Direct Measurement of the Top Quark Charge at Hadron Colliders

We consider photon radiation in tbar-t events at the upgraded Fermilab Tevatron and the CERN Large Hadron Collider (LHC) as a tool to measure the electric charge of the top quark. We analyze the contributions of tbar-t-gamma production and radiative top quark decays to p-p, pbar-p -> gamma l^+/- nu bbar-b jj, assuming that both b-quarks are tagged. With 20~fb^{-1} at the Tevatron, the possibility that the ``top quark'' discovered in Run I is actually an exotic charge -4/3 quark can be ruled out at the 95% confidence level. At the LHC, it will be possible to determine the charge of the top quark with an accuracy of about 10%.Comment: Revtex, 24 pages, 2 tables, 9 figure

Finite-size and correlation-induced effects in Mean-field Dynamics

The brain's activity is characterized by the interaction of a very large number of neurons that are strongly affected by noise. However, signals often arise at macroscopic scales integrating the effect of many neurons into a reliable pattern of activity. In order to study such large neuronal assemblies, one is often led to derive mean-field limits summarizing the effect of the interaction of a large number of neurons into an effective signal. Classical mean-field approaches consider the evolution of a deterministic variable, the mean activity, thus neglecting the stochastic nature of neural behavior. In this article, we build upon two recent approaches that include correlations and higher order moments in mean-field equations, and study how these stochastic effects influence the solutions of the mean-field equations, both in the limit of an infinite number of neurons and for large yet finite networks. We introduce a new model, the infinite model, which arises from both equations by a rescaling of the variables and, which is invertible for finite-size networks, and hence, provides equivalent equations to those previously derived models. The study of this model allows us to understand qualitative behavior of such large-scale networks. We show that, though the solutions of the deterministic mean-field equation constitute uncorrelated solutions of the new mean-field equations, the stability properties of limit cycles are modified by the presence of correlations, and additional non-trivial behaviors including periodic orbits appear when there were none in the mean field. The origin of all these behaviors is then explored in finite-size networks where interesting mesoscopic scale effects appear. This study leads us to show that the infinite-size system appears as a singular limit of the network equations, and for any finite network, the system will differ from the infinite system

Avalanches in a Stochastic Model of Spiking Neurons

Neuronal avalanches are a form of spontaneous activity widely observed in cortical slices and other types of nervous tissue, both in vivo and in vitro. They are characterized by irregular, isolated population bursts when many neurons fire together, where the number of spikes per burst obeys a power law distribution. We simulate, using the Gillespie algorithm, a model of neuronal avalanches based on stochastic single neurons. The network consists of excitatory and inhibitory neurons, first with all-to-all connectivity and later with random sparse connectivity. Analyzing our model using the system size expansion, we show that the model obeys the standard Wilson-Cowan equations for large network sizes ( neurons). When excitation and inhibition are closely balanced, networks of thousands of neurons exhibit irregular synchronous activity, including the characteristic power law distribution of avalanche size. We show that these avalanches are due to the balanced network having weakly stable functionally feedforward dynamics, which amplifies some small fluctuations into the large population bursts. Balanced networks are thought to underlie a variety of observed network behaviours and have useful computational properties, such as responding quickly to changes in input. Thus, the appearance of avalanches in such functionally feedforward networks indicates that avalanches may be a simple consequence of a widely present network structure, when neuron dynamics are noisy. An important implication is that a network need not be “critical” for the production of avalanches, so experimentally observed power laws in burst size may be a signature of noisy functionally feedforward structure rather than of, for example, self-organized criticality

Emergent Oscillations in Networks of Stochastic Spiking Neurons

Networks of neurons produce diverse patterns of oscillations, arising from the network's global properties, the propensity of individual neurons to oscillate, or a mixture of the two. Here we describe noisy limit cycles and quasi-cycles, two related mechanisms underlying emergent oscillations in neuronal networks whose individual components, stochastic spiking neurons, do not themselves oscillate. Both mechanisms are shown to produce gamma band oscillations at the population level while individual neurons fire at a rate much lower than the population frequency. Spike trains in a network undergoing noisy limit cycles display a preferred period which is not found in the case of quasi-cycles, due to the even faster decay of phase information in quasi-cycles. These oscillations persist in sparsely connected networks, and variation of the network's connectivity results in variation of the oscillation frequency. A network of such neurons behaves as a stochastic perturbation of the deterministic Wilson-Cowan equations, and the network undergoes noisy limit cycles or quasi-cycles depending on whether these have limit cycles or a weakly stable focus. These mechanisms provide a new perspective on the emergence of rhythmic firing in neural networks, showing the coexistence of population-level oscillations with very irregular individual spike trains in a simple and general framework

Top Quark Physics

We review the prospects for studies of the top quark at the LHC.We review the prospects for studies of the top quark at the LHC. Members of the working group who have contributed to this document are: A.Ahmadov, G.Azuelos, U.Baur, A.Belyaev, E.L.Berger, W.Bernreuther, E.E.Boos, M.Bosman, A.Brandenburg, R.Brock, M.Buice, N.Cartiglia, F.Cerutti, A.Cheplakov, L.Chikovani, M.Cobal-Grassmann, G.Corcella, F.del Aguila, T.Djobava, J.Dodd, V.Drollinger, A.Dubak, S.Frixione, D.Froidevaux, B.Gonzalez Pineiro, Y.P.Gouz, D.Green, P.Grenier, S.Heinemeyer, W.Hollik, V.Ilyin, C.Kao, A.Kharchilava, R. Kinnunen, V.V.Kukhtin, S.Kunori, L.La Rotonda, A.Lagatta, M.Lefebvre, K.Maeshima, G.Mahlon, S.Mc Grath, G.Medin, R.Mehdiyev, B.Mele, Z.Metreveli, D.O'Neil, L.H.Orr, D.Pallin, S.Parke, J.Parsons, D.Popovic, L.Reina, E.Richter-Was, T.G.Rizzo, D.Salihagic, M.Sapinski, M.H.Seymour, V.Simak, L.Simic, G.Skoro, S.R.Slabospitsky, J.Smolik, L.Sonnenschein, T.Stelzer, N.Stepanov, Z.Sullivan, T.Tait, I.Vichou, R.Vidal, D.Wackeroth, G.Weiglein, S.Willenbrock, W.W

Complementarity of Spike- and Rate-Based Dynamics of Neural Systems

Relationships between spiking-neuron and rate-based approaches to the dynamics of neural assemblies are explored by analyzing a model system that can be treated by both methods, with the rate-based method further averaged over multiple neurons to give a neural-field approach. The system consists of a chain of neurons, each with simple spiking dynamics that has a known rate-based equivalent. The neurons are linked by propagating activity that is described in terms of a spatial interaction strength with temporal delays that reflect distances between neurons; feedback via a separate delay loop is also included because such loops also exist in real brains. These interactions are described using a spatiotemporal coupling function that can carry either spikes or rates to provide coupling between neurons. Numerical simulation of corresponding spike- and rate-based methods with these compatible couplings then allows direct comparison between the dynamics arising from these approaches. The rate-based dynamics can reproduce two different forms of oscillation that are present in the spike-based model: spiking rates of individual neurons and network-induced modulations of spiking rate that occur if network interactions are sufficiently strong. Depending on conditions either mode of oscillation can dominate the spike-based dynamics and in some situations, particularly when the ratio of the frequencies of these two modes is integer or half-integer, the two can both be present and interact with each other