Training deep neural density estimators to identify mechanistic models of neural dynamics

Mechanistic modeling in neuroscience aims to explain observed phenomena in terms of underlying causes. However, determining which model parameters agree with complex and stochastic neural data presents a significant challenge. We address this challenge with a machine learning tool which uses deep neural density estimators-- trained using model simulations-- to carry out Bayesian inference and retrieve the full space of parameters compatible with raw data or selected data features. Our method is scalable in parameters and data features, and can rapidly analyze new data after initial training. We demonstrate the power and flexibility of our approach on receptive fields, ion channels, and Hodgkin-Huxley models. We also characterize the space of circuit configurations giving rise to rhythmic activity in the crustacean stomatogastric ganglion, and use these results to derive hypotheses for underlying compensation mechanisms. Our approach will help close the gap between data-driven and theory-driven models of neural dynamics

Evolution of precipitates, in particular cruciform and cuboid particles, during simulated direct charging of thin slab cast vanadium microalloyed steels

A study has been undertaken of four vanadium based steels which have been processed by a simulated direct charging route using processing parameters typical of thin slab casting, where the cast product has a thickness of 50 to 80mm ( in this study 50 mm) and is fed directly to a furnace to equalise the microstructure prior to rolling. In the direct charging process, cooling rates are faster, equalisation times shorter and the amount of deformation introduced during rolling less than in conventional practice. Samples in this study were quenched after casting, after equalisation, after 4th rolling pass and after coiling, to follow the evolution of microstructure. The mechanical and toughness properties and the microstructural features might be expected to differ from equivalent steels, which have undergone conventional processing. The four low carbon steels (~0.06wt%) which were studied contained 0.1wt%V (V-N), 0.1wt%V and 0.010wt%Ti (V-Ti), 0.1wt%V and 0.03wt%Nb (V-Nb), and 0.1wt%V, 0.03wt%Nb and 0.007wt%Ti (V-Nb-Ti). Steels V-N and V-Ti contained around 0.02wt% N, while the other two contained about 0.01wt%N. The as-cast steels were heated at three equalising temperatures of 1050C, 1100C or 1200C and held for 30-60 minutes prior to rolling. Optical microscopy and analytical electron microscopy, including parallel electron energy loss spectroscopy (PEELS), were used to characterise the precipitates. In the as-cast condition, dendrites and plates were found. Cuboid particles were seen at this stage in Steel V-Ti, but they appeared only in the other steels after equalization. In addition, in the final product of all the steels, fine particles were seen, but it was only in the two titanium steels that cruciform precipitates were present. PEELS analysis showed that the dendrites, plates, cuboids, cruciforms and fine precipitates were essentially nitrides. The two Ti steels had better toughness than the other steels but inferior lower yield stress values. This was thought to be, in part, due to the formation of cruciform precipitates in austenite, thereby removing nitrogen and the microalloying elements which would have been expected to precipitate in ferrite as dispersion hardening particles

Generation of macroscopic pair-correlated atomic beams by four-wave mixing in Bose-Einstein condensates

By colliding two Bose-Einstein condensates we have observed strong bosonic stimulation of the elastic scattering process. When a weak input beam was applied as a seed, it was amplified by a factor of 20. This large gain atomic four-wave mixing resulted in the generation of two macroscopically occupied pair-correlated atomic beams.Comment: Please take eps files for best details in figure

Stable Propagation of a Burst Through a One-Dimensional Homogeneous Excitatory Chain Model of Songbird Nucleus HVC

We demonstrate numerically that a brief burst consisting of two to six spikes can propagate in a stable manner through a one-dimensional homogeneous feedforward chain of non-bursting neurons with excitatory synaptic connections. Our results are obtained for two kinds of neuronal models, leaky integrate-and-fire (LIF) neurons and Hodgkin-Huxley (HH) neurons with five conductances. Over a range of parameters such as the maximum synaptic conductance, both kinds of chains are found to have multiple attractors of propagating bursts, with each attractor being distinguished by the number of spikes and total duration of the propagating burst. These results make plausible the hypothesis that sparse precisely-timed sequential bursts observed in projection neurons of nucleus HVC of a singing zebra finch are intrinsic and causally related.Comment: 13 pages, 6 figure

Observation of anomalous spin-state segregation in a trapped ultra-cold vapor

We observe counter-intuitive spin segregation in an inhomogeneous sample of ultra-cold, non-condensed Rubidium atoms in a magnetic trap. We use spatially selective microwave spectroscopy to verify a model that accounts for the differential forces on two internal spin states. In any simple understanding of the cloud dynamics, the forces are far too small to account for the dramatic transient spin polarizations observed. The underlying mechanism remains to be elucidated.Comment: 5 pages, 3 figure

On the Behaviour of General-Purpose Applications on Cloud Storages

Managing data over cloud infrastructures raises novel challenges with respect to existing and well studied approaches such as ACID and long running transactions. One of the main requirements is to provide availability and partition tolerance in a scenario with replicas and distributed control. This comes at the price of a weaker consistency, usually called eventual consistency. These weak memory models have proved to be suitable in a number of scenarios, such as the analysis of large data with Map-Reduce. However, due to the widespread availability of cloud infrastructures, weak storages are used not only by specialised applications but also by general purpose applications. We provide a formal approach, based on process calculi, to reason about the behaviour of programs that rely on cloud stores. For instance, one can check that the composition of a process with a cloud store ensures `strong' properties through a wise usage of asynchronous message-passing

Tunable tunneling: An application of stationary states of Bose-Einstein condensates in traps of finite depth

The fundamental question of how Bose-Einstein condensates tunnel into a barrier is addressed. The cubic nonlinear Schrodinger equation with a finite square well potential, which models a Bose-Einstein condensate in a quasi-one-dimensional trap of finite depth, is solved for the complete set of localized and partially localized stationary states, which the former evolve into when the nonlinearity is increased. An immediate application of these different solution types is tunable tunneling. Magnetically tunable Feshbach resonances can change the scattering length of certain Bose-condensed atoms, such as $^{85}$Rb, by several orders of magnitude, including the sign, and thereby also change the mean field nonlinearity term of the equation and the tunneling of the wavefunction. We find both linear-type localized solutions and uniquely nonlinear partially localized solutions where the tails of the wavefunction become nonzero at infinity when the nonlinearity increases. The tunneling of the wavefunction into the non-classical regime and thus its localization therefore becomes an external experimentally controllable parameter.Comment: 11 pages, 5 figure

Electrophysiological correlates of high-level perception during spatial navigation

We studied the electrophysiological basis of object recognition by recording scalp\ud electroencephalograms while participants played a virtual-reality taxi driver game.\ud Participants searched for passengers and stores during virtual navigation in simulated\ud towns. We compared oscillatory brain activity in response to store views that were targets or\ud nontargets (during store search) or neutral (during passenger search). Even though store\ud category was solely defined by task context (rather than by sensory cues), frontal ...\ud \u