Coherence scale of the two-dimensional Kondo Lattice model

A doped hole in the two-dimensional half-filled Kondo lattice model with exchange J and hopping t has momentum (pi,pi) irrespective of the coupling J/t. The quasiparticle residue of the doped hole, Z_{(\pi, \pi)}, tracks the Kondo scale, T_K, of the corresponding single impurity model. Those results stem from high precision quantum Monte Carlo simulations on lattices up to 12 X 12. Accounting for small dopings away from half-filling within a rigid band approximation, this result implies that the effective mass of the charge carriers at the Fermi level tracks 1/T_K or equivalently that the coherence temperature T_{coh} \propto T_K. This results is consistent with the large-N saddle point of the SU(N) symmetric Kondo lattice model.Comment: 4 pages, 4 figure

Fully automatic telemetry data processor

Satellite Telemetry Automatic Reduction System /STARS 2/, a fully automatic computer-controlled telemetry data processor, maximizes data recovery, reduces turnaround time, increases flexibility, and improves operational efficiency. The system incorporates a CDC 3200 computer as its central element

Self-Consistent Theory of the Gain Linewidth for Quantum Cascade Lasers

The linewidth in intersubband transitions can be significantly reduced below the sum of the lifetime broadening for the involved states, if the scattering environment is similar for both states. This is studied within a nonequilibrium Green function approach here. We find that the effect is of particular relevance for a recent, relatively low doped, THz quantum cascade laser.Comment: 3 pages, figures include

Neural networks with dynamical synapses: from mixed-mode oscillations and spindles to chaos

Understanding of short-term synaptic depression (STSD) and other forms of synaptic plasticity is a topical problem in neuroscience. Here we study the role of STSD in the formation of complex patterns of brain rhythms. We use a cortical circuit model of neural networks composed of irregular spiking excitatory and inhibitory neurons having type 1 and 2 excitability and stochastic dynamics. In the model, neurons form a sparsely connected network and their spontaneous activity is driven by random spikes representing synaptic noise. Using simulations and analytical calculations, we found that if the STSD is absent, the neural network shows either asynchronous behavior or regular network oscillations depending on the noise level. In networks with STSD, changing parameters of synaptic plasticity and the noise level, we observed transitions to complex patters of collective activity: mixed-mode and spindle oscillations, bursts of collective activity, and chaotic behaviour. Interestingly, these patterns are stable in a certain range of the parameters and separated by critical boundaries. Thus, the parameters of synaptic plasticity can play a role of control parameters or switchers between different network states. However, changes of the parameters caused by a disease may lead to dramatic impairment of ongoing neural activity. We analyze the chaotic neural activity by use of the 0-1 test for chaos (Gottwald, G. & Melbourne, I., 2004) and show that it has a collective nature.Comment: 7 pages, Proceedings of 12th Granada Seminar, September 17-21, 201

Critical and resonance phenomena in neural networks

Brain rhythms contribute to every aspect of brain function. Here, we study critical and resonance phenomena that precede the emergence of brain rhythms. Using an analytical approach and simulations of a cortical circuit model of neural networks with stochastic neurons in the presence of noise, we show that spontaneous appearance of network oscillations occurs as a dynamical (non-equilibrium) phase transition at a critical point determined by the noise level, network structure, the balance between excitatory and inhibitory neurons, and other parameters. We find that the relaxation time of neural activity to a steady state, response to periodic stimuli at the frequency of the oscillations, amplitude of damped oscillations, and stochastic fluctuations of neural activity are dramatically increased when approaching the critical point of the transition.Comment: 8 pages, Proceedings of 12th Granada Seminar, September 17-21, 201