# A model for structured information representation in neural networks

arXiv: https://arxiv.org/abs/1611.03698 · HTML: https://mlsanity.com/paper/1611.03698/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Nov 2016.

## Built on by (most cited first, 7 of 7)

- [Pointer Value Retrieval: A new benchmark for understanding the limits of neural network generalization](https://mlsanity.com/paper/2107.12580/) (2021)
- [Simplicial Hopfield networks](https://mlsanity.com/paper/2305.05179/) (2023)
- [Semantically-correlated memories in a dense associative model](https://mlsanity.com/paper/2404.07123/) (2024)
- [Sentences as connection paths: A neural language architecture of sentence structure in the brain](https://mlsanity.com/paper/2206.01725/) (2022)
- [The Spike Gating Flow: A Hierarchical Structure Based Spiking Neural Network for Online Gesture Recognition](https://mlsanity.com/paper/2206.01910/) (2022)
- [Towards a neural architecture of language: Deep learning versus logistics of access in neural architectures for compositional processing](https://mlsanity.com/paper/2210.10543/) (2022)
- [Winner-Take-All bottlenecks enforce disentangled symbolic representations in multi-task learning](https://mlsanity.com/paper/2605.22472/) (2026)

## Abstract (arXiv)

Humans possess the capability to reason at an abstract level and to structure information into abstract categories, but the underlying neural processes have remained unknown. Experimental evidence has recently emerged for the organization of an important aspect of abstract reasoning: for assigning words to semantic roles in a sentence, such as agent (or subject) and patient (or object). Using minimal assumptions, we show how such a binding of words to semantic roles emerges in a generic spiking neural network through Hebbian plasticity. The resulting model is consistent with the experimental data and enables new computational functionalities such as structured information retrieval, copying data, and comparisons. It thus provides a basis for the implementation of more demanding cognitive computations by networks of spiking neurons.