# Rate-Coding Bundle Memory: A Unified Model of Memory and Control for Symbolic Computation in the Brain

arXiv: https://arxiv.org/abs/2608.29189 · HTML: https://mlsanity.com/paper/2608.29189/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 211 references that are in the map.
- First posted on arXiv 29 Aug 2026.

## Builds on (most cited first, 14 of 14)

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- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [What Does BERT Look At? An Analysis of BERT's Attention](https://mlsanity.com/paper/1906.04341/) (2019)
- [How do Language Models Bind Entities in Context?](https://mlsanity.com/paper/2310.17191/) (2023)
- [A comparison of Vector Symbolic Architectures](https://mlsanity.com/paper/2001.11797/) (2020)
- [Rearranging the Familiar: Testing Compositional Generalization in Recurrent Networks](https://mlsanity.com/paper/1807.07545/) (2018)
- [Representing Objects, Relations, and Sequences](https://mlsanity.com/paper/1501.07627/) (2015)
- [How Do Transformers Learn Variable Binding in Symbolic Programs?](https://mlsanity.com/paper/2505.20896/) (2025)
- [Probing for Bridging Inference in Transformer Language Models](https://mlsanity.com/paper/2104.09400/) (2021)
- [Replay and compositional computation](https://mlsanity.com/paper/2209.07453/) (2022)
- [Hebbian fast plasticity and working memory](https://mlsanity.com/paper/2304.06626/) (2023)
- [One-Shot Learning in Discriminative Neural Networks](https://mlsanity.com/paper/1707.05562/) (2017)
- [The relational processing limits of classic and contemporary neural network models of language processing](https://mlsanity.com/paper/1905.05708/) (2019)

## Abstract (arXiv)

We propose a neurobiologically plausible model of cognition that combines the advantages of connectionist and symbolic systems, and that can explain a wide range of cognitive phenomena. This model, called Rate-Coding Bundle Memory (RCBM), is based on the Symbolic Subsystem Hypothesis, which posits that the brain implements a symbolic subsystem within its fundamentally connectionist nature. RCBM is a hybrid model that uses rate coding to represent symbols in a continuous space, and it uses a bundle memory system to store and retrieve these symbols. The model is capable of solving a wide range of cognitive phenomena, including one-shot learning, pattern separation, and the binding problem. We argue that RCBM provides a promising framework for understanding the nature of cognition, and that it can be used to develop more sophisticated models of cognition in the future.