- Frankfurt Institute for Advanced Studies (6)
Hagedorn states and thermalization : XLIX International Winter Meeting on Nuclear Physics, 24 - 28 January 2011, Bormio, Italy
- In recent years, Hagedorn states have been used to explain the equilibrium and transport properties of a hadron gas close to the QCD critical temperature. These massive resonances are shown to lower h/s to near the AdS/CFT limit close to the phase transition. A comparison of the Hagedorn model to recent lattice results is made and it is found that the hadrons can reach chemical equilibrium almost immediately, well before the chemical freeze-out temperatures found in thermal fits for a hadron gas without Hagedorn states.
LatticeQCD using OpenCL
- We report on our implementation of LatticeQCD applications using OpenCL. We focus on the
general concept and on distributing different parts on hybrid systems, consisting of both CPUs
(Central Processing Units) and GPUs (Graphic Processing Units).
Feedforward inhibition and synaptic scaling - two sides of the same coin?
- Feedforward inhibition and synaptic scaling are important adaptive processes that control the total input a neuron can receive from its afferents. While often studied in isolation, the two have been reported to co-occur in various brain regions. The functional implications of their interactions remain unclear, however. Based on a probabilistic modeling approach, we show here that fast feedforward inhibition and synaptic scaling interact synergistically during unsupervised learning. In technical terms, we model the input to a neural circuit using a normalized mixture model with Poisson noise. We demonstrate analytically and numerically that, in the presence of lateral inhibition introducing competition between different neurons, Hebbian plasticity and synaptic scaling approximate the optimal maximum likelihood solutions for this model. Our results suggest that, beyond its conventional use as a mechanism to remove undesired pattern variations, input normalization can make typical neural interaction and learning rules optimal on the stimulus subspace defined through feedforward inhibition. Furthermore, learning within this subspace is more efficient in practice, as it helps avoid locally optimal solutions. Our results suggest a close connection between feedforward inhibition and synaptic scaling which may have important functional implications for general cortical processing.
Parton recombination and fluctuations of conserved charges
- We study various fluctuation and correlation signals of the deconfined state using a dynamical recombination approach (quark Molecular Dynamics, qMD). We analyse charge ratio fluctuations, charge transfer fluctuations and baryon-strangeness correlations as a function of the center of mass energy with a set of central Pb+Pb/Au+Au events from AGS energies on (Elab = 4 AGeV) up to the highest RHIC energy available (V sNN = 200 GeV) and as a function of time with a set of central Au+Au qMD events at V sNN = 200 GeV with and without applying our hadronization procedure. For all studied quantities, the results start from values compatible with a weakly coupled QGP in the early stage and end with values compatible with the hadronic result in the final state. We show that the loss of the signal occurs at the same time as hadronization and trace it back to the dynamical recombination process implemented in our model.
Collapse of flow: probing the order of the phase transition
- We discuss the present collective flow signals for the phase transition to the quark-gluon plasma (QGP) and the collective flow as a barometer for the equation of state (EoS). We emphasize the importance of the flow excitation function from 1 to 50A GeV: here the hydrodynamicmodel has predicted the collapse of the v1-flow at ~ 10A GeV and of the v2-flow at ~ 40A GeV. In the latter case, this has recently been observed by the NA49 collaboration. Since hadronic rescattering models predict much larger flow than observed at this energy, we interpret this observation as potential evidence for a first order phase transition at high baryon density pB.
TRENTOOL: an open source toolbox to estimate neural directed interactions with transfer entropy
- Poster presentation from Twentieth Annual Computational Neuroscience Meeting: CNS*2011 Stockholm, Sweden. 23-28 July 2011. Poster presentation To investigate directed interactions in neural networks we often use Norbert Wiener's famous definition of observational causality. Wiener’s definition states that an improvement of the prediction of the future of a time series X from its own past by the incorporation of information from the past of a second time series Y is seen as an indication of a causal interaction from Y to X. Early implementations of Wiener's principle – such as Granger causality – modelled interacting systems by linear autoregressive processes and the interactions themselves were also assumed to be linear. However, in complex systems – such as the brain – nonlinear behaviour of its parts and nonlinear interactions between them have to be expected. In fact nonlinear power-to-power or phase-to-power interactions between frequencies are reported frequently. To cover all types of non-linear interactions in the brain, and thereby to fully chart the neural networks of interest, it is useful to implement Wiener's principle in a way that is free of a model of the interaction . Indeed, it is possible to reformulate Wiener's principle based on information theoretic quantities to obtain the desired model-freeness. The resulting measure was originally formulated by Schreiber  and termed transfer entropy (TE). Shortly after its publication transfer entropy found applications to neurophysiological data. With the introduction of new, data efficient estimators (e.g. ) TE has experienced a rapid surge of interest (e.g. ). Applications of TE in neuroscience range from recordings in cultured neuronal populations to functional magnetic resonanace imaging (fMRI) signals. Despite widespread interest in TE, no publicly available toolbox exists that guides the user through the difficulties of this powerful technique. TRENTOOL (the TRansfer ENtropy TOOLbox) fills this gap for the neurosciences by bundling data efficient estimation algorithms with the necessary parameter estimation routines and nonparametric statistical testing procedures for comparison to surrogate data or between experimental conditions. TRENTOOL is an open source MATLAB toolbox based on the Fieldtrip data format. We evaluated the performance of the toolbox on simulation data and also a neuronal dataset that provides connections that are truly unidirectional to circumvent the following generic problem: typically, for any result of an analysis of directed interactions in the brain there will be a plausible explanation because of the combination of feedforward and feedback connectivity between any two measurement sites. Therefore, we estimated TE between the electroretinogram (ERG) and the LFP response in the tectum of the turtle (Chrysemys scripta elegans) under visual stimulation by random light pulses. In addition, we also investigated transfer entropy between the input to the light source (TTL pulse) and the ERG, to test the ability of TE to detect directed interactions between signals with vastly different properties. We found significant (p<0.0005) causal interactions from the TTL pulse to the ERG and from the ERG to the tectum – as expected. No significant TE was detected in the reverse direction. CONCLUSION: TRENTOOL is an easy to use implementation of transfer entropy estimation combined with statistical testing routines suitable for the analysis of directed interactions in neuronal data.