Search

Variation in an animal's behavioral state is linked to fluctuations in brain activity and cognitive ability. In the neocortex, state-dependent circuit dynamics may reflect neuromodulatory influences such as that of acetylcholine (ACh). Although early literature suggested that ACh exerts broad, homogeneous control over cortical function, recent evidence indicates potential anatomical and functional segregation of cholinergic signaling. In addition, it is unclear whether states as defined by different behavioral markers reflect heterogeneous cholinergic and cortical network activity. Here, we perform simultaneous, dual-color mesoscopic imaging of both ACh and calcium across the neocortex of awake mice to investigate their relationships with behavioral variables. We find that higher arousal, categorized by different motor behaviors, is associated with spatiotemporally dynamic patterns of cholinergic modulation and enhanced large-scale network correlations. Overall, our findings demonstrate that ACh provides a highly dynamic and spatially heterogeneous signal that links fluctuations in behavior to functional reorganization of cortical networks.

Clinical and theoretical considerations presume that patients with different personality disorder (PD) clusters will be associated with distinct alliance rupture profiles; however, there is scarce empirical literature examining this. The present study adopted a systematic framework for investigating profiles of alliance ruptures for individuals belonging to each of the three PD clusters.The sample consisted of 94 patients from a randomized controlled trial for treatment of depression. PD cluster features were assessed at intake and ruptures were assessed across treatment. Three sets of multilevel analyses were conducted to test differences between the PD clusters in the general tendency to show a rupture profile, rupture development throughout the treatment, and timing of predicting ruptures by PD within sessions.The three clusters were associated with distinct profiles of alliance ruptures. Clusters A and B were characterized by a general tendency to show more withdrawal and confrontation ruptures. Cluster A had a greater decrease in confrontation ruptures over the course of treatment, while cluster B had a greater decrease in withdrawal ruptures. Cluster C was characterized by a general tendency to show fewer withdrawal and confrontation ruptures, with a greater increase in both ruptures over the course of treatment. For withdrawal ruptures, the differences between clusters were evident from the beginning of sessions, whereas for confrontation ruptures, there was less of a difference between beginning and end of sessions.The distinct profiles of alliance ruptures for each PD cluster may contribute to progress towards tailoring treatment to individuals with PDs.

Tuft dendrites of layer 5 pyramidal neurons form specialized compartments important for motor learning and performance, yet their computational capabilities remain unclear. Structural-functional mapping of the tuft tree from the motor cortex during motor tasks revealed two morphologically distinct populations of layer 5 pyramidal tract neurons (PTNs) that exhibit specific tuft computational properties. Early bifurcating and large nexus PTNs showed marked tuft functional compartmentalization, representing different motor variable combinations within and between their two tuft hemi-trees. By contrast, late bifurcating and smaller nexus PTNs showed synchronous tuft activation. Dendritic structure and dynamic recruitment of the N -methyl- d -aspartate (NMDA)–spiking mechanism explained the differential compartmentalization patterns. Our findings support a morphologically dependent framework for motor computations, in which independent amplification units can be combinatorically recruited to represent different motor sequences within the same tree.

Mobile devices are widely used around the world, frequently by people speaking local languages or dialects that are not well documented. For these languages, it might not be beneficial for commercial companies to develop Automatic Speech Recognition (ASR) systems, so users of these languages cannot utilize voice activation features (often using Keyword Spotting, KWS) of their devices. Standard KWS methods aim to statistically model the generation process of the speech signal, requiring hours of recorded and transcribed speech for training, and therefore are not adequate for limited-data scenarios. In this paper we propose a new KWS method, suitable for limited-data scenarios, which can be easily applied by developers. The proposed method uses a new histogram representation for words, obtained with respect to a pre-trained Gaussian Mixture Model (GMM). Sentences are represented by fixed-length global feature vectors, extracted from the response curves obtained by a word classifier. Word and sentence classifiers are trained using a discriminative approach, which is typically robust to training-set size. The dataset for training the GMM is easy to obtain, since no annotation is required. We compared the proposed system to a Hidden Markov Model (HMM) based system, trained using the same low data-resources conditions as ours, and to a state-of-the-art ASR system, trained using either the limited data scenario, or using many hours of recorded speech. In the limited data situation, our system performs better then both benchmarks in all experiments except for clean speech of children (CSLU dataset), where it performs as good as the HMM. Since the ASR benchmark performs poorly without enough training data, we also trained it without limiting the available data. In this case the ASR benchmark performs better when tested on speech of adults (TED-LIUM dataset of TED lectures) for all noise conditions, and our system performs better when tested on speech of children with low to moderate SNR values. The results demonstrate the advantages of the proposed system, and the conditions under which it performs better.

