用生物真实神经模型实现语言习得，仅凭少量语境句就学会词义和语法。
Simulated Language Acquisition in a Biologically Realistic Model of the Brain
- 基于六条神经科学基本原理构建类脑模拟系统
- 从零开始学习词汇语义、词性及句序，能生成新句子
- 为理解语言如何从神经活动产生提供可计算框架
尽管神经科学取得巨大进展，我们仍缺乏关于大脑神经元放电如何产生高级认知现象（如规划与语言）的清晰叙事。本文提出一个简单的数学形式化框架，包含六个广泛接受的神经科学基本原则：兴奋性神经元、脑区划分、随机突触、赫布可塑性、局部抑制和跨区抑制。基于此框架，我们构建了一个模拟的类脑系统，具备基础语言习得能力：从零开始，在接触少量具身语句后，该系统能在任意语言中学会词语的语义、词性（动词/名词）以及句序结构，并具备生成新句子的能力。本文讨论了该结果的多种可能扩展与意义。
原文摘要 · Abstract (English)
Despite tremendous progress in neuroscience, we do not have a compelling narrative for the precise way whereby the spiking of neurons in our brain results in high-level cognitive phenomena such as planning and language. We introduce a simple mathematical formulation of six basic and broadly accepted principles of neuroscience: excitatory neurons, brain areas, random synapses, Hebbian plasticity, local inhibition, and inter-area inhibition. We implement a simulated neuromorphic system based on this formalism, which is capable of basic language acquisition: Starting from a tabula rasa, the system learns, in any language, the semantics of words, their syntactic role (verb versus noun), and the word order of the language, including the ability to generate novel sentences, through the exposure to a modest number of grounded sentences in the same language. We discuss several possible extensions and implications of this result.
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