Cell types, network homeostasis, and pathological compensation from a biologically plausible ion channel expression model.

How do neurons develop, control, and maintain their electrical signaling properties in spite of ongoing protein turnover and perturbations to activity? From generic assumptions about the molecular biology underlying channel expression, we derive a simple model and show how it encodes an "activity set point" in single neurons. The model generates diverse self-regulating cell types and relates correlations in conductance expression observed in vivo to underlying channel expression rates. Synaptic as well as intrinsic conductances can be regulated to make a self-assembling central pattern generator network; thus, network-level homeostasis can emerge from cell-autonomous regulation rules. Finally, we demonstrate that the outcome of homeostatic regulation depends on the complement of ion channels expressed in cells: in some cases, loss of specific ion channels can be compensated; in others, the homeostatic mechanism itself causes pathological loss of function.

A reversible infinite HMM using normalised random measures

Copyright 2014 by the author(s). We present a nonparametric prior over reversible Markov chains. We use completely random measures, specifically gamma processes, to construct a countably infinite graph with weighted edges. By enforcing symmetry to make the edges undirected we define a prior over random walks on graphs that results in a reversible Markov chain. The resulting prior over infinite transition matrices is closely related to the hierarchical Dirichlet process but enforces reversibility. A reinforcement scheme has recently been proposed with similar properties, but the de Finetti measure is not well characterised. We take the alternative approach of explicitly constructing the mixing measure, which allows more straightforward and efficient inference at the cost of no longer having a closed form predictive distribution. We use our process to construct a reversible infinite HMM which we apply to two real datasets, one from epigenomics and one ion channel recording.

Ni-silicide growth kinetics in Si and Si/SiO2 core/shell nanowires.

A systematic study of the kinetics of axial Ni silicidation of as-grown and oxidized Si nanowires (SiNWs) with different crystallographic orientations and core diameters ranging from ∼ 10 to 100 nm is presented. For temperatures between 300 and 440 °C the length of the total axial silicide intrusion varies with the square root of time, which provides clear evidence that the rate limiting step is diffusion of Ni through the growing silicide phase(s). A retardation of Ni-silicide formation for oxidized SiNWs is found, indicative of a stress induced lowering of the diffusion coefficients. Extrapolated growth constants indicate that the Ni flux through the silicided NW is dominated by surface diffusion, which is consistent with an inverse square root dependence of the silicide length on the NW diameter as observed for (111) orientated SiNWs. In situ TEM silicidation experiments show that NiSi(2) is the first forming phase for as-grown and oxidized SiNWs. The silicide-SiNW interface is thereby atomically abrupt and typically planar. Ni-rich silicide phases subsequently nucleate close to the Ni reservoir, which for as-grown SiNWs can lead to a complete channel break-off for prolonged silicidation due to significant volume expansion and morphological changes.

Revealing lithium-silicide phase transformations in nano-structured silicon-based lithium ion batteries via in situ NMR spectroscopy.

Nano-structured silicon anodes are attractive alternatives to graphitic carbons in rechargeable Li-ion batteries, owing to their extremely high capacities. Despite their advantages, numerous issues remain to be addressed, the most basic being to understand the complex kinetics and thermodynamics that control the reactions and structural rearrangements. Elucidating this necessitates real-time in situ metrologies, which are highly challenging, if the whole electrode structure is studied at an atomistic level for multiple cycles under realistic cycling conditions. Here we report that Si nanowires grown on a conducting carbon-fibre support provide a robust model battery system that can be studied by (7)Li in situ NMR spectroscopy. The method allows the (de)alloying reactions of the amorphous silicides to be followed in the 2nd cycle and beyond. In combination with density-functional theory calculations, the results provide insight into the amorphous and amorphous-to-crystalline lithium-silicide transformations, particularly those at low voltages, which are highly relevant to practical cycling strategies.