Biologically plausible computational models of reasoning [SELF-FUNDED STUDENTS ONLY]

This project aims to develop a biologically plausible computational model of how the brain processes reasoning, including deductive and ampliative reasoning. Mumford [1] suggests the existence of such a theory as follows.

In summary, my belief is that pattern theory contains the germs of a universal theory of thought itself, one which stands in opposition to the accepted analysis of thought in terms of logic. ... The extraordinary similarity of the structure of all parts of the human cortex to each other and of human cortex with the cortex of the most primitive mammals suggests that a relatively simple universal principal governs its operation, even in complex and deep thinking.

No one still knows how human brains perform logic and pattern recognition in a unified way. The project uses a probabilistic model of generative reasoning, which is a probabilistic approach to logical and statistical reasoning taking advantage of the expressive and abstraction power of formal logic and the predictive power of Bayesian machine learning. The project incorporates neuroscience and psychological approaches and take mathematical, experimental and computational approaches to a biologically plausible computational model. This project is not a typical machine learning or logic project as we seek a biologically plausible theory, and it is either not a typical neuroscience or psychological project as we seek a computational model.

The deliverables include the dissertation and algorithm source code. The project will expose the successful applicant to the interdisciplinary field across logic, machine learning and neuroscience, where important open questions remain unsolved. Under the research outline, the project will provide the successful applicant with the opportunity to follow their curiosity and to carry out research into something they are interested in.

References: [1] Mumford, D.: Pattern Theory: A Unifying Perspective, pp. 187–224. Birkhäuser Basel, Basel, Switzerland (1994)

Keywords: Artificial intelligence, Reasoning and learning, Planning, Causality, Probability theory, Bayesian statistics, Neural networks, Logic

Contact for information on the project: KidoH@cardiff.ac.uk

Academic criteria: A 2:1 Honours undergraduate degree or a master's degree, in computing or a related subject. Applicants with appropriate professional experience are also considered. Degree-level mathematics (or equivalent) is required for research in some project areas.

Applicants for whom English is not their first language must demonstrate proficiency by obtaining an IELTS score of at least 6.5 overall, with a minimum of 6.0 in each skills component.

How to apply:

This project is accepting applications all year round, for self-funded candidates.

Please contact the supervisors of the project prior to submitting your application to discuss and develop an individual research proposal that builds on the information provided in this advert. Once you have developed the proposal with support from the supervisors, please submit your application following the instructions provided below.

Please submit your application via Computer Science and Informatics - Study - Cardiff University

In order to be considered candidates must submit the following information:

·       Supporting statement

·       CV

·       In the ‘Research Proposal’ section of the application enter the name of the project you are applying to and upload your Individual research proposal, as mentioned above in BOLD

·       In the funding field of your application, please provide details of your funding source.

·       Qualification certificates and Transcripts

·       References x 2

·       Proof of English language (if applicable)

Interview - If the application meets the entrance requirements, you will be invited to an interview. 

If you have any additional questions or need more information, please contact: COMSC-PGR@cardiff.ac.uk