Implications of the precision data for very light Higgs boson scenario in 2HDM, 2

We present an up-to-date analysis of the constraints imposed bythe precision data on the ($CP-$ conserving) Two-Higgs-Doublet Model of type II, with emphasis on the possible existence of very light neutral (pseudo)scalar Higgs boson with mass below 20--30 GeV. We show that even in the presence of such light particles, the 2HDM(II) can describe the electroweak data with precision comparable to that given by the SM. Particularly interesting lower limits on the mass of the lighter neutral $CP-$even scalar $h^0$ are obtained in the scenario with a light $CP-$odd Higgs boson $A^0$ and large $\tan\beta$

Topographic hub maps of the human structural neocortical network

Hubs within the neocortical structural network determined by graph theoretical analysis play a crucial role in brain function. We mapped neocortical hubs topographically, using a sample population of 63 young adults. Subjects were imaged with high resolution structural and diffusion weighted magnetic resonance imaging techniques. Multiple network configurations were then constructed per subject, using random parcellations to define the nodes and using fibre tractography to determine the connectivity between the nodes. The networks were analysed with graph theoretical measures. Our results give reference maps of hub distribution measured with betweenness centrality and node degree. The loci of the hubs correspond with key areas from known overlapping cognitive networks. Several hubs were asymmetrically organized across hemispheres. Furthermore, females have hubs with higher betweenness centrality and males have hubs with higher node degree. Female networks have higher small-world indices

Network and cellular mechanisms underlying heterogeneous excitatory/inhibitory balanced states

Recent work has explored spatiotemporal relationships between excitatory (E) and inhibitory (I) signaling within neural networks, and the effect of these relationships on network activity patterns. Data from these studies have indicated that excitation and inhibition are maintained at a similar level across long time periods and that excitatory and inhibitory currents may be tightly synchronized. Disruption of this balance—leading to an aberrant E/I ratio—is implicated in various brain pathologies. However, a thorough characterization of the relationship between E and I currents in experimental settings is largely impossible, due to their tight regulation at multiple cellular and network levels. Here, we use biophysical neural network models to investigate the emergence and properties of balanced states by heterogeneous mechanisms. Our results show that a network can homeostatically regulate the E/I ratio through interactions among multiple cellular and network factors, including average firing rates, synaptic weights and average neural depolarization levels in excitatory/inhibitory populations. Complex and competing interactions between firing rates and depolarization levels allow these factors to alternately dominate network dynamics in different synaptic weight regimes. This leads to the emergence of distinct mechanisms responsible for determining a balanced state and its dynamical correlate. Our analysis provides a comprehensive picture of how E/I ratio changes when manipulating specific network properties, and identifies the mechanisms regulating E/I balance. These results provide a framework to explain the diverse, and in some cases, contradictory experimental observations on the E/I state in different brain states and conditions.Network can homeostatically regulate the E/I ratio and net post‐synaptic current through interactions among multiple cellular and network factors, including average firing rates, synaptic weights and average neural depolarization levels in excitatory/inhibitory neuronal populations, leading to the emergence of distinct mechanisms responsible for determining a balanced E/I state and its dynamical correlate. This in turn leads to the emergence of multiple balance states having different dynamical properties.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154922/1/ejn14669_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154922/2/ejn14669-sup-0001-Supinfo.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154922/3/ejn14669.pd

The malleability of uranium: manipulating the charge-density wave in epitaxial films

We report x-ray synchrotron experiments on epitaxial films of uranium, deposited on niobium and tungsten seed layers. Despite similar lattice parameters for these refractory metals, the uranium epitaxial arrangements are different and the strains propagated along the a-axis of the uranium layers are of opposite sign. At low temperatures these changes in epitaxy result in dramatic modifications to the behavior of the charge-density wave in uranium. The differences are explained with the current theory for the electron-phonon coupling in the uranium lattice. Our results emphasize the intriguing possibilities of producing epitaxial films of elements that have complex structures like the light actinides uranium to plutonium.Comment: 6 pages, 6 figure

Structural network heterogeneities and network dynamics: a possible dynamical mechanism for hippocampal memory reactivation

The hippocampus has the capacity for reactivating recently acquired memories [1-3] and it is hypothesized that one of the functions of sleep reactivation is the facilitation of consolidation of novel memory traces [4-11]. The dynamic and network processes underlying such a reactivation remain, however, unknown. We show that such a reactivation characterized by local, self-sustained activity of a network region may be an inherent property of the recurrent excitatory-inhibitory network with a heterogeneous structure. The entry into the reactivation phase is mediated through a physiologically feasible regulation of global excitability and external input sources, while the reactivated component of the network is formed through induced network heterogeneities during learning. We show that structural changes needed for robust reactivation of a given network region are well within known physiological parameters [12,13].Comment: 16 pages, 5 figure

