Research Summary

Scientific Motivation

The brain is by far the most intricate and complex organ known. membraneWith 1010 neurons and 1014 connections it effortlessly performs computations that far exceed the capability of any computer. Neuroinformatics and computational neuroscience combine neuroscience and informatics research to develop and apply the advanced tools and approaches that are essential to understand the structure and function of the brain. Understanding the brain’s computational principles and harnessing the ability to simulate it will revolutionise science and technology.

In order to do this, we need to bridge many different levels of investigation, from molecules to cells, from systems to organisms. These are addressed in diverse disciplines ranging from molecular biology to psychology. Despite the rapid accumulation of data on the brain, our insight regarding their meaning remains limited. Similarly, over the last few decades we have seen tremendous advances in computer science, yet our most advanced computer system is surpassed in many real-world tasks by even so-called primitive creatures, such as a fly, ant, or honeybee. Both from a practical and a conceptual perspective there are many points of contact between the neuroscience-related life-sciences (e.g., neuroscience, psychology, linguistics) and the information sciences and related disciplines (e.g., computer science, mathematics, statistics, physics and electrical engineering).

Neuroinformatics and computational neuroscience lie at the intersection of the neurosciences and the information sciences. To make significant progress in our understanding of the brain, a focussed, multidisciplinary approach is essential. Our training programme is unique in providing students from the physical, mathematical and computing sciences with the cross-disciplinary background that they need.

Research Areas

dist1 The research topics fall into three main categories:

1) Research to further our knowledge of the nervous system using experiments and computational models;

2) research that applies findings from neuroscience to build better software and hardware (robots and microcircuits);

3) research that uses advanced methods to improve data handling and analysis, including clinical diagnosis.

The three categories can be divided up into the five specific, highly interconnected research areas listed below. However, remember that there is always room for new collaborations and research topics.


Using computational tools to gain insight into the genetics and proteomics of single cells including neurons.

Biomedical Imaging Algorithms and Tools

Using advanced data analysis tools such as machine learning and Bayesian approaches to address issues in the life sciences, such as improving imaging-based diagnosis.

Cognitive Science

Studying human cognitive processes and analysing them in computational terms.

Computational Neuroscience

Using analytical and computational models, potentially supplemented with experiments, to gain quantitative understanding of the computations performed by the nervous system.

Neuromorphic Hardware

Using insights from neuroscience to help build better hardware such as robots that perform robustly under natural conditions. and neuromorphic VLSI circuits that are more efficient and robust against noise than digital circuits.

Software Systems and Applications

Using discoveries from neuroscience to develop intelligent computer interfaces and software that can handle real-life data.