Rate-Coding Bundle Memory: A Unified Model of Memory and Control for Symbolic Computation in the Brain
Abstract
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.
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BibTeX
@misc{gils2026ratecoding,
title = {{Rate-Coding Bundle Memory: A Unified Model of Memory and Control for Symbolic Computation in the Brain}},
author = {Teun van Gils and Rowan P. Sommers and Markus Ostarek and Peter Hagoort},
howpublished = {arXiv},
year = {2026},
month = aug,
doi = {10.48550/arxiv.2608.29189},
eprint = {2608.29189},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2608.29189},
note = {CC BY-SA 4.0},
}
RIS
TY - UNPB
TI - Rate-Coding Bundle Memory: A Unified Model of Memory and Control for Symbolic Computation in the Brain
AU - Teun van Gils
AU - Rowan P. Sommers
AU - Markus Ostarek
AU - Peter Hagoort
PY - 2026
DA - 2026/08/29
PB - arXiv
DO - 10.48550/arxiv.2608.29189
UR - https://arxiv.org/abs/2608.29189
AB - 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.
ER -