Efficient Universal Computing Architectures for Decoding Neural Activity

The ability to decode neural activity into meaningful control signals for prosthetic devices is critical to the development of clinically useful brain– machine interfaces (BMIs). Such systems require input from tens to hundreds of brain-implanted recording electrodes in order to deliver robust and accurate performance; in serving that primary function they should also minimize power dissipation in order to avoid damaging neural tissue; and they should transmit data wirelessly in order to minimize the risk of infection associated with chronic, transcutaneous implants. Electronic architectures for brain– machine interfaces must therefore minimize size and power consumption, while maximizing the ability to compress data to be transmitted over limited-bandwidth wireless channels. Here we present a system of extremely low computational complexity, designed for real-time decoding of neural signals, and suited for highly scalable implantable systems. Our programmable architecture is an explicit implementation of a universal computing machine emulating the dynamics of a network of integrate-and-fire neurons; it requires no arithmetic operations except for counting, and decodes neural signals using only computationally inexpensive logic operations. The simplicity of this architecture does not compromise its ability to compress raw neural data by factors greater than . We describe a set of decoding algorithms based on this computational architecture, one designed to operate within an implanted system, minimizing its power consumption and data transmission bandwidth; and a complementary set of algorithms for learning, programming the decoder, and postprocessing the decoded output, designed to operate in an external, nonimplanted unit. The implementation of the implantable portion is estimated to require fewer than 5000 operations per second. A proof-of-concept, 32-channel field-programmable gate array (FPGA) implementation of this portion is consequently energy efficient. We validate the performance of our overall system by decoding electrophysiologic data from a behaving rodent.United States. National Institutes of Health (Grant NS056140

Local dynamics of gap-junction-coupled interneuron networks

Interneurons coupled by both electrical gap-junctions (GJs) and chemical GABAergic synapses are major components of forebrain networks. However, their contributions to the generation of specific activity patterns, and their overall contributions to network function, remain poorly understood. Here we demonstrate, using computational methods, that the topological properties of interneuron networks can elicit a wide range of activity dynamics, and either prevent or permit local pattern formation. We systematically varied the topology of GJ and inhibitory chemical synapses within simulated networks, by changing connection types from local to random, and changing the total number of connections. As previously observed we found that randomly coupled GJs lead to globally synchronous activity. In contrast, we found that local GJ connectivity may govern the formation of highly spatially heterogeneous activity states. These states are inherently temporally unstable when the input is uniformly random, but can rapidly stabilize when the network detects correlations or asymmetries in the inputs. We show a correspondence between this feature of network activity and experimental observations of transient stabilization of striatal fast-spiking interneurons (FSIs), in electrophysiological recordings from rats performing a simple decision-making task. We suggest that local GJ coupling enables an active search-and-select function of striatal FSIs, which contributes to the overall role of cortical-basal ganglia circuits in decision-making.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85426/1/ph10_1_016015.pd

An experimental study of the large deformation of plastic hinges

Recent applications of structural plasticity to areas such as vehicle crashworthiness has led to interest in the large deformation plastic collapse of general frames. Even when displacements are comparable to the original structural dimensions, the plasticity is confined to localized regions or "hinges". This paper reports an experimental study of the behavior of such hinges in thin walled structural members. Due to local deformation the load carrying capacity of the hinge significantly decreases at large rotations. In a companion paper [4] a structural constitutive theory is proposed to account for this behavior. Numerical data for this theory is obtained in the present paper. Finally test results are given for a large deformation combined loading test designed to validate the theory of [4]. The experimental results are in good agreement with the theoretical predictions.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/23017/1/0000586.pd

Profile of the U 5f magnetization in U/Fe multilayers

Recent calculations, concerning the magnetism of uranium in the U/Fe multilayer system have described the spatial dependence of the 5f polarization that might be expected. We have used the x-ray resonant magnetic reflectivity technique to obtain the profile of the induced uranium magnetic moment for selected U/Fe multilayer samples. This study extends the use of x-ray magnetic scattering for induced moment systems to the 5f actinide metals. The spatial dependence of the U magnetization shows that the predominant fraction of the polarization is present at the interfacial boundaries, decaying rapidly towards the center of the uranium layer, in good agreement with predictions.Comment: 7 pages, 6 figure