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

The Kalman filter is a fundamental filtering algorithm that fuses noisy
sensory data, a previous state estimate, and a dynamics model to produce a
principled estimate of the current state. It assumes, and is optimal for,
linear models and white Gaussian noise. Due to its relative simplicity and
general effectiveness, the Kalman filter is widely used in engineering
applications. Since many sensory problems the brain faces are, at their core,
filtering problems, it is possible that the brain possesses neural circuitry
that implements equivalent computations to the Kalman filter. The standard
approach to Kalman filtering requires complex matrix computations that are
unlikely to be directly implementable in neural circuits. In this paper, we
show that a gradient-descent approximation to the Kalman filter requires only
local computations with variance weighted prediction errors. Moreover, we show
that it is possible under the same scheme to adaptively learn the dynamics
model with a learning rule that corresponds directly to Hebbian plasticity. We
demonstrate the performance of our method on a simple Kalman filtering task,
and propose a neural implementation of the required equations.

本文介绍了一种基于梯度下降逼近的 Kalman 滤波方法，其仅需要进行加权预测误差的局部计算，同时还提出了一种适应性学习规则。作者在一个简单的 Kalman 滤波任务中展示了该方法的性能，并提出了一种神经实现方法。