Adaptive movements are critical for animal survival. To guide future actions, the brain monitors various outcomes, including achievement of movement and appetitive goals. The nature of these outcome signals and their neuronal and network realization in the motor cortex (M1), which directs skilled movements, is largely unknown. Using a dexterity task, calcium imaging, optogenetic perturbations, and behavioral manipulations, we studied outcome signals in the murine forelimb M1. We found two populations of layer 2-3 neurons, termed success- and failure-related neurons, that develop with training, and report end results of trials. In these neurons, prolonged responses were recorded after success or failure trials independent of reward and kinematics. In addition, the initial state of layer 5 pyramidal tract neurons contained a memory trace of the previous trial's outcome. Intertrial cortical activity was needed to learn new task requirements. These M1 layer-specific performance outcome signals may support reinforcement motor learning of skilled behavior.

There has been tremendous interest in piezoelectricity at the nanoscale, for example in nanowires and nanofibers where piezoelectric properties may be enhanced or controllably tuned, thus necessitating robust characterization techniques of piezoelectric response in nanomaterials. Piezo-response force microscopy (PFM) is a well-established scanning probe technique routinely used to image piezoelectric/ferroelectric domains in thin films, however, its applicability to nanoscale objects is limited due to the requirement for physical contact with an atomic force microscope (AFM) tip that may cause dislocation or damage, particularly to soft materials, during scanning. Here we report a non-destructive PFM (ND-PFM) technique wherein the tip is oscillated into “discontinuous” contact during scanning, while applying an AC bias between tip and sample and extracting the piezoelectric response for each contact point by monitoring the resulting localized deformation at the AC frequency. ND-PFM is successfully applied to soft polymeric (poly-L-lactic acid) nanowires, as well as hard ceramic (barium zirconate titanate–barium calcium titanate) nanowires, both previously inaccessible by conventional PFM. Our ND-PFM technique is versatile and compatible with commercial AFMs, and can be used to correlate piezoelectric properties of nanomaterials with their microstructural features thus overcoming key characterisation challenges in the field.

The authors report the observation of a cavity mode, at λ = 0.94 µm, which is tightly confined between two slabs of two-dimensional photonic crystals deeply etched into a laser-like heterostructure waveguide. Guided photoluminescence of this heterostructure is used as an internal point source to probe the horizontal cavity.

The goal of voice conversion is to modify a source speaker's speech to sound as if spoken by a target speaker. Common conversion methods are based on Gaussian mixture modeling (GMM). They aim to statistically model the spectral structure of the source and target signals and require relatively large training sets (typically dozens of sentences) to avoid over-fitting. Moreover, they often lead to muffled synthesized output signals, due to excessive smoothing of the spectral envelopes. Mobile applications are characterized with low resources in terms of training data, memory footprint, and computational complexity. As technology advances, computational and memory requirements become less limiting; however, the amount of available training data still presents a great challenge, as a typical mobile user is willing to record himself saying just few sentences. In this paper, we propose the grid-based (GB) conversion method for such low resource environments, which is successfully trained using very few sentences (5---10). The GB approach is based on sequential Bayesian tracking, by which the conversion process is expressed as a sequential estimation problem of tracking the target spectrum based on the observed source spectrum. The converted Mel frequency cepstrum coefficient (MFCC) vectors are sequentially evaluated using a weighted sum of the target training vectors used as grid points. The training process includes simple computations of Euclidian distances between the training vectors and is easily performed even in cases of very small training sets. We use global variance (GV) enhancement to improve the perceived quality of the synthesized signals obtained by the proposed and the GMM-based methods. Using just 10 training sentences, our enhanced GB method leads to converted sentences having closer GV values to those of the target and to lower spectral distances at the same time, compared to enhanced version of the GMM-based conversion method. Furthermore, subjective evaluations show that signals produced by the enhanced GB method are perceived as more similar to the target speaker than the enhanced GMM signals, at the expense of a small degradation in the perceived quality.