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Modelling Brain-Like Intelligence in an evolutionary context for AI applications

Project Description

A challenge for AI research is to operate autonomously in natural environments. Although many organisms can respond adaptively to the natural world in milliseconds, the computational complexity in interpreting natural images is an on-going fundamental problem. One suggested solution is to pursue research on Brain-Like Intelligence (Sendhoff, Koerner & Sporns, 2009) as a means of creating biologically-inspired solutions that might provide a generalised approach to the computational demands of multiple sensory inputs and potential motor outputs. The promising perspective to develop Brain-Like AI has been hampered by the mistaken view that intelligence is a hallmark of “highly evolved creatures”; instead, the pluralistic view that all creatures have evolved adaptive sensory and motor capabilities for different environments might better endow autonomous robots with the flexibility necessary to use different computational approaches attuned to the environment.

This project is associated with the UKRI CDT in Accountable, Responsible and Transparent AI (ART-AI), which is looking for its first cohort of at least 10 students to start in September 2019. Students will be fully funded for 4 years (stipend, UK/EU tuition fees and research support budget). Further details can be found here:

The project aims to examine the evolution of sensory cognition by phylogenetic modelling of behavioural data to provide a foundation for biologically-inspired Brain-Like AI. The results will lead to innovations in biologically-inspired AI and by increasing the input and output modalities for AI agents and robotics. In turn the advances in AI that this will bring have implications for scientific literacy, particularly in regards to evolution, with demonstrations that there is not a set of “more evolved” creatures or one superior form of “intelligence” but rather a diversity of artificial intelligences to be discovered and applied.

The approach will be multisensory and task-based, to understand the best biological approaches (e.g., seeing or hearing?) to solve natural computational problems (e.g., how to find food). The supervision for this project will be interdisciplinary and thus provide training and theory from the perspectives of (psychology, primary supervisor Dr Michael Proulx), evolutionary neuroscience (Dr Alexandra de Sousa), and computer science (Prof Eamonn O’Neill).

Desirable qualities in candidates include intellectual curiosity, a strong background in maths and programming experience. Applicants should hold, or expect to receive, a First Class or good Upper Second Class Honours degree in a relevant field. A master’s level qualification would also be advantageous.

Informal enquiries about the project should be directed to Dr Michael Proulx .

Enquiries about the application process should be sent to .

Formal applications should be made via the University of Bath’s online application form for a PhD in Computer Science:

Start date: 23 September 2019.

Funding Notes

ART-AI CDT studentships are available on a competition basis for UK and EU students for up to 4 years. Funding will cover UK/EU tuition fees as well as providing maintenance at the UKRI doctoral stipend rate (£15,009 per annum for 2019/20) and a training support fee of £1,000 per annum.

We also welcome all-year-round applications from self-funded candidates and candidates who can source their own funding.


Finnegan, D. J., O'Neill, E., & Proulx, M. J. (2016, May). Compensating for Distance Compression in Audiovisual Virtual Environments Using Incongruence. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (pp. 200-212). ACM.

de Sousa, A. A., & Proulx, M. J. (2014). What can volumes reveal about human brain evolution? A framework for bridging behavioral, histometric and volumetric perspectives. Frontiers in Neuroanatomy, 8, 51. doi: 10.3389/fnana.2014.00051

Sendhoff, B., Körner, E., Sporns, O., Ritter, H., & Doya, K. (Eds.). (2009). Creating Brain-Like Intelligence: From Basic Principles to Complex Intelligent Systems (Vol. 5436). Springer Science & Business Media.

How good is research at University of Bath in Allied Health Professions, Dentistry, Nursing and Pharmacy?

FTE Category A staff submitted: 54.20

Research output data provided by the Research Excellence Framework (REF)

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