Identification of Metastable Cortical Dynamics Underlying Cognitive Decisions

Do we have analysis tools for discerning neuronal dynamics underlying cognition? This is a fundamental question, touching the very basics of our understanding of neural computation and hence one of the most exciting topics in computational neuroscience. However, it is also a major challenge for current theoretical approaches (Gerstner et al., 2013, Science).
A classical view on neural computation is that it can be characterized in terms of convergence to metastable i.e. temporarily stable states which map e.g., memory patterns or stimuli representations in the cerebral cortex. This hypothesis underlies almost all models of cognitive functions: the existence of stable states, despite the high neuronal noise, which represent similar stimuli or behaviours. However, such “metastable states” have not been conclusively found empirically. For instance, in cortical regions associated to decision-making there are only few preliminary attempts (e.g., Lapish & Balaguer-Ballester et al., 2015 Journal of Neuroscience).
Thus, in general, there is no robust empirical basis for selecting among competing models of high cognitive functions. Two main reasons are: (1) the technical difficulty of performing simultaneous recordings in different cortical regions in behaving animals and hence to establish causal relationships among them and (2) the lack of theoretical methods for a reliable analysis of neuronal network dynamics.
The aim of this project is to provide new theoretical ideas which enables us to discern metastable dynamics in high cognitive areas. This is important for deciding among competing models and would be a significant advance in theoretical neuroscience; for instance, the discovery of stable states in hippocampus (by the British scientist John O’Kefee) deserved to win the last Nobel Prize. This is a scientific goal with no immediate clinical application. Nevertheless, altered brain dynamics is well-known to underlie, for instance, hyperactivity disorders or schizophrenia. Therefore, understanding neuronal network dynamics is a prerequisite for the future design of pharmacological cocktails which have a modulatory effect on altered brain activity dynamics.
The objectives of the project are to:
(1) analyse state-of-the-art recordings undertaken in behaving rats (in different cortical regions simultaneously) at professor Sanchez-Vives’ lab, one of the leading centres in animal electrophysiology (Sanchez-Vives et al., 1997 Science; 2000 Nature Neurosci). The experimental paradigm will be then extended to human subjects at the Bournemouth University Electroencephalography (EEG) lab.
(2) develop innovative approaches designed for discerning metastable dynamics of neural ensembles underlying cognitive states. The approaches will consider ideas from machine and statistical learning, nonlinear dynamical systems, the Fokker-Planck formalism, recurrent neuronal networks theory etc.

How to apply: Applications are made via our website using the Apply Online button below. If you have an enquiry about this project please contact us via the Email NOW button below, however your application will only be processed once you have submitted an application form as opposed to emailing your CV to us.

Candidates for funded PhD studentship must demonstrate outstanding qualities and be motivated to complete a PhD in 3 years.
All candidates must satisfy the University’s minimum doctoral entry criteria for studentships of an honours degree at Upper Second Class (2.1) and/or an appropriate Master’s degree. An IELTS (Academic) score of 6.5 minimum (or equivalent) is essential for candidates for whom English is not their first language.

In addition to satisfying basic entry criteria, BU will look closely at the qualities, skills and background of each candidate and what they can bring to their chosen research project in order to ensure successful and timely completion.