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Scientific Staff:
     PIs                           Postdocs                               Affiliates
   Bruno Olshausen
   Fritz Sommer
    Kilian Koepsell
    Matthias Bethge
    Thomas Lauritzen
   Pentti Kanerva
Bruno Olshausen | Principal Investigator

Bruno Olshausen - Photo Bruno Olshausen's research attempts to unravel how the brain constructs meaningful representations of sensory information. Much of his work has focused on developing probabilistic models of natural images, and relating these models to the sorts of representations found in the cerebral cortex. Together with David Field he showed that the receptive field properties of neurons in primary visual cortex may be understood in terms of a sparse coding strategy for natural scenes - that is, where incoming data is described using a small number of active neurons out of a large population.

Olshausen received B.S. and M.S. degrees in electrical engineering from Stanford University, and a Ph.D. in computation and neural systems from the California Institute of Technology. In addition to his appointment at RNI, he is Associate Professor of Neurobiology, Physiology, and Behavior, and a member of the Center for Neuroscience at UC Davis.

“Sparse coding appears to be central to both sensory coding and associative memory. One of my goals at the Redwood Neuroscience Institute is to better understand the link between the two - i.e., how are sparse sensory representations utilized at later stages of processing for pattern matching and forming associations? I am most interested in developing hierarchical models that can provide insight into the representations employed in higher cortical areas (beyond V1), as well as the role of thalamo-cortical and cortico-cortical feedback loops in perception.”
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Fritz Sommer | Principal Investigator

Fritz Sommer - Photo Friedrich Sommer is interested in understanding information processing in the brain, especially distributed associative memory and fault-tolerant computation. For example, he integrates methods from diverse areas (Neural Networks Theory, Information Theory, Computational Anatomy, Functional Neuroimaging) to address questions about the formation of cortical memory. He is also interested in methods of analyzing data from neuroimaging (fMRI and PET) by applying principles of neurocomputing and unsupervised learning. He recently edited a book on this topic.

Before joining the RNI he led the Research Group of Biological Neural Networks in the Department of Neural Information Processing at the University of Ulm. Earlier appointments include a position as a visiting scientist at MIT and a research scientist at the Institutes of Medical Psychology and Neuroradiology at the University of Tuebingen. He completed his doctoral work with Guenther Palm at the University of Duesseldorf after receiving a diploma in physics from the University of Tuebingen.

“Single experiments on the working brain, by necessity, are limited to describing only a part of brain structure and function. Theoretical modeling provides a means to link experimental results to form an integrated view of brain function. Associative memory is one class of neural network models central to many theories of cerebral processing. One of my goals at the RNI is to study the role of sparse associative memory in multimodal information processing in the brain.”
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Kilian Koepsell | Postdoctoral Fellow

Kilian Koepsell - Photo Kilian Koepsell is interested in understanding the interaction between perception and memory. His previous research focussed on the analysis of symmetries and invariances of supergravity using group theoretical methods. One of his main contributions is the formulation of a hidden symmetry structure in supergravity which was only known to be present after dimensional reduction. This symmetry puts constraints on possible formulations of supergravity and therefore is important in the search for a consistent quantum theory of gravitation.

Kilian received a diploma in physics and in mathematics and a Ph.D. in physics from Hamburg University in Germany. Before joining the RNI he has worked as a research assistant at the Albert Einstein Institute in Potsdam and at King's College London.

“I believe that mechanisms underlying information encoding, processing, and storage in the brain are intertwined and should be addressed simultaneously. Symmetries that are present both in the physical world and in its cortical representations impose constraints that might lead to a better understanding of these processes. I would like to apply methods from group theory, probability theory, and dynamical systems to study these mechanisms. ”
Matthias Bethge | Postdoctoral Fellow

Matthias Bethge - Photo Matthias Bethge's research is ultimately motivated by the quest for understanding "intelligence" and the neural basis thereof in terms of information processing principles and ecology. During his PhD he investigated how neuronal signal transmission and representations are affected by noise and by the dynamics of neurons and synapses from an optimization point of view. Currently he seeks to explore which functional demands of (human) visual processing are most relevant and how these constraints shape neuronal representations in striate cortex.

Matthias Bethge graduated at the Georg-August University in Göttingen and received his PhD from the University of Bremen. In Göttingen, he was a member of the Nonlinear Dynamics group at the Max-Planck-Institute of Fluid Dynamics and in Bremen he worked together with Klaus Pawelzik at the Institute of Neurophysics.

“The possibility of reliable inference about the surrounding physical geometry from ambiguous retinal input in highly variable environments is impressively demonstrated by the succesful behavior of animals and humans. Although or rather even because the history of computer vision research tells us a long story about the difficulties to attain this performance, I believe that struggling with this kind of challenge has a great potential to reveal a lot about the way brains function.”
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Thomas Z. Lauritzen | Postdoctoral Fellow

Thomas Lauritzen - Photo Thomas Lauritzen has worked in two main fields: Balanced chaotic activity and attractor properties in networks of spiking neurons; and the role of inhibition on cortical responses. He was the first to propose that different types of inhibitory cells contribute to different aspects of the response properties of cells in primary visual cortex.

Thomas got his M.Sc. in physics from the Niels Bohr Institute in Copenhagen, Denmark, working with John Hertz at NORDITA, and Ph.D. in biophysics from the University of California, San Francisco, working with Ken Miller. During is Masters studies he spent an academic year at the University of California, Berkeley.

“Millions of years of cortical evolution has given us a somewhat optimal way of doing neural computations and the cortical structure indicates that these computational principles are similar throughout cortex. But similar arguments goes for the individual cell types: all must play a vital role, else they would have been eliminated by evolution. Determining this role of each given cell type and how they combine with the whole network to convey information is a major challenge.”
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Pentti Kanerva | Research Affiliate

Pentti Kanerva - Photo Pentti Kanerva is best known for his work on sparse distributed memory, which relates properties of human memory to mathematical properties of high-dimensional spaces and compares artificial neural-net associative memory to conventional computer RAM and to the cortex of the cerebellum. His more recent work is on stochastic pattern computing for modeling information processing by the brain. He came to RNI from the Swedish Institute of Computer Science where he was a principal investigator in the Japanese-sponsored Real World Computing Program, and before that (in 1985-92) at RIACS at the NASA Ames Research Center. He has a masters degree in forestry from University of Helsinki, a Ph.D. in philosophy from Stanford, and 20 years of computer programming and engineering experience in between. He has written one book, two book chapters, and 25 papers on distributed representation and memory, and has co-edited a book.

“Understanding the brain's computing is a formidable challenge. It requires knowledge, insight, and scrutiny of many fields, including neurosciences, behavioral sciences, computer science and engineering, mathematical sciences, and ultimately also the humanities. That in itself is a challenge when different disciplines have their own special languages. One of my goals at the Redwood Neuroscience Institute is lowering this language barrier — we must make our work understandable — another is to use the Internet in creative ways to foster interaction across disciplines.”
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