In recent years new and powerful computational methods have been developed to quantify the information content of brain signals. While the original focus of this was on patterns of action potentials (spikes), it has emerged that oscillating electric fields in the brain (EEG and Local Field Potentials) can add substantial information not present in these spike trains. These findings have strengthened the idea that EEG/LFP plays an active role in information processing together with spikes. The specific theoretical framework that allows a rigorous quantification of the information content of brain signals is Information Theory (IT). However, whilst IT can quantify the information content in the signal exchanged between brain areas it cannot reveal anything about the direction of information flow. Recent developments, however, have provided new mathematical tools that allow us to not only quantify the information exchanged between brain areas but also determine the causal relationship between them. These new techniques open up the possibility of mapping the dynamic flow of information both within and between brain regions. However, to reveal how the brain processes information it is critical to develop models that that allow us to make testable predictions. In this project the student will, therefore, complement our analysis of brain recordings by proposing and implementing computational models that can reproduce these experimental observations and lead to novel testable predictions about brain function. A natural extension of the work will be to the rodent models of neuropsychiatric disease currently in use in our labs, including Alzheimer’s and schizophrenia, where memory function is severely compromised.
Training/techniques to be provided:
The student will be trained to analyse complex multi-channel electrophysiological data using cutting-edge mathematical and computational approaches. Students will also learn how to design and implement computational models of neural populations in order to match the experimental data. Overall, students will develop advanced skills that are sought-after in the field of mathematical and computational neuroscience (Montemurro). The student will also be trained in recording spikes and LFP/EEG simultaneously from multiple locations in the rat hippocampal formation in vivo (Gigg).
Candidates are expected to hold (or be about to obtain) a minimum upper second-class honours degree (or equivalent) in a mathematical or computational discipline (physics, mathematics, computer science) and experience in computer programming. Some previous exposure to neuroscience will be an advantage, and high motivation to undertake a challenging interdisciplinary topic is essential
For international students we also offer a unique 4 year PhD programme that gives you the opportunity to undertake an accredited Teaching Certificate whilst carrying out an independent research project across a range of biological, medical and health sciences. For more information please visit http://www.internationalphd.manchester.ac.uk
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Gigg J., McEwan F., Smautz R., Neill J. and Harte M. (2019). Synaptic biomarker reduction and impaired cognition in the sub-chronic PCP mouse model for schizophrenia. J. Psychopharm. in press.
Lea-Carnall CA, Montemurro MA, Trujillo-Barreto NJ, Parkes LM, El-Deredy W. (2016) Cortical resonance frequencies emerge from network size and connectivity. PLoS computational biology. 12(2):e1004740.
Lea-Carnall CA, Trujillo-Barreto NJ, Montemurro MA, El-Deredy W, Parkes LM. (2017) Evidence for frequency-dependent
cortical plasticity in the human brain. Proceedings of the National Academy of Sciences. 114(33):8871-6.
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