Retinal Coding of Visual Scenes— Repetitive and Redundant Too?

Visual information reaches the brain by way of a fine cable, the optic nerve. The million or so axons in the optic nerve represent an information bottleneck in the visual pathway—where the fewest number of neurons convey the visual scene. It has long been thought that to make the most of the optic nerve’s limited capacity the retina may encode visual information in an optimally efficient manner. In this issue of Neuron, Puchalla et al. report a test of this hypothesis using multielectrode recordings from retinal ganglion cells stimulated with movies of natural scenes. The authors find substantial redundancy in the retinal code and estimate that there is an ∼10-fold overrepresentation of visual information

Theory of resonance energy transfer involving nanocrystals: the role of high multipoles

A theory for the fluorescence resonance energy transfer (FRET) between a pair of semiconducting nanocrystal quantum dots is developed. Two types of donor-acceptor couplings for the FRET rate are described: dipole-dipole (d-d) and the dipole-quadrupole (d-q) coupling. The theory builds on a simple effective mass model which is used to relate the FRET rate to measureable quantities such as the nanocrystal size, fundamental gap, effective mass, exciton radius and dielectric constant. We discuss the relative contribution to the FRET rate of the different multipole terms, the role of strong to weak confinement limits, and the effects of nanocrystal siz-es.Comment: 12 pages, 7 figure

Dissipationless Spin Current in Anisotropic p-Doped Semiconductors

Recently, dissipationless spin current has been predicted for the p-doped semiconductors with spin-orbit coupling. Here we investigate the effect of spherical symmetry breaking on the dissipationless spin current, and obtain values of the intrinsic spin Hall conductivity for realistic semiconductor band structures with cubic symmetry

Spectral Learning of Binomial HMMs for DNA Methylation Data

We consider learning parameters of Binomial Hidden Markov Models, which may be used to model DNA methylation data. The standard algorithm for the problem is EM, which is computationally expensive for sequences of the scale of the mammalian genome. Recently developed spectral algorithms can learn parameters of latent variable models via tensor decomposition, and are highly efficient for large data. However, these methods have only been applied to categorial HMMs, and the main challenge is how to extend them to Binomial HMMs while still retaining computational efficiency. We address this challenge by introducing a new feature-map based approach that exploits specific properties of Binomial HMMs. We provide theoretical performance guarantees for our algorithm and evaluate it on real DNA methylation data

Phase diagram for unzipping DNA with long-range interactions

We present a critique and extension of the mean-field approach to the mechanical pulling transition in bound polymer systems. Our model is motivated by the theoretically and experimentally important examples of adsorbed polymers and double-stranded DNA, and we focus on the case in which quenched disorder in the sequence of monomers is unimportant for the statistical mechanics. We show how including excluded volume interactions in the model affects the phase diagram for the critical pulling force, and we predict a re-entrancy phase at low temperatures which has not been previously discussed. We also consider the case of non-equilibrium pulling, in which the external force probes the local, rather than the global structure of the dsDNA or adsorbed polymer. The dynamics of the pulling transition in such experiments could illuminate the polymer's loop structure, which depends on the nature of excluded volume interactions.Comment: 4 pages, 2 figures; this version clarifies Eq. 8, and corrects errors in Fig.

Direct measurement of spatial Wigner function with area-integrated detection

We demonstrate experimentally a novel technique for characterizing transverse spatial coherence using the Wigner distribution function. The presented method is based on measuring interference between a pair of rotated and displaced replicas of the input beam with an area-integrating detector, and it can be superior in regimes when array detectors are not available. We analyze the quantum optical picture of the presented measurement for single-photon signals and discuss possible applications in quantum information processing.Comment: 3 pages, REVTe

Phase-based measures of cross-frequency coupling in brain electrical dynamics under general anesthesia

