Pyramidal neurons of the neocortex display a wide range of synchronous EEG rhythms, which arise from electric activity along the apical dendrites of neocortical pyramidal neurons. Here we present a theoretical description of oscillation frequency profiles along apical dendrites which exhibit resonance frequencies in the range of 10 to 100 Hz. The apical dendrite is modeled as a leaky coaxial cable coated with a dielectric, in which a series of compartments act as coupled electric circuits that gradually narrow the resonance profile. The tuning of the peak frequency is assumed to be controlled by the average amplitude of voltage-gated outward currents, which in turn are regulated by the subthreshold noise in the thousands of synaptic spines that are continuously bombarded by local circuits. The results of simulations confirmed the ability of the model both to tune the peak frequency in the 10–100 Hz range and to gradually narrow the resonance profile. Considerable additional narrowing of the resonance profile is provided by repeated looping through the apical dendrite via the corticothalamocortical circuit, which reduced the width of each resonance curve (at half-maximum) to approximately 1 Hz. Synaptic noise in the neural circuit is discussed in relation to the ways it can influence the narrowing process.

This volume engages place through the movement of objects, the evocation of senses, desires, and memories and the on-going project of articulating the parameters of place and location.

We report the first high-resolution spectra for the out-of-plane librational vibration in the water dimer. Three vibrational subbands comprising a total of 188 transitions have been measured by diode laser spectroscopy near 500 cm(-1) and assigned to (H2O)2 libration-rotation-tunneling eigenstates. The band origin for the Ka = 1 subband is ~524 cm(-1). Librational excitation increases the interchange and bifurcation hydrogen bond rearrangement tunneling splittings by factors of 3-5 and 4-40, respectively. Analysis of the rotational constants obtained from a nonlinear least squares fit indicates that additional external perturbations to the energy levels are likely.

Does neuroscience have the potential to inform education? In this debate, three participants will describe emerging results in neuroscience with translatable links to learning and memory. Experts in learning sciences will discuss connections as well as limits in the ways that neuroscience can inform education at the individual and classroom levels. Although significant progress has been made in our understanding of how the brain learns and remembers, the question for this symposium is whether this progress does or can provide a direct bridge from brain science to education.

This is a summary of the activities of the working group on cosmology at WHEPP-X. The three main problems that were discussed at some length by the group during the course of the workshop were (i) canceling a 'large' cosmological constant, (ii) non-Gaussianities in inflationary models and (iii) stability of interacting models of dark energy and dark matter. We have briefly outlined these problems and have indicated the progress made.

This is the report of the neutrino physics working group at WHEPP-X. We summarize the problems selected and discussed at the workshop and the papers which have resulted subsequently.

Continuous imaging of cardiac functions is highly desirable for the assessment of long-term cardiovascular health, detection of acute cardiac dysfunction and clinical management of critically ill or surgical patients^1-4. However, conventional non-invasive approaches to image the cardiac function cannot provide continuous measurements owing to device bulkiness^5-11, and existing wearable cardiac devices can only capture signals on the skin^12-16. Here we report a wearable ultrasonic device for continuous, real-time and direct cardiac function assessment. We introduce innovations in device design and material fabrication that improve the mechanical coupling between the device and human skin, allowing the left ventricle to be examined from different views during motion. We also develop a deep learning model that automatically extracts the left ventricular volume from the continuous image recording, yielding waveforms of key cardiac performance indices such as stroke volume, cardiac output and ejection fraction. This technology enables dynamic wearable monitoring of cardiac performance with substantially improved accuracy in various environments.