Computational Modelling of Biological Learning, Adaptation and Cognition Utilising Integrated Neural Systems

Specific qualifications/subject areas required of the applicants for this project: 2.1 honours degree or above in STEM-related subjects such as Computer Science, Mathematics, Physics or Computational Neuroscience. This project would suit a student with good programming/modelling skills (e.g. in Matlab) and a background in one of the following: computer science, physics, mathematics, computational neuroscience or a closely related discipline, together with a strong interest in multi-disciplinary modelling of brain signal processing, neural networks or cognition.

Brain information processing, learning and cognition are dependent on neural connections formed during development and modified during life. The structural complexity, scale, extensive and substantially unknown connectivity, and limited accessibility to neurons, complicate the study of the dynamics of brain networks. Despite important advances having been made by biologists and neuroscientists/computational neuroscientists, the exact ways in which neuronal circuits interconnect and their precise information processing and dysfunction in health and disease are still active areas of research.

Two main approaches have been utilised by scientists to understand the fundamental (i.e. non-psychological) processes of learning and cognition. In the first (more biological) approach, the cellular and molecular mechanisms involved in learning and memory have been analysed in simple animals and in circuits of mammalian brains. The second (more computational) approach involves algorithmic modelling of cells and axonal/synaptic interconnections, at various network scales on powerful computational platforms. This latter approach is entirely dependent on the accuracy and fidelity of the neuronal and synaptic models. The ideal research platform would be a hybrid system, that has available a controllable and fully observable bio-computational, 3D structured and electronically interconnected matrix of in-vitro biological cells, with direct computational control of stimulation and read-back of intercellular communication signals. This would allow for the possibility of computational modelling using accurate (real) cells under precise experimental control.

This PhD project is one component of an integrated effort to develop and exploit a controllable and fully observable platform of real biological cells for computational modelling. A cross-disciplinary team of experts in nanotechnology, biology and computational modelling is driving the overall effort and establishing reliable, biologically relevant and well-characterised multi-layer nanofiber-based 3D tissue scaffolds. These scaffold systems will have a range of applications (e.g. studies of neural development and function) but are particularly applicable to computational modelling of brain signal processing for learning and adaptation. The project will build upon our recent developments of novel bio-modified nanofiber lattices that can be electrically stimulated.

The PhD Project
This PhD project will be focused on the development of approaches for computational modelling of inter-cell communication, where biological cells are deposited on a hybrid, nanostructured lattice (other members of the team will focus on the nanotechnology and cell deposition aspects). The PhD student will:

• Explore whether bioactive nanofiber lattices, with co-cultures of distinct physiologically active populations of cells found in the nervous system, can be interconnected (bio-electronically) to produce simple models of stimulus-response signalling events in brain tissue;
• Develop algorithms which address the computationally controlled formation of synaptic interconnect by guided growth, utilising graphene-based interconnect incorporated into the nanofiber-based 3D tissue scaffolds;
• Research computational approaches for studying synaptic modification utilising computational neuroscience learning algorithms;
• Explore the potential of the platform for studies of larger scale neural development.

For informal discussion regarding the project, please contact: [Email Address Removed]

HOW TO APPLY
For further details please see the web site here:
http://www.ntu.ac.uk/research/graduate_school/studentships/132586.html

Please find attached an Application Form, notes for completion and guidance, and further details about the Schools and the available research projects.

*Applications from non-EU students are welcome, but a successful non-EU candidate would be responsible for paying the difference between non-EU and UK/EU fees. (Fees for 2015/16 are £12,300 for non-EU students and £4052 for UK/EU students)

ELIGIBILITY & ENTRY REQUIREMENTS
To be eligible to apply, you must hold, or expect to obtain by 1st October 2016, a Master’s degree, or a 1stClass/2.1 Bachelor’s degree in a relevant subject area (including, where appropriate, training in the relevant research methods and, where relevant, laboratory experience).

Please note that these scholarships are only available for new applicants. Existing PhD students are not eligible to apply


ENGLISH LANGUAGE REQUIREMENTS
Applications can be accepted from UK/EU and International students. The minimum English language proficiency requirement for candidates who have not undertaken a higher degree at a UK HE institution is IELTS 6.5 (with a minimum of 6.0 in all skills).