Kazushi Ikeda

Actin Migration Driven by Directional Assembly and Disassembly of Membrane-Anchored Actin Filaments

Actin and actin-associated proteins migrate within various cell types. To uncover the mechanism of their migration, we analyzed actin waves, which translocate actin and actin-associated proteins along neuronal axons toward the growth cones. We found that arrays of actin filaments constituting waves undergo directional assembly and disassembly, with their polymerizing ends oriented toward the axonal tip, and that the lateral side of the filaments is mechanically anchored to the adhesive substrate. A combination of live-cell imaging, molecular manipulation, force measurement, and mathematical modeling revealed that wave migration is driven by directional assembly and disassembly of actin filaments and their anchorage to the substrate. Actin-associated proteins co-migrate with actin filaments by interacting with them. Furthermore, blocking this migration, by creating an adhesion-free gap along the axon, disrupts axonal protrusion. Our findings identify a molecular mechanism that translocates actin and associated proteins toward the cells leading edge, thereby promoting directional cell motility.

Shootin1–cortactin interaction mediates signal–force transduction for axon outgrowth

The shootin1–cortactin interaction participates in netrin-1–induced F-actin–adhesion coupling and in the promotion of traction forces for axon outgrowth.

Exponential Family Tensor Factorization for Missing-Values Prediction and Anomaly Detection

In this paper, we study probabilistic modeling of heterogeneously attributed multi-dimensional arrays. The model can manage the heterogeneity by employing an individual exponential-family distribution for each attribute of the tensor array. These entries are connected by latent variables and are shared information across the different attributes. Because a Bayesian inference for our model is intractable, we cast the EM algorithm approximated by using the Lap lace method and Gaussian process. This approximation enables us to derive a predictive distribution for missing values in a consistent manner. Simulation experiments show that our method outperforms other methods such as PARAFAC and Tucker decomposition in missing-values prediction for cross-national statistics and is also applicable to discover anomalies in heterogeneous office-logging data.

Heart rate variability predicts the emotional state in dogs

Although it is known that heart rate variability (HRV) is a useful indicator of emotional states in animals, there are few reports of research in dogs. Thus, we investigated the relationship between HRV and emotional states in dogs. The electrocardiogram and behavior in two situations that elicited a positive and negative emotion, in addition to baseline (when dogs were not presented any social stimuli), were recorded in 33 healthy house dogs. After testing, we chose 15seconds from each situation and baseline and calculated three HRV parameters: standard deviation of normal-to-normal R-R intervals (SDNN), the root mean square of successive heartbeat interval differences (RMSSD), and mean R-R intervals (mean RRI). In comparing these parameters with baseline, only SDNN was lower in a positive situation. In contrast, only RMSSD was lower in a negative situation. A change in HRV occurred with a stimulus eliciting emotion, and was able to distinguish between positive and negative situations. Thus, HRV is useful for estimating the emotional state in dogs.

Discharge-Rate Persistence of Baseline Activity During Fixation Reflects Maintenance of Memory-Period Activity in the Macaque Posterior Parietal Cortex

Recent evidence has demonstrated that spatiotemporal patterns of spontaneous activity reflect the patterns of activity evoked by sensory stimuli. However, few studies have examined whether response profiles of task-evoked activity, which is not related to external sensory stimuli but rather to internal processes, are also reflected in those of spontaneous activity. To address this, we recorded activity of neurons in the lateral intraparietal area (LIP) when monkeys performed reaction-time and delayed-response visual-search tasks. We particularly focused on the target location-dependent modulation of delay-period activity (delay-period modulation) in the delayed-response task, and the discharge-rate persistency in fixation-period activity (baseline-activity maintenance) in the reaction-time task. Baseline-activity maintenance was assessed by the correlation between the spike counts of 2 separate bins. We found that baseline-activity maintenance, calculated from bins separated by a long interval (200-500 ms), was correlated with delay-period modulation, whereas that calculated from bins separated by a short interval (~100 ms) was correlated with trial-to-trial fluctuations in baseline activity, suggesting a link between the capability to hold task-related information in delay-period activity and the degree of baseline-activity maintenance in a timescale-dependent manner.

