Bernstein Award
| Call for proposals: | 2006, funding will be provided for one research project per year within the framework of relevant medium-term fiscal planning |
| Funding period: | five years for each individual Bernstein Award project |
| Funding volume: | up to €1.25 million for each individual Bernstein Award project |
| Number of projects: | 3 |
1. Funding purpose
The BMBF has established four "Bernstein Centers for Computational Neuroscience" within the framework of its "National Network for Computational Neuroscience" funding activity. These high-performing centers are the major structural elements of the National Network. This means that a new structure has been created which is necessary for developing a new quality in Computational Neuroscience, for networking this research area and for promoting its international visibility. It is particularly important to attract excellent young researchers in order to lastingly strengthen and establish Computational Neuroscience as a field of research in Germany. The Bernstein Centers contribute substantially to achieving this goal with their concepts for supporting junior researchers at the level of study programmes and postgraduate studies. But there is also a great need to attract and support young researchers at the level of research group leaders, where they will be able to develop their own research profile and greater scientific independence by establishing and heading their own junior research groups.
The "Bernstein Award" funding activity aims to support research projects in the field of Computational Neuroscience, thus enabling excellent young researchers working in this field to implement innovative project ideas in Computational Neuroscience in the German research environment. This is intended inter alia to promote the academic qualification of these outstanding young researchers. The projects supported under the "Bernstein Award" initiative will become an integral part of the National Network for Computational Neuroscience and give new impetus to scientific activities.
Funding is provided for research projects which have been designed by young, German or foreign Postdocs and which will be conducted at a German research institution. By realizing research projects which they have designed and will supervise themselves and by establishing their own junior research group, the young project leaders are to be given an opportunity to conduct independent research. The funded research projects can be conducted within or outside the Bernstein Centers.
2. Status of funding measure
The Bernstein award was granted for the first time in October 2006. It was awarded to Dr. Matthias Bethge who is now establishing an own research group at the Max-Planck-Institute for Biological Kybernetics in Tübingen. He will investigate the neuronal foundations of signal processing in the brain. The Bernstein award will be awarded annually.
3. Funded projects
a) Short description of current projects
Bernstein Award for Computational Neuroscience 2010
Schnelle parallele Konfiguration der visuellen Informationsverarbeitung
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Universität Bremen - Fachbereich 01 |
Leiter: |
Dr. Udo Ernst |
Bernstein Award for Computational Neuroscience 2009
Modulation of value representations during human decision-making: a neurocomputational approach
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Universitätsklinikum Hamburg-Eppendorf |
Principal investigator: |
Dr. Jan Gläscher |
This project at the University Medical Center Hamburg-Eppendorf will analyze factors that influence us in decision making. Common to all these decision is a basic valuation process, by which the brain assigns a predictive value to each of the decision options. These values are then compared with each other and the subsequent decision is usually in favor of the option with the highest expected value. But which internal and external factor influence the valuation process? In order to solve this question, the project uses functional magnetic resonance imaging (fMRI) in humans in combination with computational models of learning and decision-making. For example, in one set of experiments experimental subjects must select one out of multiple symbols. Their decision is, depending on the selected symbol, associated with monetary gain or loss. With time, the subjects learn which decisions lead to gain and which to loss. During the whole experiment, brain processes are measured using fMRI in order to identify relevant brain areas that influence the valuation process and in order to characterize how these brain areas modify the computations necessary for value-based decision making. In a further set of experiments the project investigates the influence of several different simultaneous factors on decision making. For example, the symbols are replaced by pictures of more or less attractive persons. With such an approach the following questions may be addressed: How are attractiveness and expected monetary gain integrated in the brain? How do men react to pictures of women in leadership positions – do prejudices or unconscious evaluation processes play a role in decision making? How long does re-learning take after the relation between symbol and monetary value changes? Furthermore, the project investigates social (decision in group situations) as well as genetic and pharmacological influences that modulate the availability of certain neurotransmitters in the human brain. A better understanding of human decision-making will contribute to the improvement of existing and development of new therapies for psychiatric diseases with impaired decision behavior, such as depression and obsessive-compulsive disorders.
Bernstein Award for Computational Neuroscience, 2008
From single neurons to neural networks: uncovering computational principles based on intrinsic neuronal properties
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Humboldt-Universität zu Berlin |
Principal investigator: |
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Biological behavior emerges from the coordinated activity of neurons. For the functioning of the nervous system both the architecture of the local network as well as the unique intrinsic properties of the cells comprising the network are important. The intrinsic properties are largely shaped by the dynamics of ion channels and determine how a cell translates input signals into responses consisting of electrical pulses. It can therefore be assumed that mechanisms arising from ion-channel dynamics are critical for neural information processing. The project aims to investigate the influence of these cellular dynamics on neural computation. To that end, mathematical simulations of neuronal responses will be combined with electrophysiological recordings in the entorhinal cortex and the hippocampus of rats. Neural computation will be analyzed from the level of ion channels and the single neuron to the level of neural networks. The results of this project will contribute to a better understanding of neural information processing. In the long term, linking specic ion channel dynamics to pathological network behavior promises deeper insight into mechanisms of neural diseases, such as epilepsy.
Bernstein Award for Computational Neuroscience, 2007
Function of intrinsic noise in sensory signal processing
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Ludwig-Maximilians-Universität München |
Principal investigator: |
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Sense organs translate sensory stimuli into neural signals – neurons fire brief electrical pulses. The timing of these spikes can be quite variable, which on a first glance seems to degrade precise neuronal signal transmission. However, neurons need to process sensory information and this „noise“ can therefore in principle play an important role, since the amount of noise determines the level of synchronization between many neurons. In this way, noise could help to filter out only behaviorally relevant stimulus features. Goal of the proposed project is to investigate the role of noise in processing signals in sensory systems. Comparative electrophysiological studies on the active and the passive electrosensory system of two species of weakly electric fish are the key for achieving this goal. In addition, observation of the behavior of the fish both in the lab and in their natural habitats will result in valuable data on the statistics of natural stimuli. The results of this project will contribute to the understanding of neural information processing in general, including the human brain.
Bernstein Award for Computational Neuroscience, 2006
Research on Neural Coding and Inference in Early Vision
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Max-Planck-Institut für biologische Kybernetik |
al investigator: |
Dr. Matthias Bethge |
The agenda of the research project can be summarized by the two basic questions originally posed by Hermann von Helmholtz: “What are the principles that govern how the visual pathways make inferences from the visual image? How do we use image information to compute these perceptual inferences?” A principal difficulty in the understanding of biological vision is the complexity of the inference problems one encounters both at the level of behaviour as well as at the level of neuronal responses. This complexity mostly results from the large number of degrees of freedoms in the sensory input and in the neuronal responses. Using methods of statistical inference and learning theory, as well as signal processing, nonlinear dynamics and optimization theory, the research addresses the problem of perceptual inference from natural images and its neural basis at different levels:
(A) The development of mathematical generative models of natural images and image transformations using unsupervised learning methods. Particular emphasis is placed on quantitative comparisons of the performance of these models.
(B) The performance of psychophysical studies in order to evaluate the relationship between natural image models and perception.
(C) The development of new efficient methods to predict the spike trains of neurons in response to natural stimuli with the goal of inferring the contribution of these neurons to the image processing performed in the early visual system.