The state of general anesthesia (GA) is associated with an increase in spectral power in scalp electroencephalogram (EEG) at frequencies below 40 Hz, including spectral peaks in the slow oscillation (SO, 0.1-1 Hz) and α (8-14 Hz) bands. Because conventional power spectral analyses are insensitive to possible cross-frequency coupling, the relationships among the oscillations at different frequencies remain largely unexplored. Quantifying such coupling is essential for improving clinical monitoring of anesthesia and understanding the neuroscience of this brain state. We tested the usefulness of two measures of cross-frequency coupling: the bispectrum-derived SynchFastSlow, which is sensitive to phase-phase coupling in different frequency bands, and modulogram analysis of coupling between SO phase and α rhythm amplitude. SynchFastSlow, a metric that is used in clinical depth-of-anesthesia monitors, showed a robust correlation with the loss of consciousness at the induction of propofol GA, but this could be largely explained by power spectral changes without considering cross-frequency coupling. Modulogram analysis revealed two distinct modes of cross-frequency coupling under GA. The waking and two distinct states under GA could be discriminated by projecting in a two-dimensional phase space defined by the SynchFastSlow and the preferred SO phase of α activity. Our results show that a stereotyped pattern of phase-amplitude coupling accompanies multiple stages of anesthetic-induced unconsciousness. These findings suggest that modulogram analysis can improve EEG based monitoring of brain state under GA.National Institutes of Health (U.S.) (Grant DP2-OD006454)National Institutes of Health (U.S.) (Grant K25-NS057580)National Institutes of Health (U.S.) (Grant DP1-OD003646)National Institutes of Health (U.S.) (Grant R01-EB006385)National Institutes of Health (U.S.) (Grant R01-MH071847

Robust time-varying multivariate coherence estimation: Application to electroencephalogram recordings during general anesthesia

Coherence analysis characterizes frequency-dependent covariance between signals, and is useful for multivariate oscillatory data often encountered in neuroscience. The global coherence provides a summary of coherent behavior in high-dimensional multivariate data by quantifying the concentration of variance in the first mode of an eigenvalue decomposition of the cross-spectral matrix. Practical application of this useful method is sensitive to noise, and can confound coherent activity in disparate neural populations or spatial locations that have a similar frequency structure. In this paper we describe two methodological enhancements to the global coherence procedure that increase robustness of the technique to noise, and that allow characterization of how power within specific coherent modes change through time.National Institutes of Health (U.S.) (Grant DP2-OD006454)National Institutes of Health (U.S.) (Grant K25-NS057580)National Institutes of Health (U.S.) (Grant DP1-OD003646)National Institutes of Health (U.S.) (Grant R01-EB006385)National Institutes of Health (U.S.) (Grant R01-MH071847

A transient cortical state with sleep-like sensory responses precedes emergence from general anesthesia in humans

During awake consciousness, the brain intrinsically maintains a dynamical state in which it can coordinate complex responses to sensory input. How the brain reaches this state spontaneously is not known. General anesthesia provides a unique opportunity to examine how the human brain recovers its functional capabilities after profound unconsciousness. We used intracranial electrocorticography and scalp EEG in humans to track neural dynamics during emergence from propofol general anesthesia. We identify a distinct transient brain state that occurs immediately prior to recovery of behavioral responsiveness. This state is characterized by large, spatially distributed, slow sensory-evoked potentials that resemble the K-complexes that are hallmarks of stage two sleep. However, the ongoing spontaneous dynamics in this transitional state differ from sleep. These results identify an asymmetry in the neurophysiology of induction and emergence, as the emerging brain can enter a state with a sleep-like sensory blockade before regaining responsivity to arousing stimuli.National Institutes of Health (U.S.) (Grant K99-MH111748)National Institutes of Health (U.S.) (Grant R00-NS080911)National Institutes of Health (U.S.) (Grant DP2-OD006454)National Institutes of Health (U.S.) (Grant S10-RR023401)National Institutes of Health (U.S.) (Grant R01- NS062092)National Institutes of Health (U.S.) (Grant R01AG056015)National Institutes of Health (U.S.) (Grant P01GM118269)National Institutes of Health (U.S.) (Grant R01-EB009282

Bayesian analysis of trinomial data in behavioral experiments and its application to human studies of general anesthesia

Accurate quantification of loss of response to external stimuli is essential for understanding the mechanisms of loss of consciousness under general anesthesia. We present a new approach for quantifying three possible outcomes that are encountered in behavioral experiments during general anesthesia: correct responses, incorrect responses and no response. We use a state-space model with two state variables representing a probability of response and a conditional probability of correct response. We show applications of this approach to an example of responses to auditory stimuli at varying levels of propofol anesthesia ranging from light sedation to deep anesthesia in human subjects. The posterior probability densities of model parameters and the response probability are computed within a Bayesian framework using Markov Chain Monte Carlo methods.National Institutes of Health (U.S.) (Grant DP2-OD006454)National Institutes of Health (U.S.) (Grant K25-NS057580)National Institutes of Health (U.S.) (Grant DP1-OD003646)National Institutes of Health (U.S.) (Grant R01-EB006385)National Institutes of Health (U.S.) (Grant R01-MH071847