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Noise appears in the brain due to various sources, such as ionic channel fluctuations and synaptic events. They affect the activities of the brain and influence neuron action potentials. Stochastic differential equations have been used to model firing patterns of neurons subject to noise. In this work, we consider perturbing noise in the adaptive exponential integrate-and-fire (AEIF) neuron. The AEIF is a two-dimensional model that describes different neuronal firing patterns by varying its parameters. Noise is added in the equation related to the membrane potential. We show that a noise current can induce continuous and noncontinuous transitions in neuronal interspike intervals. Moreover, we show that the noncontinuous transition occurs mainly for parameters close to the border between tonic spiking and burst activities of the neuron without noise.

Brain plasticity refers to brain's ability to change neuronal connections, as a result of environmental stimuli, new experiences, or damage. In this work, we study the effects of the synaptic delay on both the coupling strengths and synchronisation in a neuronal network with synaptic plasticity. We build a network of Hodgkin-Huxley neurons, where the plasticity is given by the Hebbian rules. We verify that without time delay the excitatory synapses became stronger from the high frequency to low frequency neurons and the inhibitory synapses increases in the opposite way, when the delay is increased the network presents a non-trivial topology. Regarding the synchronisation, only for small values of the synaptic delay this phenomenon is observed.

In this paper, we study the effects of spike timing-dependent plasticity on synchronisation in a network of Hodgkin-Huxley neurons. Neuron plasticity is a flexible property of a neuron and its network to change temporarily or permanently their biochemical, physiological, and morphological characteristics, in order to adapt to the environment. Regarding the plasticity, we consider Hebbian rules, specifically for spike timing-dependent plasticity (STDP), and with regard to network, we consider that the connections are randomly distributed. We analyse the synchronisation and desynchronisation according to an input level and probability of connections. Moreover, we verify that the transition for synchronisation depends on the neuronal network architecture, and the external perturbation level.

Optical properties of ferromagnetic half-metallic full-Heusler Co$_{2}$FeGe alloy are investigated experimentally and theoretically. Co$_{2}$FeGe thin films were obtained by DC magnetron sputtering and show the saturation magnetization at $T$=10 K of $m\approx$5.6 $\mu_{B}$/f.u., close to the value predicted by the Slater-Pauling rule. First-principles calculations of the electronic structure and the dielectric tensor are performed using the full-potential linearized-augmented-plane-wave method in the generalized gradient (GGA) and GGA+U approximations. The measured interband optical conductivity spectrum for the alloy exhibits a strong absorption band in the 1 - 4 eV energy range with pronounced fine structure, which agrees well with the calculated half-metallic spectrum of the system, suggesting a near perfect spin-polarization in the material., Comment: 6 pages, 9 figures

In this work we study the behavior of the optical phonon modes in bilayer graphene devices by applying top gate voltage, using Raman scattering. We observe the splitting of the Raman G band as we tune the Fermi level of the sample, which is explained in terms of mixing of the Raman (Eg) and infrared (Eu) phonon modes, due to different doping in the two layers. We theoretically analyze our data in terms of the bilayer graphene phonon self-energy which includes non-homogeneous charge carrier doping between the graphene layers. We show that the comparison between the experiment and theoretical model not only gives information about the total charge concentration in the bilayer graphene device, but also allows to separately quantify the amount of unintentional charge coming from the top and the bottom of the system, and therefore to characterize the interaction of bilayer graphene with its surrounding environment.