Dr Andrew Lin
Room: B2 228 Alfred Denny building
Olfactory sensory coding and memory
Undergraduate and postgraduate taught modules
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.
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
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:
- Pavlou HJ, Lin AC, Neville MC, Nojima T, Diao F, Chen BE, White BH & Goodwin SF (2016) Neural circuitry coordinating male copulation. eLife, 5. View this article in WRRO
- Lin AC, Bygrave AM, de Calignon A, Lee T & Miesenböck G (2014) Sparse, decorrelated odor coding in the mushroom body enhances learned odor discrimination. Nature Neuroscience, 17(4), 559-568. View this article in WRRO
- Parnas M, Lin A, Huetteroth W & Miesenböck G (2013) Odor Discrimination in Drosophila: From Neural Population Codes to Behavior. Neuron, 79(5), 932-944.
- Perisse E, Yin Y, Lin A, Lin S, Huetteroth W & Waddell S (2013) Different Kenyon Cell Populations Drive Learned Approach and Avoidance in Drosophila. Neuron, 79(5), 945-956.