Kozlov projects

SupervisorTitleTypeThemeDescription
Andriy Kozlov, Piotr Sirko Testing effects of serotonin receptor agonists on brain circuits using patch clamp Lab based Neurotechnology & robotics The project will focus on using acute rat brain slices to record effects of 5-HT receptor agonists and antagonists on specific ionic currents in cortical and subcortical neurons. The student will work in collaboration with other members of the Kozlov lab working in this area. This is an excellent opportunity to learn the method of patch-clamp in brain slices and get training in neuropharmacology.
Andriy Kozlov, Piotr Sirko Characterising mechano-electrical transduction currents in hair cells of the inner ear Lab based Biomechanics & mechanobiology, Microscopy, Molecular & cellular bioengineering The project will focus on recording and analysing mechano-electrical transduction currents in cochlear hair cells. This is an excellent opportunity to learn the method of patch clamp, optical microscopy and get training in other methods used in the field of the biophysics of hearing. The student will work with other members of the Kozlov lab (www.kozlovlab.com). An ideal candidate should have a background in physics or engineering and strong analytical skills. Familiarity with optical methods would be particularly welcome.
Andriy Kozlov, Piotr Sirko Representational similarity analysis of natural and artificial neural networks for object recognition Desk based Computational & theoretical modelling This project will look into similarities and differences in feature representations in biological and artificial neural networks. Specifically, we will train ANNs on birdsong and mouse vocalizations and extract the learned features. We will use recordings from auditory cortex (obtained in the Kozlov lab) in response to those stimuli to extract receptive field features of biological neurons. We will then use representation dissimilarity analysis to quantify how the ensemble of features are represented in both the ANNs and biological networks and whether one can make the artificial representations better mimic the biological ones. Such approaches have been used in the field of fMRI to compare representations of visual stimuli, but not in auditory neuroscience. As such, this project will combine elements of machine learning and data analysis in the context of systems neuroscience.