Responses of midbrain auditory neurons to two different environmental sounds-A new approach on cross-sound modeling.

When modeling auditory responses to environmental sounds, results are satisfactory if both training and testing are restricted to datasets of one type of sound. To predict 'cross-sound' responses (i.e., to predict the response to one type of sound e.g., rat Eating sound, after training with another type of sound e.g., rat Drinking sound), performance is typically poor. Here we implemented a novel approach to improve such cross-sound modeling (single unit datasets were collected at the auditory midbrain of anesthetized rats). The method had two key features: (a) population responses (e.g., average of 32 units) instead of responses of individual units were analyzed; and (b) the long sound segment was first divided into short segments (single sound-bouts), their similarity was then computed over a new metric involving the response (called Stimulus Response Model map or SRM map), and finally similar sound-bouts (regardless of sound type) and their associated responses (peri-stimulus time histograms, PSTHs) were modelled. Specifically, a committee machine model (artificial neural networks with 20 stratified spectral inputs) was trained with datasets from one sound type before predicting PSTH responses to another sound type. Model performance was markedly improved up to 92%. Results also suggested the involvement of different neural mechanisms in generating the early and late responses to amplitude transients in the broad-band environmental sounds. We concluded that it is possible to perform rather satisfactory cross-sound modeling on datasets grouped together based on their similarities in terms of the new metric of SRM map.

Hallmarks of Hunds coupling in the Mott insulator Ca2RuO4.

A paradigmatic case of multi-band Mott physics including spin-orbit and Hund's coupling is realized in Ca2RuO4. Progress in understanding the nature of this Mott insulating phase has been impeded by the lack of knowledge about the low-energy electronic structure. Here we provide-using angle-resolved photoemission electron spectroscopy-the band structure of the paramagnetic insulating phase of Ca2RuO4 and show how it features several distinct energy scales. Comparison to a simple analysis of atomic multiplets provides a quantitative estimate of the Hund's coupling J=0.4 eV. Furthermore, the experimental spectra are in good agreement with electronic structure calculations performed with Dynamical Mean-Field Theory. The crystal field stabilization of the dxy orbital due to c-axis contraction is shown to be essential to explain the insulating phase. These results underscore the importance of multi-band physics, Coulomb interaction and Hund's coupling that together generate the Mott insulating state of Ca2RuO4.

Non-Kondo-like electronic structure in the correlated rare-earth hexaboride YbB(6).

We present angle-resolved photoemission studies on the rare-earth-hexaboride YbB(6), which has recently been predicted to be a topological Kondo insulator. Our data do not agree with the prediction and instead show that YbB(6) exhibits a novel topological insulator state in the absence of a Kondo mechanism. We find that the Fermi level electronic structure of YbB(6) has three 2D Dirac cone like surface states enclosing the Kramers's points, while the f orbital that would be relevant for the Kondo mechanism is ∼1 eV below the Fermi level. Our first-principles calculation shows that the topological state that we observe in YbB(6) is due to an inversion between Yb d and B p bands. These experimental and theoretical results provide a new approach for realizing novel correlated topological insulator states in rare-earth materials.

Surface electronic structure of the topological Kondo-insulator candidate correlated electron system SmB6.

The Kondo insulator SmB6 has long been known to exhibit low-temperature transport anomalies whose origin is of great interest. Here we uniquely access the surface electronic structure of the anomalous transport regime by combining state-of-the-art laser and synchrotron-based angle-resolved photoemission techniques. We observe clear in-gap states (up to ~4 meV), whose temperature dependence is contingent on the Kondo gap formation. In addition, our observed in-gap Fermi surface oddness tied with the Kramers' point topology, their coexistence with the two-dimensional transport anomaly in the Kondo hybridization regime, as well as their robustness against thermal recycling, taken together, collectively provide strong evidence for protected surface metallicity with a Fermi surface whose topology is consistent with the theoretically predicted topological Fermi surface. Our observations of systematic surface electronic structure provide the fundamental electronic parameters for the anomalous Kondo ground state of correlated electron material SmB6.

Modeling complex responses of FM-sensitive cells in the auditory midbrain using a committee machine.

Frequency modulation (FM) is an important building block of complex sounds that include speech signals. Exploring the neural mechanisms of FM coding with computer modeling could help understand how speech sounds are processed in the brain. Here, we modeled the single unit responses of auditory neurons recorded from the midbrain of anesthetized rats. These neurons displayed spectral temporal receptive fields (STRFs) that had multiple-trigger features, and were more complex than those with single-trigger features. Their responses have not been modeled satisfactorily with simple artificial neural networks, unlike neurons with simple-trigger features. To improve model performance, here we tested an approach with the committee machine. For a given neuron, the peri-stimulus time histogram (PSTH) was first generated in response to a repeated random FM tone, and peaks in the PSTH were segregated into groups based on the similarity of their pre-spike FM trigger features. Each group was then modeled using an artificial neural network with simple architecture, and, when necessary, by increasing the number of neurons in the hidden layer. After initial training, the artificial neural networks with their optimized weighting coefficients were pooled into a committee machine for training. Finally, the model performance was tested by prediction of the response of the same cell to a novel FM tone. The results showed improvement over simple artificial neural networks, supporting that trigger-feature-based modeling can be extended to cells with complex responses. This article is part of a Special Issue entitled Neural Coding 2012. This article is part of a Special Issue entitled Neural Coding 2012.

