Problem
Research questions and friction points this paper is trying to address.
Rate-Coding
Bundle Memory
Cognitive Phenomena
Symbolic Computation
Connectionist Systems
Rate-Coding Bundle Memory: A Unified Model of Memory and Control for Symbolic Computation in the Brain
🤖 AI Summary
该研究提出了一种基于率编码和捆绑记忆系统的混合模型RCBM，旨在结合连接主义和符号系统的优势以解释多种认知现象。
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Rate-Coding
Bundle Memory
Symbolic Subsystem Hypothesis
Cognitive Phenomena
🔎 Similar Papers
2024-09-19Citations: 0
