Identification of Metastable Cortical Dynamics Underlying Cognitive Decisions

Do we have analysis tools for a reliable identification of the dynamical processes underlying decision-making? This is a fundamental question, touching the very basics of our understanding of neural computation and hence one of the most exciting topics in neuroscience. However, current data analysis approaches are unable to reconstruct in detail neural dynamics generating cognitive decisions (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 "attracting states”; which map e.g., memory patterns or stimuli representations. This hypothesis underlies almost all models of decision-making: the existence of neural states towards which activity of neural constellations converges, despite the intrinsic neuronal noise, while processing similar cognitive entities. Unfortunately, such metastable states have only been found empirically in sensory systems and in deep areas such as hippocampus; while in cortical regions associated to decision-making there are only few preliminary attempts (e.g., Lapish & Balaguer-Ballester and colleagues, 2015 Journal of Neuroscience).
Thus, in general, there is no robust empirical basis for selecting among competing theories of high cognitive functions. The 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 analysis methods for a reliable identification of neural network metastable dynamics empirically.
The aim of this project is to provide analysis and modelling tools which enables us to derive a conclusive demonstration of metastable dynamics in high cognitive areas such as frontal cortex. This would be of paramount importance for deciding among competing decision- making models and a highly significant advance; for instance the discovery of such attracting states in hippocampus (by the British scientist John O’Kefee) deserved to win the last Nobel Prize. This is a purely scientific goal with no immediate clinical application. Nevertheless, altered brain dynamics is well-known to underlie, for instance, hyperactivity disorders or schizophrenia. Therefore, identifying network dynamics is a prerequisite for the future design of pharmacological cocktails which have a modulatory effect on altered activity dynamics.
The objectives of the project are to:
(1) undertake state-of-the-art recordings in behaving rats in different cortical regions simultaneously at professor Sanchez-Vives’ lab, one of the leading centres in animal electrophysiology; where the renowned "up-down" states driving spontaneous brain activity were first identified (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. lead by Drs Xun He and Angela Goslin.
(2) Develop innovative approaches designed for a reliable identification of metastable dynamics of neural ensembles underlying cognitive decisions. The methods will consist of a fusion of theoretically robust approaches to pattern discovery and recent trajectory coherence methods for identifying attractor dynamics (e.g., Balaguer-Ballester and colleagues, 2015 J Neurosci; 2014 Plos 1, 2011 Plos Comput Biology, PNAS). The novel approach will reconstruct neural ensemble dynamics during tasks specifically designed to this goal both in animals and in human subjects.
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.
Candidates to hold a degree in either, Physics, Mathematics, Engineering or similar; or in Medicine, Biology, Psychology etc. and ideally have mathematical background and keen interest in data analysis and modelling.
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.