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Maintaining effective sensory coding in the face of inter-neuronal variation

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  • Full or part time
    Dr A Lin
  • Application Deadline
    Applications accepted all year round
  • Self-Funded PhD Students Only
    Self-Funded PhD Students Only

Project Description

How can neurons have consistent properties to allow effective sensory coding, in the face of developmental noise and inherent inter-neuronal variability? This fundamental problem occurs across species, and we address it in Drosophila, where ~2000 neurons called Kenyon cells encode olfactory associative memories. To accurately distinguish learned associations for different odours, Kenyon cell population responses to odours must be decorrelated, i.e. different odours activate non-overlapping subsets of Kenyon cells.

Inter-odour decorrelation requires Kenyon cells to be roughly equally excitable: if some Kenyon cells are more excitable than others, these same cells tend to dominate all odour responses, which increases overlap between odour representations. Yet recent work shows that Kenyon cells receive extremely variable amounts of excitatory input. Our computational models suggest that this variability impairs odour decorrelation unless Kenyon cells compensate for variability along one parameter (e.g., amount of excitatory input) with counteracting variability along another parameter (e.g., spiking threshold). In this project, the student will test whether and how such compensatory variability occurs, and will computationally model how it would affect circuit function.

In carrying out this project, the student will a range of cutting-edge techniques, including multiphoton imaging, patch-clamp electrophysiology, fly genetics, and computational modelling. The results will be broadly important for understanding for how neurons develop and maintain the correct electrical and synaptic properties to effectively encode sensory information.

References

References:
About the model system:
Lin, A.C., Bygrave, A.M., de Calignon, A., Lee, T., Miesenböck, G. (2014). Sparse, decorrelated odor coding in the mushroom body enhances learned odor discrimination. Nature Neuroscience, 17, 559-68.
About “compensatory variability”:
Marder, E., and Goaillard, J.-M. (2006). Variability, compensation and homeostasis in neuron and network function. Nat Rev Neurosci 7, 563–574.

web: http://www.sheffield.ac.uk/bms/research/lin
http://www.aclinlab.org
email: [email protected]

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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