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

arXiv 2608.29189 ↗First posted 29 Aug 2026q-bio.NC cs.AI cs.CLcited by 0

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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.

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References · 14

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2 more
  1. 2017One-Shot Learning in Discriminative Neural Networkscited by 1
  2. 2019The relational processing limits of classic and contemporary neural network models of language processingcited by 1

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