用神经编码模拟符号计算，解释大脑如何快速学习与记忆
Rate-Coding Bundle Memory: A Unified Model of Memory and Control for Symbolic Computation in the Brain
- 用连续神经放电率表示符号，结合记忆库实现符号存储与检索
- 可解决一次性学习、模式分离和符号绑定等认知难题
- 为理解大脑认知机制提供新框架，适合认知科学与类脑模型研究者
我们提出一种神经生物学上合理的认知模型——速率编码束记忆（Rate-Coding Bundle Memory, RCBM），融合连接主义与符号系统的优势，解释多种认知现象。该模型基于符号子系统假说，认为大脑在本质上是连接主义的，但内部存在符号处理机制。RCBM利用速率编码在连续空间中表示符号，并通过束记忆系统存储与检索这些符号。模型能够解决包括一次性学习、模式分离和符号绑定在内的广泛认知问题。我们认为RCBM为理解认知本质提供了有前景的框架，未来可用于构建更复杂的认知模型。
原文摘要 · Abstract (English)
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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