Neural decoding of single vowels during covert articulation using electrocorticography

The human brain has important abilities for manipulating phonemes, the basic building blocks of speech; these abilities represent phonological processing. Previous studies have shown change in the activation levels of broad cortical areas such as the premotor cortex, the inferior frontal gyrus, and the superior temporal gyrus during phonological processing. However, whether these areas actually convey signals to representations related to individual phonemes remains unclear. This study focused on single vowels and investigated cortical areas important for representing single vowels using electrocorticography (ECoG) during covert articulation. To identify such cortical areas, we used a neural decoding approach in which machine learning models identify vowels. A decoding model was trained on the ECoG signals from individual electrodes placed on the subjects cortices. We then statistically evaluated whether each decoding model showed accurate identification of vowels, and we found cortical areas such as the premotor cortex and the superior temporal gyrus. These cortical areas were consistent with previous findings. On the other hand, no electrodes over Brocas area showed significant decoding accuracies. This was inconsistent with findings from a previous study showing that vowels within the phonemic sequence of words can be decoded using ECoG signals from Brocas area. Our results therefore suggest that Brocas area is involved in the processing of vowels within phonemic sequences, but not in the processing of single vowels.

Cloth dynamics modeling in latent spaces and its application to robotic clothing assistance

Real-time estimation of human-cloth relationship is crucial for efficient learning of motor skills in robotic clothing assistance. However, cloth state estimation using a depth sensor is a challenging problem with inherent ambiguity. To address this problem, we propose the offline learning of a cloth dynamics model by incorporating reliable motion capture data and applying this model for the online tracking of human-cloth relationship using a depth sensor. In this study, we evaluate the performance of using a shared Gaussian Process Latent Variable Model in learning the dynamics of clothing articles. The experimental results demonstrate the effectiveness of shared GP-LVM in capturing cloth dynamics using few data samples and the ability to generalize to unseen settings. We further demonstrate three key factors that affect the predictive performance of the trained dynamics model.

Art expertise reduces influence of visual salience on fixation in viewing abstract-paintings.

When viewing a painting, artists perceive more information from the painting on the basis of their experience and knowledge than art novices do. This difference can be reflected in eye scan paths during viewing of paintings. Distributions of scan paths of artists are different from those of novices even when the paintings contain no figurative object (i.e. abstract paintings). There are two possible explanations for this difference of scan paths. One is that artists have high sensitivity to high-level features such as textures and composition of colors and therefore their fixations are more driven by such features compared with novices. The other is that fixations of artists are more attracted by salient features than those of novices and the fixations are driven by low-level features. To test these, we measured eye fixations of artists and novices during the free viewing of various abstract paintings and compared the distribution of their fixations for each painting with a topological attentional map that quantifies the conspicuity of low-level features in the painting (i.e. saliency map). We found that the fixation distribution of artists was more distinguishable from the saliency map than that of novices. This difference indicates that fixations of artists are less driven by low-level features than those of novices. Our result suggests that artists may extract visual information from paintings based on high-level features. This ability of artists may be associated with artists’ deep aesthetic appreciation of paintings.

Estimation of Driving Phase by Modeling Brake Pressure Signals

It is important for a driver-assist system to know the phase of the driver, that is, safety or danger. This paper proposes two methods for estimating the drivers phase by applying machine learning techniques to the sequences of brake signals. One method models the signal set with a mixture of Gaussians, where a Gaussian corresponds to a phase. The other method classifies a segment of the brake sequence to one of the hidden Markov models, each of which represents a phase. These methods are validated with experimental data, and are shown to be consistent with each other for the collected data from an unconstrained drive.

Adaptive Markov chain Monte Carlo for auxiliary variable method and its application to parallel tempering

Auxiliary variable methods such as the Parallel Tempering and the cluster Monte Carlo methods generate samples that follow a target distribution by using proposal and auxiliary distributions. In sampling from complex distributions, these algorithms are highly more efficient than the standard Markov chain Monte Carlo methods. However, their performance strongly depends on their parameters and determining the parameters is critical. In this paper, we proposed an algorithm for adapting the parameters during drawing samples and proved the convergence theorem of the adaptive algorithm. We applied our algorithm to the Parallel Tempering. That is, we developed an adaptive Parallel Tempering that tunes the parameters on the fly. We confirmed the effectiveness of our algorithm through the validation of the adaptive Parallel Tempering, comparing samples from the target distribution by the adaptive Parallel Tempering and samples by conventional algorithms.