Modeling frequency modulated responses of midbrain auditory neurons based on trigger features and artificial neural networks.

Frequency modulation (FM) is an important building block of communication signals for animals and human. Attempts to predict the response of central neurons to FM sounds have not been very successful, though achieving successful results could bring insights regarding the underlying neural mechanisms. Here we proposed a new method to predict responses of FM-sensitive neurons in the auditory midbrain. First we recorded single unit responses in anesthetized rats using a random FM tone to construct their spectro-temporal receptive fields (STRFs). Training of neurons in the artificial neural network to respond to a second random FM tone was based on the temporal information derived from the STRF. Specifically, the time window covered by the presumed trigger feature and its delay time to spike occurrence were used to train a finite impulse response neural network (FIRNN) to respond to this random FM. Finally we tested the model performance in predicting the response to another similar FM stimuli (third random FM tone). We found good performance in predicting the time of responses if not also the response magnitudes. Furthermore, the weighting function of the FIRNN showed temporal 'bumps' suggesting temporal integration of synaptic inputs from different frequency laminae. This article is part of a Special Issue entitled: Neural Coding.

Fine frequency-modulation trigger features of midbrain auditory neurons extracted by the progressive thresholding method--a preliminary study.

Spectro-temporal receptive fields (STRFs) are commonly used to characterize response properties of central auditory neurons and for visualizing 'trigger features'. However, trigger features in STRF maps typically have a blurry appearance. Therefore it is unclear what details could be embedded in them. To investigate this, we developed a new method called 'progressive thresholding' to resolve fine structures in the STRFs, and applied the method to FM responses recorded from single units at the auditory midbrain of anesthetized rats. Random FM tones of a narrow frequency range (approximately 0.5 octave) were first presented to evoked spike responses at the cell's best frequency. Perispike modulating time waveforms collected (50 msec long, n = 1500 to 4000 tracings) were used to generate STRF based on spike-triggered-averaging. After supra-threshold areas of pixel counts had been determined through a step of progressive thresholding in the map, those peri-spike modulating waveforms passing through each area were dejittered systematically. At what seemed to be an optimal threshold, multiple trigger features (up to a maximum of 4 fine bands) were extracted from the initially simple-looking STRF. Results show that fine FM trigger features are present in STRFs and that they can be resolved with the present method of analysis.

Should spikes be treated with equal weightings in the generation of spectro-temporal receptive fields?

Knowledge on the trigger features of central auditory neurons is important in the understanding of speech processing. Spectro-temporal receptive fields (STRFs) obtained using random stimuli and spike-triggered averaging allow visualization of trigger features which often appear blurry in the time-versus-frequency plot. For a clearer visualization we have previously developed a dejittering algorithm to sharpen trigger features in the STRF of FM-sensitive cells. Here we extended this algorithm to segregate spikes, based on their dejitter values, into two groups: normal and outlying, and to construct their STRF separately. We found that while the STRF of the normal jitter group resembled full trigger feature in the original STRF, those of the outlying jitter group resembled a different or partial trigger feature. This algorithm allowed the extraction of other weaker trigger features. Due to the presence of different trigger features in a given cell, we proposed that in the generation of STRF, the evoked spikes should not be treated indiscriminately with equal weightings.

Mechanisms and dynamics of mechanical strengthening in ligament-equivalent fibroblast-populated collagen matrices.

We have measured the dynamics of extracellular matrix consolidation and strengthening by human dermal fibroblasts in hydrated collagen gels. Constraining matrix consolidation between two porous polyethylene posts held rigidly apart set up the mechanical stress which led to the formation of uniaxially oriented fibroblast-populated collagen matrices with a histology resembling a ligament. We measured the mechanical stiffness and tensile strength of these ligament equivalents (LEs) as a function of age at biweekly intervals up to 12 weeks in culture using a mechanical spectrometer customized for performing experiments under physiologic conditions. The LE load-strain curve changed as a function of LE age, increasing in stiffness and exhibiting less plastic-like behavior. At 12 weeks, LEs had acquired up to 30 times the breaking strength of 1-week-old LEs. Matrix strengthening occurred primarily through the formation of BAPN-sensitive, lysyl oxidase catalyzed crosslinks. Sulfated glycosaminoglycan (GAG) content increased monotonically with LE age, reaching levels that are characteristic of ligaments. Cells in the LEs actively incorporated [3H]proline and [35S]sulfate into the extracellular matrix. Over the first three weeks, DNA content increased rapidly but thereafter remained constant. This data represent the first documentation of strengthening kinetics for cell-assembled biopolymer gels and the results suggest that this LE tissue may be a valuable model for studying the cellular processes responsible for tissue growth, repair, and remodeling.