| Title | State-dependent and cell-type-specific auditory cortical processing on multiple timescales |
|---|---|
| Supervisor | Shuzo Sakata |
| Research Area | Neuroscience, Neurotechnology, Ageing, Machine Learning |
| Description | The brain is never at rest: the activity state of the brain constantly changes over multiple timescales. Over the last decades, the role of ongoing brain activity in various brain functions has been intensively explored with a variety of experimental and theoretical/computational approaches. However, we still lack an integrative understanding of how global brain states change over multiple timescales, from milliseconds to months or years, and how such state changes affect brain functions, in particular sensory processing. Our research group concerns this fundamental issue in systems neuroscience. In this PhD project, we will investigate age-related and cell-type-specific changes in neural ensembles in the mouse auditory system. By applying advanced data analytical approaches including deep learning, we will analyse a large amount of neurophysiological data, which has been collected from the mouse central auditory system across age by combining in vivo electrophysiological and optogenetic approaches with pupillometry recording. We will specifically ask (1) how spontaneous population activity changes over multiple timescales in a cell-type-specific fashion, and (2) how stimulus encoding strategies of neural populations change depending on brain states, cell-types, age, and peripheral inputs. This project will provide further insight into state-dependent and cell-type-specific information processing in the brain over multiple timescales. |
| Techniques Used | Machine learning Signal and image processing Computational modelling |
| References | Tsunematsu T, Patel AA, Onken A, and Sakata S. (2020). State-dependent brainstem ensemble dynamics and their interactions with hippocampus across sleep states. eLife 9:e52244. Hėricė C and Sakata S. (2019). Pathway-dependent regulation of sleep dynamics in a network model of the sleep-wake cycle. Frontiers in Neuroscience 13:1380. Lyngholm D, and Sakata S. (2019). Cre-dependent optogenetic transgenic mice without early age-related hearing loss. Frontiers in Aging Neuroscience 11:29. Kayser C, Wilson C, Saffai H, Sakata S*, Panzeri S*. (2015). Rhythmic auditory cortex activity at multiple time scales shapes stimulus-response gain and background firing. Journal of Neuroscience 35 (20), 7750-7762. (* equal contribution) |
| Conditions | In this project, data analysis with computer programs (e.g., Python, MATLAB) is an essential component. Applicants should possess or be about to obtain a 1st class or 2:1 Honours degree or equivalent in Computational Neuroscience, Data Science, Computer Science, Physics, Statistics or related fields in addition to receipt of satisfactory references and an IELTS score of 6.5 where appropriate. |
| Bench Fee | Running costs of £10000 p.a. will be associated with this project in addition to University tuition fees. |
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