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Bridging Computer Science with Neuroscience towards a new understanding of reasoning

将计算机科学与神经科学联系起来，以获得对推理的新理解

基本信息

批准号：
1778161
负责人：
金额：
--
依托单位：
University of Cambridge
依托单位国家：
英国
项目类别：
Studentship
财政年份：
2016
资助国家：
英国
起止时间：
2016 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=studentship-1778161
关键词：
Bridging Computer Science Neuroscience towards

项目摘要

This PhD research project aims to bridge automated reasoning and neuroscience to shed light on designing more human-like computing system. Traditional machine reasoning, usually designed to follow a set of pre-defined logical rules, performs well for logical symbolic reasoning such as mathematical theorem proving. However, such symbolic logic rule based systems are often not able to deal with reasoning tasks that contain multi-modal heterogeneous information (including visual, audial information, etc.). Human brains, on the other hand, are very efficient at reasoning with such informal, heterogeneous and approximate reasoning tasks. By studying brain neural-activities when people are actively reasoning about a task, we can gain a deeper understanding of the nature of human reasoning, and develop systems that emulate such reasoning processes.Many researches have been developing machine learning models such as Artificial Neural Networks (ANN) and Hierarchical Hidden Markov Models that emulate the functioning of the brain. One particular type of ANN, the Convolutional Neural Network (CNN), has been particularly successful recently in image processing and speech recognition tasks. CNN is inspired in studying the neural structures of the visual cortex. CNN has become popular recently because of improvement in parallel and distributed computing technology, mainly the GPU parallel computing technology. Parallel computing allows much deeper neural networks to be trained in feasible time. Researches have simulating human visual cortex in tasks of object recognition with CNN. However, little research has been done on emulating the reasoning engine, the pre-frontal cortex (PFC). This project aims to study neural activities in PFC while people are undertaking reasoning, and shed light on developing a new type of artificial neural system for automated reasoning.There are many types of reasoning, such as verbal reasoning, visual (diagrammatic) reasoning and symbolic reasoning. Different types of reasoning activate PFC and specific somatosensory cortex together. For example, PFC and visual cortex are activated in the process of creating mental models for visual relations. Visual reasoning is the most common type of reasoning in mammals with neo-cortex. Visual reasoning is arguably more primary in an evolutional sense than other types of reasoning. Moreover visual reasoning has been widely studied in the neuroscience community. Therefore, in this project I plan to conduct research into visual reasoning in the first phase, and then extend to other types of reasoning. Functional Magnetic Resonance Imaging (fMRI) allows us to monitor neural activities inside the brain by measuring Blood Oxygen Level Dependent (BOLD) response. Higher level of neural activities in certain area of the brain corresponds to increased level of blood oxygen consumption in that area. With fMRI we can monitor patterns of neural activations inside PFC of people undertaking reasoning tasks. These patterns of neural activations can then be analyzed to shed light on the neural computational processes of reasoning tasks. With knowledge of the neural computational processes, we can modify existing neural networks (such as Deep Belief Network, Recurrent Neural Network, and Convolutional Neural Network) to allow better mapping on to the neural circuitries inside PFC, or propose new types of neural computational models that more accurately captures the neural processes. We can also use these insights to guide automated reasoning systems' reasoning processes in the form of heuristics - these would reflect human reasoning more, as well as make automated systems more human-like and thus accessible.

这个博士研究项目旨在弥合自动推理和神经科学，以揭示设计更人性化的计算系统。传统的机器推理通常被设计为遵循一组预定义的逻辑规则，对于逻辑符号推理（如数学定理证明）表现良好。然而，这种基于符号逻辑规则的系统往往不能处理包含多模态异构信息（包括视觉，听觉信息等）的推理任务。另一方面，人类的大脑在进行这种非正式的、异质的和近似的推理任务时非常有效。通过研究人在主动推理时的脑神经活动，我们可以更深入地了解人类推理的本质，并开发模拟这种推理过程的系统。许多研究一直在开发模拟大脑功能的机器学习模型，如人工神经网络（ANN）和分层隐马尔可夫模型。一种特殊类型的ANN，卷积神经网络（CNN），最近在图像处理和语音识别任务中特别成功。CNN的灵感来自于研究视觉皮层的神经结构。近年来，随着并行和分布式计算技术的发展，尤其是GPU并行计算技术的发展，CNN成为一种新兴的计算技术。并行计算允许在可行的时间内训练更深的神经网络。已有研究用CNN模拟人类视觉皮层的物体识别任务。然而，很少有研究已经做了模拟的推理引擎，前额叶皮层（PFC）。本项目旨在研究人在进行推理时前额叶皮层的神经活动，为开发一种新型的人工神经系统提供指导。推理有多种类型，如语言推理、视觉（图解）推理和符号推理。不同类型的推理同时激活PFC和特定的躯体感觉皮层。例如，PFC和视觉皮层在创建视觉关系的心理模型的过程中被激活。视觉推理是具有新皮层的哺乳动物中最常见的推理类型。视觉推理在进化的意义上比其他类型的推理更重要。此外，视觉推理在神经科学界也得到了广泛的研究。因此，在这个项目中，我计划在第一阶段对视觉推理进行研究，然后扩展到其他类型的推理。功能性磁共振成像（fMRI）允许我们通过测量血氧水平依赖（BOLD）反应来监测大脑内的神经活动。大脑某些区域的神经活动水平越高，该区域的血氧消耗水平就越高。通过功能磁共振成像，我们可以监测人们在执行推理任务时前额叶皮层内的神经激活模式。然后，可以分析这些神经激活模式，以揭示推理任务的神经计算过程。有了神经计算过程的知识，我们可以修改现有的神经网络（如深度信念网络，递归神经网络和卷积神经网络），以便更好地映射到PFC内部的神经电路，或者提出更准确地捕获神经过程的新型神经计算模型。我们还可以使用这些见解来指导自动推理系统的推理过程，这将更多地反映人类的推理，并使自动系统更像人类，从而更容易访问。