Fluctuation Effects in High Sheet Resistance Superconducting Films

As the normal state sheet resistance, $R_n$, of a thin film superconductor increases, its superconducting properties degrade. For $R_n\simeq h/4e^2$ superconductivity disappears and a transition to a nonsuperconducting state occurs. We present electron tunneling and transport measurements on ultrathin, homogeneously disordered superconducting films in the vicinity of this transition. The data provide strong evidence that fluctuations in the amplitude of the superconducting order parameter dominate the tunneling density of states and the resistive transitions in this regime. We briefly discuss possible sources of these amplitude fluctuation effects. We also describe how the data suggest a novel picture of the superconductor to nonsuperconductor transition in homogeneous 2D systems.Comment: 11 pages, 5 figure

Proximity effect in granular superconductor-normal metal structures

We fabricated three-dimensional disordered Pb-Cu granular structures, with various metal compositions. The typical grain size of both metals is smaller than the superconductor and normal metal coherence lengths, thus satisfying the Cooper limit. The critical temperature of the samples was measured and compared with the critical temperature of bilayers. We show how the proximity effect theories, developed for bilayers, can be modified for random mixtures and we demonstrate that our experimental data fit well the de Gennes weak coupling limit theory in the Cooper limit. Our results indicate that, in granular structures, the Cooper limit can be satisfied over a wide range of concentrations.Comment: 15 pages, 4 figure

Coherent, mechanical control of a single electronic spin

The ability to control and manipulate spins via electrical, magnetic and optical means has generated numerous applications in metrology and quantum information science in recent years. A promising alternative method for spin manipulation is the use of mechanical motion, where the oscillation of a mechanical resonator can be magnetically coupled to a spins magnetic dipole, which could enable scalable quantum information architectures9 and sensitive nanoscale magnetometry. To date, however, only population control of spins has been realized via classical motion of a mechanical resonator. Here, we demonstrate coherent mechanical control of an individual spin under ambient conditions using the driven motion of a mechanical resonator that is magnetically coupled to the electronic spin of a single nitrogen-vacancy (NV) color center in diamond. Coherent control of this hybrid mechanical/spin system is achieved by synchronizing pulsed spin-addressing protocols (involving optical and radiofrequency fields) to the motion of the driven oscillator, which allows coherent mechanical manipulation of both the population and phase of the spin via motion-induced Zeeman shifts of the NV spins energy. We demonstrate applications of this coherent mechanical spin-control technique to sensitive nanoscale scanning magnetometry.Comment: 6 pages, 4 figure

Reconstructing Speech from Human Auditory Cortex

Direct brain recordings from neurosurgical patients listening to speech reveal that the acoustic speech signals can be reconstructed from neural activity in auditory cortex

A Generalized Linear Model for Estimating Spectrotemporal Receptive Fields from Responses to Natural Sounds

In the auditory system, the stimulus-response properties of single neurons are often described in terms of the spectrotemporal receptive field (STRF), a linear kernel relating the spectrogram of the sound stimulus to the instantaneous firing rate of the neuron. Several algorithms have been used to estimate STRFs from responses to natural stimuli; these algorithms differ in their functional models, cost functions, and regularization methods. Here, we characterize the stimulus-response function of auditory neurons using a generalized linear model (GLM). In this model, each cell's input is described by: 1) a stimulus filter (STRF); and 2) a post-spike filter, which captures dependencies on the neuron's spiking history. The output of the model is given by a series of spike trains rather than instantaneous firing rate, allowing the prediction of spike train responses to novel stimuli. We fit the model by maximum penalized likelihood to the spiking activity of zebra finch auditory midbrain neurons in response to conspecific vocalizations (songs) and modulation limited (ml) noise. We compare this model to normalized reverse correlation (NRC), the traditional method for STRF estimation, in terms of predictive power and the basic tuning properties of the estimated STRFs. We find that a GLM with a sparse prior predicts novel responses to both stimulus classes significantly better than NRC. Importantly, we find that STRFs from the two models derived from the same responses can differ substantially and that GLM STRFs are more consistent between stimulus classes than NRC STRFs. These results suggest that a GLM with a sparse prior provides a more accurate characterization of spectrotemporal tuning than does the NRC method when responses to complex sounds are studied in these neurons

Cortical Surround Interactions and Perceptual Salience via Natural Scene Statistics

Spatial context in images induces perceptual phenomena associated with salience and modulates the responses of neurons in primary visual cortex (V1). However, the computational and ecological principles underlying contextual effects are incompletely understood. We introduce a model of natural images that includes grouping and segmentation of neighboring features based on their joint statistics, and we interpret the firing rates of V1 neurons as performing optimal recognition in this model. We show that this leads to a substantial generalization of divisive normalization, a computation that is ubiquitous in many neural areas and systems. A main novelty in our model is that the influence of the context on a target stimulus is determined by their degree of statistical dependence. We optimized the parameters of the model on natural image patches, and then simulated neural and perceptual responses on stimuli used in classical experiments. The model reproduces some rich and complex response patterns observed in V1, such as the contrast dependence, orientation tuning and spatial asymmetry of surround suppression, while also allowing for surround facilitation under conditions of weak stimulation. It also mimics the perceptual salience produced by simple displays, and leads to readily testable predictions. Our results provide a principled account of orientation-based contextual modulation in early vision and its sensitivity to the homogeneity and spatial arrangement of inputs, and lends statistical support to the theory that V1 computes visual salience