“Biologically detailed single neuron and network models ar

“Biologically detailed single neuron and network models are important for understanding how ion channels, synapses and anatomical connectivity underlie the complex electrical behavior of the brain. While neuronal simulators such as NEURON, GENESIS, MOOSE, NEST, and PSICS facilitate the development of these data-driven neuronal models, the specialized languages they employ are generally not interoperable, limiting model accessibility and

preventing reuse of model components and cross-simulator validation. To overcome these problems we have used an Open Source software approach to develop NeuroML, a neuronal model description language based on XML (Extensible Markup Language). This enables these detailed models and their components to be defined in a standalone form, allowing LCL161 them to be used across multiple simulators and archived in a standardized format. Here we describe the structure of NeuroML and demonstrate its scope by converting into NeuroML models of a number of different voltage-and ligand-gated conductances, models of electrical coupling, synaptic transmission and short-term plasticity, together with morphologically detailed models of individual neurons. We have also used these NeuroML-based components to develop an highly detailed cortical

network model. NeuroML-based NCT-501 ic50 model descriptions were validated by demonstrating similar model behavior across five independently developed simulators. Although our results confirm that simulations run on different simulators converge, they reveal limits to model interoperability, by showing that for some models convergence only occurs at high levels of spatial and temporal discretisation, when the

computational overhead is high. Our development of NeuroML as a common description language for biophysically detailed neuronal and network models enables interoperability across multiple simulation environments, thereby improving model transparency, accessibility Salubrinal research buy and reuse in computational neuroscience.”
“Temperature-dependent photoluminescence (PL) properties of indium-doped ZnO nanorods grown by vapor transport method are investigated. At low temperatures, two peaks at 3.294 and 3.221 eV are observed. With the temperature increasing, these peaks shift to 3.315 and 3.238 eV, respectively. This is the characteristic of the transformation from donor-acceptor pair recombination to free electron-to-neutral acceptor (eA(0)) transition. Two acceptors are identified with energy levels of similar to 120 and similar to 200 meV (labeled A1 and A2). The nanorods show yellow emission around 2.1 eV at 8 K, which blueshifts to 2.3 eV at room temperature. From the thermal quenching analysis, it is suggested that the acceptor involved in the yellow emission is the same as A2. A defect level close to A1 is also observed in the PL-excitation spectrum.

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