Postdoctoral Project: Architectural design principles for intelligence: Modularity vs. integation (analytical)

Part of research project: Architectural design principles for intelligence - Modularity vs Integration

Principal Investigators

Description of the research project

This project will study how architectural design principles affect performance of an intelligent system. We will focus on the tradeoff between modularity and integration and compare this between a biological system (the human brain) and a synthetic system (a computational model). A key question is how the modularity of a cognitive system can be optimized for the tasks they have to solve. The project comprises two postdoctoral research positions, which focus on graph-theoretical analyses of functional connectivity of the brain, and the development of control-theoretic methods for optimizing visual network models, respectively. The former subproject will primarily supervised by J.-D. Haynes, the latter by J. Raisch and H. Sprekeler.

Description of the postdoctoral project

This subproject (primarily supervised by Haynes) pertains to the empirical study of task-dependence of the modularity-integration tradeoff in the human brain. In a first part, the project will employ a battery of visual tasks and visual stimuli that allow to vary the degree of context-sensitivity required for optimal perceptual decision making. We will then measure brain activity with fMRI and perform graph-theoretical analyses to assess the task-dependence of modularity indices. In a second part, the project will employ a task-switching design in order to identify how the brain switches between different dynamic architectures when solving different tasks. This will also be used to identify the meta-control systems in prefrontal cortex. All aspects of this project, graph theory, task switching and control, will be performed in close interaction with another postdoc who will provide an analysis of the architectural principles from the perspective of computational neuroscience and control theory (primarily supervised by Sprekeler and Raisch).


Project start date: October 1, 2019 (an earlier starting date may be possible)


Applicants must hold a PhD in Cognitive Neuroscience, Medical Neuroscience or Computational Neuroscience or a related field. The applicant should have proven skills/background in following topics:
• Excellent training in cognitive neuroscience (desirable specialisation in vision and/or executive control)
• Top-level experience in behavioral and neuroscientific methods in cognitive neuroscience (e.g. experimental psychology, functional MRI, EEG)
• Mathematical and computational skills (statistics, advanced neuroimaging data analysis, basics in computational neuroscience)
• Advanced programming skills (e.g. MATLAB, Python)
We aim to fill as early as possible, but no later than October 2019.


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