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Modelling thalamic and cortical neurons

School of Life Sciences

Sunday, January 10, 2021 Competition Funded PhD Project (Students Worldwide)

About the Project

This project is available through the MIBTP programme on a competition basis. The successful applicant will join the MIBTP cohort and will take part in all of the training offered by the programme. For further details please visit the MIBTP website -

We use a combination of electrophysiology, detailed analysis and computational modelling to define the properties of neurons (passive and active) and their synapses and how they are modified in health and disease (for example see ENEURO.0166-19.2019; Front Comput Neurosci 10, 116; J Neurophysiol 113(3):871-82; J of Physiol 591:371-3380). We wish to extend these methods to investigate specific subtypes of neurons and their connections in hippocampal and thalamic circuits.

• Use the dynamic-IV experimental methodology (Badel) to extract parameters from thalamic and hippocampal interneurons, and then to combine this with mathematical modelling to construct simplified networks of these neurons.
• Use multiple patch-clamp recording to investigate synaptic transmission between pairs of neurons and to quantify its history dependence by patterned stimulation.
• Use patch clamp recording from soma, dendrites and axon to build up state-of-the-art computational models of neuronal response properties. These models will be used to guide further experiment and build models of cortical microcircuits.


You will combine electrophysiological recording (single and multiple patch-clamp recording) with computational modelling and analysis. Neurons will be identified and reconstructed using confocal microscopy.

BBSRC Strategic Research Priority: Understanding the Rules of Life: Neuroscience and behaviour

Techniques that will be undertaken during the project:
• Production of acute brain slices
• Electrophysiological recording methods (single and multiple patch clamp recording)
• Immunohistochemistry
• Computational modelling using Python and Julia environments.

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