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Controlling the growth of cultured neuronal networks for the creation of animats


Project Description

A cultured neuronal network is a cell culture of biological neurons that is used as a model to study the nervous system, especially the brain. These neuronal networks are often grown on specially designed substrates that enable them to be directly connected to input/outputs devices, such as a multi-electrode-array (MEA), this allowing bidirectional communication between the network and a computer system. These networks are invaluable tools for studying the underlying principles behind neuronal learning, memory, plasticity, connectivity, and information processing.

Cultured neurons are often connected via computer to a real or simulated robotic component, creating a hybrot or animat, respectively. Researchers can then thoroughly study learning and plasticity in a realistic context, where the neuronal networks are able to interact with their environment and receive at least some artificial sensory feedback.

One of the main challenges associated with these networks is how to get the neurons to grow in a controlled pattern, as this dictates the network geometry. This primary aim of this project is to explore different mechanisms for directing the growth of cultured neurons by modifying the properties of the substrate surface. It is known that surface features (such as micro-pillars) can be used to encourage neurons to grow in particular geometries, and this approach will be used to develop new neuronal networks that can be grown to perform functional tasks.

AI Innovation: The development of cultured neuronal networks will create invaluable tools for studying the underlying principles behind biological functions such as neuronal learning, memory, plasticity, connectivity, and information processing. The knowledge of these principles will be directly applicable to developing new AI techniques.

Implications: The potential implications of this work are widespread, with direct improvement of artificial neural networks and an improved understanding of the way in which biological neurons grow and develop. This has implications in both regenerative medicine and machine learning.

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 at: http://www.bath.ac.uk/research-centres/ukri-centre-for-doctoral-training-in-accountable-responsible-and-transparent-ai/.

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. A master’s level qualification would also be advantageous.

Informal enquiries about the project should be directed to Dr Ben Metcalfe on email address .

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: https://samis.bath.ac.uk/urd/sits.urd/run/siw_ipp_lgn.login?process=siw_ipp_app&code1=RDUCM-FP01&code2=0013

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.

How good is research at University of Bath in Electrical and Electronic Engineering, Metallurgy and Materials?

FTE Category A staff submitted: 20.50

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

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