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.

Neurons and Cognition (q-bio.NC); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Symbolic Computation (cs.SC)

arXiv:2608.29189 [q-bio.NC]

(or arXiv:2608.29189v1 [q-bio.NC] for this version)

https://doi.org/10.48550/arXiv.2608.29189 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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