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，其中执行简单计算的大量处理单元可以一起使用来解决更复杂的问题。也许这方面最好的例子是人类的大脑，它包含大约1000亿个神经元。单独来说，这些神经元只需决定是否发射，它们是根据最近与它们连接的其他神经元发射的数量来决定的。当这种简单的局部计算分布在数十亿个神经元上时，它能够支持人类展示的所有极其复杂的行为/说话、阅读、行走、跑步等/行为远远超出了更传统的计算机的能力。由于这个和其他原因，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，目前这些模型总是使用标准PC进行模拟，这意味着模型中的每个单元都必须在一个连续的过程中一个接一个地处理。这种连续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如何支持语言功能，这种功能在脑损伤后是如何分解的，以及支持恢复/康复的机制。这将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的时间，甚至可能超过一生。因此，这个项目的第一个目标是生产一个真正并行的并行分布式处理机(PDP平方)。我们打算将10,000个ARM处理器阵列整合到一台机器中，这台机器运行我们对人类行为的模拟的速度将比目前在一台PC上可能的速度快500-1000倍。一旦我们成功地生产了这台机器(项目的第一阶段)，我们将使用它来构建一个正常人类语言功能的模型，该模型可以支持英语中所有单音节单词的阅读(包括朗读和意义)、理解、语音、命名和重复。我们将通过证明损伤大脑可以导致与大脑受损个体相同的行为模式来验证这一模型(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，并将在一组有语言问题的中风患者中测试这些预测。

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.