速率编码束记忆：大脑符号计算的记忆与控制统一模型

中文摘要 AI 辅助

我们提出了一种神经生物学上合理的认知模型，结合了联结主义系统与符号系统的优势，可解释广泛的认知现象。该模型名为速率编码束记忆（RCBM），基于符号子系统假说，该假说认为大脑在其根本的联结主义本质中实现了一个符号子系统。RCBM是一种混合模型，使用速率编码在连续空间中表示符号，并采用束记忆系统存储和检索这些符号。该模型能够解决多种认知现象，包括一次学习、模式分离和绑定问题。我们认为RCBM为理解认知的本质提供了一个有前景的框架，未来可用于开发更复杂的认知模型。

英文摘要

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.

发表机构

• Max Planck Institute for Psycholinguistics（马克斯·普朗克心理语言学研究所）
• Donders Institute for Brain, Cognition and Behaviour（唐德斯大脑、认知与行为研究所）
• Radboud University（拉德堡德大学）

机构由 AI 辅助整理，请以论文原文为准。