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

PDP-squared：使用并行分布式处理器的有意义的 PDP 语言模型。

基本信息

批准号：
EP/F03430X/1
负责人：
Stephen Welbourne
金额：
$ 103.49万
依托单位：
University of Manchester
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2008
资助国家：
英国
起止时间：
2008 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FF03430X%2F1
关键词：
PDP squared Meaningful language models

项目摘要

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.

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。也许最好的例子是人类的大脑，它包含大约1000亿个神经元。这些神经元只需要单独决定是否发射，它们根据最近有多少与它们相连的其他神经元发射来决定。当这种简单的本地计算分布在数十亿个神经元上时，它能够支持人类表现出的所有极其复杂的行为（说话、阅读、行走、跑步等），这些行为远远超出了传统计算机的能力。出于这个和其他原因，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串行过程中一个接一个地处理。这种串行处理对可以处理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，大脑损伤后语言功能如何分解，以及支持恢复/康复的机制。这就需要一个能够模拟说话、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一个模型将花费太长的时间/可能超过一生。因此，这个项目的第一个目标是产生一个真正并行（PDP平方）的并行分布式处理机。我们打算将10，000个ARM处理器的阵列集成到一台机器中，这台机器将能够以比目前在单个PC上快500-1000倍的速度运行我们的人类行为模拟。一旦我们成功地生产出这台机器（项目的第一阶段），我们将用它来建立一个正常人类语言功能的模型，它可以支持阅读（包括大声阅读和意义），理解，演讲，命名和重复所有英语单音节单词。我们将通过证明破坏它可以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的语言治疗策略的结果，并将在有语言问题的中风患者人群中测试这些预测。