Investigation of the dipole response of nickel isotopes in the presence of a high-frequency electromagnetic field

The electric dipole response of neutron-rich nickel isotopes has been investigated using the LAND setup at GSI in Darmstadt (Germany). Relativistic secondary beams of 56−57Ni and 67−72Ni at approximately 500 AMeV have been generated using projectile fragmentation of stable ions on a 4 g/cm2 Be target and subsequent separation in the magnetic dipole fields of the FRagment Separator (FRS). After reaching the LAND setup in Cave C, the radioactive ions were excited electromagnetically in the electric field of a Pb target. The decay products have been measured in inverse kinematics using various detectors. Neutron-rich 67−69Ni isotopes decay by the emission of neutrons, which are detected in the LAND detector. The present analysis concentrates on the (gamma,n) and (gamma,2n) channels in these nuclei, since the proton and three-neutron thresholds are unlikely to be reached considering the virtual photon spectrum for nickel ions at 500 AMeV. A measurement of the stable 58Ni isotope is used as a benchmark to check the accuracy of the present results with previously published data. The measured (gamma,n) and (gamma,np) channels are compared with an inclusive photoneutron measurement by Fultz and coworkers, which are consistent within the respective errors. The measured excitation energy distributions of 67−69Ni contain a large portion of the Giant Dipole Resonance (GDR) strength predicted by the Thomas-Reiche-Kuhn energy-weighted sum rule, as well as a significant amount of low-lying E1 strength, that cannot be attributed to the GDR alone. The GDR distribution parameters are calculated using well-established semi-empirical systematic models, providing the peak energies and widths. The GDR strength is extracted from the chi-square minimization of the model GDR to the measured data of the (gamma,2n) channel, thereby excluding any influence of eventual low-lying strength. The subtraction of the obtained GDR distribution from the total measured E1 strength provides the low-lying E1 strength distribution, which is attributed to the Pygmy Dipole Resonance (PDR). The extraction of the peak energy, width and strength is performed using a Gaussian function. The minimization of trial Gaussian distributions to the data does not converge towards a sharp minimum. Therefore, the results are presented by a chi-square distribution as a function of all three Gaussian parameters. Various predictions of PDR distributions exist, as well as a recent measurement of the 68Ni pygmy dipole-resonance obtained by virtual photon scattering, to which the present pygmy dipole-resonance distribution is also compared.

Periodicities in the Hodgkin-Huxley model and versions of this model with stochastic input

A field of computational neuroscience develops mathematical models to describe neuronal systems. The aim is to better understand the nervous system. Historically, the integrate-and-fire model, developed by Lapique in 1907, was the first model describing a neuron. In 1952 Hodgkin and Huxley [8] described the so called Hodgkin-Huxley model in the article “A Quantitative Description of Membrane Current and Its Application to Conduction and Excitation in Nerve”. The Hodgkin-Huxley model is one of the most successful and widely-used biological neuron models. Based on experimental data from the squid giant axon, Hodgkin and Huxley developed their mathematical model as a four-dimensional system of first-order ordinary differential equations. One of these equations characterizes the membrane potential as a process in time, whereas the other three equations depict the opening and closing state of sodium and potassium ion channels. The membrane potential is proportional to the sum of ionic current flowing across the membrane and an externally applied current. For various types of external input the membrane potential behaves differently. This thesis considers the following three types of input: (i) Rinzel and Miller [15] calculated an interval of amplitudes for a constant applied current, where the membrane potential is repetitively spiking; (ii) Aihara, Matsumoto and Ikegaya [1] said that dependent on the amplitude and the frequency of a periodic applied current the membrane potential responds periodically; (iii) Izhikevich [12] stated that brief pulses of positive and negative current with different amplitudes and frequencies can lead to a periodic response of the membrane potential. In chapter 1 the Hodgkin-Huxley model is introduced according to Izhikevich [12]. Besides the definition of the model, several biological and physiological notes are made, and further concepts are described by examples. Moreover, the numerical methods to solve the equations of the Hodgkin-Huxley model are presented which were used for the computer simulations in chapter 2 and chapter 3. In chapter 2 the statements for the three different inputs (i), (ii) and (iii) will be verified, and periodic behavior for the inputs (ii) and (iii) will be investigated. In chapter 3 the inputs are embedded in an Ornstein-Uhlenbeck process to see the influence of noise on the results of chapter 2.