Bridging Computer Science with Neuroscience towards a new understanding of reasoning
Lead Research Organisation:
University of Cambridge
Department Name: Computer Science and Technology
Abstract
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.
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.
Organisations
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| EP/N509620/1 | 01/10/2016 | 30/09/2022 | |||
| 1778161 | Studentship | EP/N509620/1 | 01/10/2016 | 31/03/2020 | Duo Wang |
| Description | While I am still conducting various projects of my PhD research. I will describe a work that was already done. In this work, we developed Euler-Net, a neural network architecture that is able to solve syllogism tasks represented as Euler diagrams. A syllogism task is composed of two premises and one conclusion. For example, 'Socrates is a man' and 'All men will die' are two premises, and 'Socrates will die' is a valid conclusion. The ontological relationships between entities in the tasks can be represented with Euler diagrams. Euler-Net takes a sequence of Euler diagrams representing the premises in the syllogism, and infer the conclusion. The Euler-Net closely resemble human neural pathway of reasoning. Euler-Net is composed of a perception module similar to visual cortex in the brain and a reasoning module similar to prefrontal cortex. Perception module encodes input diagrams into a high-level representation, similar to the neural coding process in neuroscience literature. Reasoning module takes these representations as input and perform reasoning. Euler-Net is able to correct infer conclusions with 99.5% accuracy. In addition we developed a novel model called MXGNet that achieved state-of-the-art accuracy on Raven Progressive Matrices test, which is often used to test human fluid intelligence. We took ideas from neuroscience, such as human attention mechanism on extracting object-level representations, and integrated them into the current neural network models. |
| Exploitation Route | Euler-Net is the first approach of applying neural network on diagrammatic reasoning. Euler-Net can be further developed to accommodate other types of diagrams, and be developed as a powerful diagrammatic reasoning engine itself. The inner representations of Euler-Net can also be studied by logicians and diagram designers to investigate many questions, such as which type of diagram is most easy to use for a particular type of tasks. MXGNet is currently the state-of-the-art model for RPM reasoning test. This model can be further explored by other researchers to extend the model for other reasoning tests, or improve the performance even further. |
| Sectors | Digital/Communication/Information Technologies (including Software),Education,Other |
| URL | https://www.cl.cam.ac.uk/~wd263/ |