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Modelling optimal synaptic plasticity rules underlying associative memory.

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  • Full or part time
    Dr A Lin
    Dr Eleni Vasilaki
  • Application Deadline
    Applications accepted all year round

Project Description

"How can intelligent systems learn? What learning rules and information coding strategies are best for allowing systems to learn that certain stimuli or actions are good or bad, even with imperfect inputs or limited, unreliable computational resources? Solutions to this engineering problem may find inspiration in biology: the humble fruit fly has solved this problem, as it can learn to associate specific odours with reward or punishment with a brain of only 100,000 neurons.

This learning occurs by synaptic plasticity: changing connection strength between odour-encoding neurons and output neurons that lead to approach or avoidance behaviour. During reward learning, synapses to avoidance outputs are weakened; during punishment learning, synapses to approach outputs are weakened. Why does the fly use opposing output channels (approach and avoidance)? Why does learning weaken, rather than strengthen, synapses? We ask whether these synaptic learning rules are in some sense ‘optimal’: are they better than alternative rules? By what measures? Under what conditions?

We will address these questions experimentally, by measuring synaptic plasticity rules for different output neurons, and computationally, by modelling how different rules perform under different conditions. The answers will inform the design of efficient learning algorithms for artificial intelligence.

We seek a motivated and creative student with a strong interest in how the brain works. We welcome applications from candidates from a range of backgrounds (from biology to computer science or physics), especially those with strong quantitative backgrounds. In carrying out this interdisciplinary project, the student will learn a range of cutting-edge techniques, including multiphoton imaging and computational modelling. The project will be co-supervised by Dr Andrew Lin and Prof Eleni Vasilaki."

Science Graduate School
As a PhD student in one of the science departments at the University of Sheffield, you’ll be part of the Science Graduate School. You’ll get access to training opportunities designed to support your career development by helping you gain professional skills that are essential in all areas of science. You’ll be able to learn how to recognise good research and research behaviour, improve your communication abilities and experience the breadth of technologies that are used in academia, industry and many related careers. Visit www.sheffield.ac.uk/sgs to learn more.

Funding Notes

The studentship is funded for 3.5 years and covers: (i) a tax-free stipend at the standard Research Council rate (£14,777/year for 2018-2019), (ii) research costs, and (iii) tuition fees at the UK/EU rate. The studentship is available to UK and EU students who meet the UK residency requirements, see https://epsrc.ukri.org/skills/students/help/eligibility/. Non-UK EU students are eligible for fees-only funding. Students who do not meet residency requirements may be eligible for full (fees+stipend) funding if their background is very strong.


Aso, Y., and Rubin, G.M. (2016). Dopaminergic neurons write and update memories with cell-type-specific rules. Elife 5, e16135.
Hige, T., Aso, Y., Modi, M.N., Rubin, G.M., and Turner, G.C. (2015). Heterosynaptic Plasticity Underlies Aversive Olfactory Learning in Drosophila. Neuron 88, 985–998.

Andrew Lin:
Eleni Vasilaki:

How good is research at University of Sheffield in Biological Sciences?

FTE Category A staff submitted: 44.90

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

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