Graphene is one atom-thick layer of carbon atoms arranged in a two-dimensional honeycomb lattice. The theoretical study of some of its properties goes back to the 1940s, however only in 2004 it has been experimentally realized. The experimental realization of graphene has spurred an enormous amount of interest and activity in the physics community due to the graphene's unique properties and its possible applications in electronic devices. One of the most interesting aspects of graphene is that the low energy electronic excitations are described by a massless Dirac fermion model. Electrons in graphene behave as ultra-relativistic electrons described by two-dimensional Quantum Electro Dynamics (QED), albeit with a much lower (1/300 th) speed of light and bigger (≈ 1), and tunable, fine structure constant. After presenting the main properties of graphene I will discuss its unusual transport properties that arise from its gapless Dirac spectrum characterized by a single point, the Dirac point where the valence and conductance band touch. Close to the Dirac point the average carrier density is zero and the physics of graphene is dominated by the density fluctuations. I will present a microscopic theory that is able to characterize quantitatively these fluctuations. I will then present a transport theory for graphene that properly takes into account the strong density fluctuations close to the Dirac point and is able to answer the most puzzling questions that have been posed by transport experiments on graphene since its realization in 2004

Arguably, one of the most intriguing properties of graphene transport is the non-vanishing "minimum conductivity" close to zero carrier density that is now understood to arise from the inhomogeneous situation where the local potential fluctuates around zero, breaking the landscape into puddles of electrons and holes. We propose and discuss a particular hierarchy of approximations to understand graphene transport properties that is in remarkable agreement with recent experiments. To better understand the success of the theory, we scrutinize the underlying assumptions by examining the inclusion of classical percolation, many-body effects, and phase-coherent quantum transport. We conclude that experimental realization of chiral Dirac Fermions provides an exciting new test-bed to study the interplay between quantum mechanics, disorder, interactions and the effects of boundaries.

Two dimensional electrons confined at semiconductor interfaces display remarkable quantum collective behavior when placed in a perpendicular magnetic field. At high magnetic fields electrons form stable, incompressible quantum liquids that give rise to the fractional quantum Hall effect. How do these quantum liquids emerge and what stabilizes them? I will review the integer and fractional quantum Hall effects as well as the composite fermion theory of the fractional quantum Hall regime. I will show how the stability of certain fractional quantum Hall states derives from the formation of quantum Hall rotons in the excitation spectra, analogous to roton excitations found in superfluid helium. I will discuss recent surface acoustic wave experiments that directly observe quantum Hall rotons and numerical simulations that quantitatively reproduce experimental results.

The antineutrino line from 2-body decay of tritium ³H → ³He+ν_e in crystals can be emitted with natural width because of motional averaging by lattice vibrations despite the very long lifetime of ³H (∼12.5y). It can induce resonant transitions ³H ↔ ³He with very high cross section σ ∼ 10^-17 cm². A specific experimental design applying modern technology of ³H and ³He storage in metals is proposed. Using the "time-filtering " effect on the resonance rate by the physical age of the tritium source, the absolute energy width Γ_τ ∼ 10^-24eV of ³H expected from its lifetime by the uncertainty principle can be measured. The achievable energy precision is extremely high, ΔE/E ∼ 10^-29. This can test if the measured width Γ_exp=Γ_τ in the untested energy regime probed by this experiment. A discrepancy Γ_exp > Γ_τ would imply a crisis - the breakdown of the key-stone of quantum mechanics - but interpretable as evidence for the presence of a fundamental or Planck length ∼ 10^-33cm in nature. That could presage the transition quantum mechanics → quantum gravity at ultra-small energies, reminiscent of the transition classical → quantum physics in the regime of atomic dimensions.

Cells within highly differentiated multi-cellular organisms, such as humans, are constantly receiving signals from their extracellular environment that direct their function. This information transfer is bi-directional, that is, cells both respond to and modify the state of their extracellular environment. In order to begin to understand this complex single-response network one must first appreciate how the meshwork that cells are imbedded in, called the extracellular matrix, participates in regulating cell structure and function. The extracellular matrix is comprised of structural components such as collagen and elastic fibers (the insoluble phase) and transient components such as peptide growth factors, which are generally considered to exist within a soluble phase occupied by hydrated polysaccharides within complex macromolecules called proteoglycans. In this presentation, the interactions between the various components of the extracellular matrix and their impact on cell function will be discussed. Specifically, quantitative aspects of the ability of the heparan sulfate proteoglycans to bind growth factors and control cell binding and signaling will be described. Data will be presented that support a critical role for these growth factor-proteoglycan interactions in controlling tissue development, repair and disease. Thoughts on human disease treatment and approaches for tissue engineering targeting the extracellular matrix will be presented.

Hosted by Uwe Täuber.

Abnormal stretch and strain is a major cause of injury to the spinal cord and brainstem. Such forces can develop from age-related degeneration, congenital malformations, occupational exposure, or trauma such as sporting accidents, whiplash and blast injury. While current imaging technologies provide excellent morphology and anatomy of the spinal cord, there is no validated diagnostic tool to assess mechanical stresses exerted upon the spinal cord and brainstem. Furthermore, there is no current means to correlate these stress patterns with known spinal cord injuries and other clinical metrics such as neurological impairment. We have therefore developed the spinal cord stress injury assessment (SCOSIA) system, which uses imaging and finite element analysis to predict stretch injury. This system was tested on a small cohort of neurosurgery patients. Initial results show that the calculated stress values decreased following surgery, and that this decrease was accompanied by a significant decrease in neurological symptoms. Regression analysis identified modest correlations between stress values and clinical metrics. SCOSIA therefore shows encouraging initial results and may have applicability to evaluating trauma and degenerative disease involving the spinal cord and brainstem.

Population rate or activity equations are the foundation of a common approach to modeling for neural networks and have been used to analyze various neural phenomena from EEG rhythms to visual hallucinations. However, these equations only provide mean field dynamics for the firing rate or activity of neurons within a network given some connectivity. The shortcoming of these equations is that they take into account only the average firing rate while leaving out higher order statistics like correlations between firing. This talk will describe how techniques from non-equilibrium statistical physics have been adapted in order to understand the statistics of neural systems. In particular, I will describe a path integral formulation of the stochastic neural model and develop a "semi-classical" expansion from this. In addition, renormalization group arguments can be made to make a connection to the cortical slice experiments of so-called "neural avalanches". This approach brings together different points of view on neural modeling.