- Opens: Friday 28 February 2020
- Deadline: Friday 31 July 2020
- Number of places: 1
- Duration: 36 Months
OverviewNeuroscience, Neurotechnology, Ageing, Machine Learning
In this project, data analysis with computer programs (e.g., Python, MATLAB) is an essential component. A successful candidate should have or expect to have an Honours Degree at 2.1 or above (or equivalent) in Computational Neuroscience, Data Science, Computer Science, Physics, Statistics or related fields.
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.
Signal and image processing
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)
Applicant will need to self-fund, find sponsorship for tuition fees for duration of studies
Primary Supervisor: Dr Shuzo Sakata