# A Brain-inspired Memory Transformation based Differentiable Neural Computer for Reasoning-based Question Answering

arXiv: https://arxiv.org/abs/2301.02809 · HTML: https://mlsanity.com/paper/2301.02809/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 50 references that are in the map.
- First posted on arXiv 7 Jan 2023.

## Builds on (most cited first, 15 of 15)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Neural Turing Machines](https://mlsanity.com/paper/1410.5401/) (2014)
- [Self-Normalizing Neural Networks](https://mlsanity.com/paper/1706.02515/) (2017)
- [Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks](https://mlsanity.com/paper/1502.05698/) (2015)
- [Using Fast Weights to Attend to the Recent Past](https://mlsanity.com/paper/1610.06258/) (2016)
- [Ask Me Anything: Dynamic Memory Networks for Natural Language Processing](https://mlsanity.com/paper/1506.07285/) (2015)
- [Dynamic Memory Networks for Visual and Textual Question Answering](https://mlsanity.com/paper/1603.01417/) (2016)
- [Tracking the World State with Recurrent Entity Networks](https://mlsanity.com/paper/1612.03969/) (2016)
- [Self-Attentive Associative Memory](https://mlsanity.com/paper/2002.03519/) (2020)
- [Scaling Memory-Augmented Neural Networks with Sparse Reads and Writes](https://mlsanity.com/paper/1610.09027/) (2016)
- [Learning to Remember More with Less Memorization](https://mlsanity.com/paper/1901.01347/) (2019)
- [Robust and Scalable Differentiable Neural Computer for Question Answering](https://mlsanity.com/paper/1807.02658/) (2018)
- [Metamorphic Relation Based Adversarial Attacks on Differentiable Neural Computer](https://mlsanity.com/paper/1809.02444/) (2018)

## Built on by (most cited first, 2 of 2)

- [A Framework for Inference Inspired by Human Memory Mechanisms](https://mlsanity.com/paper/2310.09297/) (2023)
- [Advancing Perception in Artificial Intelligence through Principles of Cognitive Science](https://mlsanity.com/paper/2310.08803/) (2023)

## Abstract (arXiv)

Reasoning and question answering as a basic cognitive function for humans, is nevertheless a great challenge for current artificial intelligence. Although the Differentiable Neural Computer (DNC) model could solve such problems to a certain extent, the development is still limited by its high algorithm complexity, slow convergence speed, and poor test robustness. Inspired by the learning and memory mechanism of the brain, this paper proposed a Memory Transformation based Differentiable Neural Computer (MT-DNC) model. MT-DNC incorporates working memory and long-term memory into DNC, and realizes the autonomous transformation of acquired experience between working memory and long-term memory, thereby helping to effectively extract acquired knowledge to improve reasoning ability. Experimental results on bAbI question answering task demonstrated that our proposed method achieves superior performance and faster convergence speed compared to other existing DNN and DNC models. Ablation studies also indicated that the memory transformation from working memory to long-term memory plays essential role in improving the robustness and stability of reasoning. This work explores how brain-inspired memory transformation can be integrated and applied to complex intelligent dialogue and reasoning systems.