We present a simulated biologically plausible language organ, made up of
stylized but realistic neurons, synapses, brain areas, plasticity, and a
simplified model of sensory perception. We show through experiments that this
model succeeds in an important early step in language acquisition: the learning
of nouns, verbs, and their meanings, from the grounded input of only a modest
number of sentences. Learning in this system is achieved through Hebbian
plasticity, and without backpropagation. Our model goes beyond a parser
previously designed in a similar environment, with the critical addition of a
biologically plausible account for how language can be acquired in the infant's
brain, not just processed by a mature brain.

通过实验，我们展示了一个仿生学上可行的模拟语言器官，由风格化但逼真的神经元、突触、大脑区域、可塑性和感知模型组成，通过希伯规可塑性完成了语言习得的重要早期阶段：只从有限的句子输入中学习名词、动词及其含义。

生物学可信的语言器官的构架

The Architecture of a Biologically Plausible Language Organ

We investigate the state space reconstruction from multiple time series
derived from continuous and discrete systems and propose a method for building
embedding vectors progressively using information measure criteria regarding
past, current and future states. The embedding scheme can be adapted for
different purposes, such as mixed modelling, cross-prediction and Granger
causality. In particular we apply this method in order to detect and evaluate
information transfer in coupled systems. As a practical application, we
investigate in records of scalp epileptic EEG the information flow across brain
areas.

研究多维时间序列的状态空间重建，提出一种使用信息度量准则逐步构建嵌入向量的方法，并以头皮癫痫 EEG 记录为例，探究了耦合系统中的信息流。