Dr Andrew Lin

Andrew Lin

Vice-Chancellor's Fellow
Department of Biomedical Science
University of Sheffield
Western Bank
Sheffield S10 2TN
United Kingdom

Room: B2 228 Alfred Denny building
Telephone: +44 (0) 114 222 3643
Email: andrew.lin@sheffield.ac.uk

Bateson Member Staff Logo

Centre for Sensory Neuroscience

Brief career history

  • 2015 - present: Vice-Chancellor’s Fellow, University of Sheffield
  • 2009 - 2015: Postdoctoral fellow, University of Oxford. Advisor: Gero Miesenböck
  • 2004 - 2009: PhD, University of Cambridge. Advisor: Christine Holt
  • 2000 - 2004: AB Biology, Harvard University

Research interests

We study how the brain represents sensory information to allow it to store unique memories, using the olfactory system of the fruit fly Drosophila melanogaster as a model system.

Full Publications


Olfactory sensory coding and memory

How does the brain recognise sensory stimuli? How does it form distinct memories for different stimuli, even very similar ones? And how does it wire itself up to process information in the best way to achieve these remarkable feats? Our research addresses these fundamental questions using the olfactory system of the fruit fly Drosophila melanogaster. Flies have a much simpler nervous system than humans but are still capable of complex behaviours such as associative memory. This simplicity, combined with the power of fly genetics, makes Drosophila an excellent model system for tackling basic questions about neural circuit function.

Flies can form distinct associative memories for different odours, even very similar ones, and this stimulus-specificity depends on ‘sparse coding’, in which Kenyon cells, the neurons that encode olfactory associative memories, respond sparsely to odours, i.e. only a few neurons in the population respond to each odour. This sparse coding in turn depends on a delicate balance of excitation and inhibition onto Kenyon cells. We are studying how this balance is created and maintained. By improving our understanding of how the brain balances excitation and inhibition, this work may shed light on neurological disorders, like epilepsy, where this balance goes wrong.

Some methods we use:

  • In vivo two-photon imaging
  • Patch-clamp electrophysiology
  • Individual-fly behavioural experiments
  • Genetic manipulation of identified neurons
  • Transcriptional profiling
  • Computational modelling

Undergraduate and postgraduate taught modules

Level 3:

  • BMS349 Extended Library Project
  • BMS355 Sensory Neuroscience

Phd Project

Title: Modelling optimal synaptic plasticity rules underlying associative memory

Supervisor 1: Dr Andrew Lin

Supervisor 2: Professor Eleni Vasilaki

Funding status: Directly funded project European/UK students only

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 http://www.bbsrc.ac.uk/documents/studentship-eligibility-pdf/.

Students from EU countries who do not meet residency requirements are eligible for a fees-only award and may be eligible for full (fees+stipend) funding if their background is very strong.

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.

Keywords: Bioinformatics, Biophysics, Genetics, Neuroscience/Neurology, Biophysics, Applied Mathematics, Bioinformatics, Information Science, Statistics, Data Analysis


  • 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.

Contact information:

For informal enquiries about the project or application process, please feel free to contact me or

To find out more about this project and how to apply see our PhD opportunities page:

PhD Opportunities

Selected publications

Journal articles