRMs have a compact memory pattern consisting of neurons that act explicitly in both long-term and short-term fashions. For some specific tasks, empirical results show that MCRMs outperform previously used architectures.

本篇论文介绍了一种名为MCRM的卷积神经网络结构，它通过嵌套LSTM和GRU，重点关注于在序列建模中提高记忆储存能力。研究表明，该结构在某些任务中的表现优于传统结构。

MCRM：母简约循环记忆