PDP-squared: Meaningful PDP language models using parallel distributed processors.

并行分布式处理（PDP）是一种计算形式，其中大量执行简单计算的处理单元可以一起用于解决更复杂的问题。也许最好的例子是人类的大脑，它包含了大约一千亿个神经元。单独地，这些神经元必须决定是否放电，它们的决定是基于与它们相连的其他神经元最近有多少放电。当这个简单的局部计算分布在数十亿个神经元上时，它就能够支持人类表现出的所有极其复杂的行为，如说话、阅读、走路、跑步等，这些行为远远超出了传统计算机的能力。出于这样或那样的原因，许多心理学家认为PDP模型是描述人类认知的最佳方式。不幸的是，目前这些模型都是用标准pc来模拟的，这意味着模型中的每个单元都必须在串行过程中一个接一个地处理。这种串行处理对可以解决的问题的复杂性施加了严重的限制。我们的目标是了解大脑是如何支持语言功能的，大脑损伤后语言功能是如何分解的，以及支持恢复/康复的机制。这需要一种能够模拟说话、重复、理解、命名和阅读的语言模型。使用现有的基于pc的模拟器来训练这样的模型将花费太长时间/可能超过一生。因此，这个项目的第一个目标是生产一个真正并行（pdp平方）的并行分布式处理机器。我们打算将一万个ARM处理器整合到一台机器中，这台机器将能够以比目前单台pc快500-1000倍的速度运行我们对人类行为的模拟。一旦我们成功生产了这台机器（项目的第一阶段），我们将用它来建立一个正常的人类语言功能模型，可以支持阅读（大声朗读和有意义的阅读）、理解、说话、命名和重复所有的单音节英语单词。我们将通过证明破坏它会导致与脑损伤个体相同的行为模式来验证这个模型（第二阶段）。最后，我们将使用该模型来预测不同语言治疗策略的结果，并将在有语言问题的中风患者群体中测试这些预测。

Parallel Distributed Processing (PDP) is a form of computation where a large number of processing units performing simple calculations can be employed all together to solve much more complex problems. Perhaps the best example of this is the human brain, which contains approximately one hundred billion neurones. Individually these neurones simply have to decide whether to fire or not, and they do this based upon how many other neurones that are connected to them have fired recently. When this simple local computation is distributed over billions of neurones it is capable of supporting all the extremely complex behaviours that humans exhibit / talking, reading, walking, running etc / behaviours that are well beyond the abilities of more traditional computers. For this and other reasons, many psychologists believe that PDP models are the best way of describing human cognition. Unfortunately, at the moment these models are invariably simulated using standard PCs, which means that each unit in the model has to be dealt with one after the other in a serial process. This serial processing imposes severe limitations upon the complexity of problems that can be tackled. Our goal is to us to understand how the brain supports language function, how this breaks down after brain damage and the mechanisms that support recovery/rehabilitation. This will require a model of language that is capable of simulating speech, repetition, comprehension, naming and reading. To train such a model using existing pc-based simulators would take far too long /possibly more than a lifetime. So the first objective of this project is to produce a parallel distributed processing machine that is truly parallel (PDP-squared). We intend to use an array of 10,000 ARM processors incorporated into a machine that will be able to run our simulations of human behaviour 500-1000 times faster than is currently possible on a single pc. Once we have successfully produced this machine (Phase1 of the project), we will use it to build a model of normal human language function that can support reading (both aloud and for meaning), comprehension, speech, naming and repetition for all of the single monosyllabic words in English. We will validate this model by showing that damaging it can lead to the same patterns of behaviour as found in brain damaged individuals (Phase 2). Finally we will use the model to predict the results of different speech therapy strategies and will test these predictions in a population of stroke patients who have linguistic problems.