Probabilistic Estimation and Control of Dynamical Systems Using Particle Filter with Adaptive Backward Sampling.

Estimating and controlling dynamical systems from observable time-series data are essential for understanding and manipulating nonlinear dynamics. This paper proposes a probabilistic framework for simultaneously estimating and controlling nonlinear dynamics under noisy observation conditions. Our proposed method utilizes the particle filter not only as a state estimator and a prior estimator for the dynamics but also as a controller.

Super-resolution of X-ray CT images of rock samples by sparse representation: applications to the complex texture of serpentinite.

X-ray computed tomography (X-ray CT) has been widely used in the earth sciences, as it is non-destructive method for providing us the three-dimensional structures of rocks and sediments. Rock samples essentially possess various-scale structures, including millimeters to centimeter scales of layering and veins to micron-meter-scale mineral grains and porosities. As the limitations of the X-ray CT scanner, sample size and scanning time, it is not easy to extract information on multi-scale structures, even when hundreds meter scale core samples were obtained during drilling projects.

Estimating Distributions of Parameters in Nonlinear State Space Models with Replica Exchange Particle Marginal Metropolis-Hastings Method.

Extracting latent nonlinear dynamics from observed time-series data is important for understanding a dynamic system against the background of the observed data. A state space model is a probabilistic graphical model for time-series data, which describes the probabilistic dependence between latent variables at subsequent times and between latent variables and observations. Since, in many situations, the values of the parameters in the state space model are unknown, estimating the parameters from observations is an important task.

Data-Driven Analysis of Nonlinear Heterogeneous Reactions through Sparse Modeling and Bayesian Statistical Approaches.

Heterogeneous reactions are chemical reactions that occur at the interfaces of multiple phases, and often show a nonlinear dynamical behavior due to the effect of the time-variant surface area with complex reaction mechanisms. It is important to specify the kinetics of heterogeneous reactions in order to elucidate the microscopic elementary processes and predict the macroscopic future evolution of the system. In this study, we propose a data-driven method based on a sparse modeling algorithm and sequential Monte Carlo algorithm for simultaneously extracting substantial reaction terms and surface models from a number of candidates by using partial observation data.

Estimation of neuronal dynamics based on sparse modeling.

Elucidating neural dynamics is one of the important subjects in neuroscience. To elucidate nonlinear dynamics of single neurons, it is important to extract nonlinear membrane currents from many types of membrane current candidates. In this study, we propose a sparse modeling method for estimating a conductance-based neuron model from observed data, by extracting necessary membrane currents from multiple candidates.

Microglia Enhance Synapse Activity to Promote Local Network Synchronization.

Microglia are highly motile immunoreactive cells that play integral roles in the response to brain infection and damage, and in the progression of various neurological diseases. During development, microglia also help sculpt neural circuits, via both promoting synapse formation and by targeting specific synapses for elimination and phagocytosis. Microglia are also active surveyors of neural circuits in the mature, healthy brain, although the functional consequences of such microglia-neuron contacts under these conditions is unclear.

Putative Neural Network Within an Olfactory Sensory Unit for Nestmate and Non-nestmate Discrimination in the Japanese Carpenter Ant: The Ultra-structures and Mathematical Simulation.

Ants are known to use a colony-specific blend of cuticular hydrocarbons (CHCs) as a pheromone to discriminate between nestmates and non-nestmates and the CHCs were sensed in the basiconic type of antennal sensilla (). To investigate the functional design of this type of antennal sensilla, we observed the ultra-structures at 2D and 3D in the Japanese carpenter ant, , using a serial block-face scanning electron microscope (SBF-SEM), and conventional and high-voltage transmission electron microscopes. Based on the serial images of 352 cross sections of SBF-SEM, we reconstructed a 3D model of the sensillum revealing that each houses > 100 unbranched dendritic processes, which extend from the same number of olfactory receptor neurons (ORNs).

Bayesian inversion analysis of nonlinear dynamics in surface heterogeneous reactions.

It is essential to extract nonlinear dynamics from time-series data as an inverse problem in natural sciences. We propose a Bayesian statistical framework for extracting nonlinear dynamics of surface heterogeneous reactions from sparse and noisy observable data. Surface heterogeneous reactions are chemical reactions with conjugation of multiple phases, and they have the intrinsic nonlinearity of their dynamics caused by the effect of surface-area between different phases.

Free-energy landscape and nucleation pathway of polymorphic minerals from solution in a Potts lattice-gas model.

Metastable minerals commonly form during reactions between water and rock. The nucleation mechanism of polymorphic phases from solution are explored here using a two-dimensional Potts model. The model system is composed of a solvent and three polymorphic solid phases.

Higher-order spike triggered analysis of neural oscillators.

For the purpose of elucidating the neural coding process based on the neural excitability mechanism, researchers have recently investigated the relationship between neural dynamics and the spike triggered stimulus ensemble (STE). Ermentrout et al. analytically derived the relational equation between the phase response curve (PRC) and the spike triggered average (STA).

Measurement of infinitesimal phase response curves from noisy real neurons.

We sought to measure infinitesimal phase response curves (iPRCs) from rat hippocampal CA1 pyramidal neurons. It is difficult to measure iPRCs from noisy neurons because of the dilemma that either the linearity or the signal-to-noise ratio of responses to external perturbations must be sacrificed. To overcome this difficulty, we used an iPRC measurement model formulated as the Langevin phase equation (LPE) to extract iPRCs in the Bayesian scheme.

Switch of encoding characteristics in single neurons by subthreshold and suprathreshold stimuli.

Spike-triggered analysis is a statistical method used to elucidate encoding properties in neural systems by estimating the statistical structure of input stimulus preceding spikes. A recent numerical study suggested that the profile of the spike-triggered average (STA) changes depending on whether the mean input stimuli are subthreshold or suprathreshold. Here we analytically verify the difference between subthreshold STA and suprathreshold STA by using the spike response model (SRM).

An analytic solution of the cable equation predicts frequency preference of a passive shunt-end cylindrical cable in response to extracellular oscillating electric fields.

Under physiological and artificial conditions, the dendrites of neurons can be exposed to electric fields. Recent experimental studies suggested that the membrane resistivity of the distal apical dendrites of cortical and hippocampal pyramidal neurons may be significantly lower than that of the proximal dendrites and the soma. To understand the behavior of dendrites in time-varying extracellular electric fields, we analytically solved cable equations for finite cylindrical cables with and without a leak conductance attached to one end by employing the Green's function method.

Steep decrease in the specific membrane resistance in the apical dendrites of hippocampal CA1 pyramidal neurons.

Specific membrane resistance (R(m)), distributed non-uniformly over the dendrite, has a substantial effect on neuronal information processing, since it is a major determinant in subthreshold-synaptic integration. From experimental data of dendritic excitatory postsynaptic potential (EPSP) spread, we previously reported that non-uniform R(m) distribution in hippocampal CA1 pyramidal neurons could be expressed as a step function. However, it remains unclear how steeply R(m) decreases.

Statistical mechanics of lossy compression for nonmonotonic multilayer perceptrons.

A lossy data compression scheme for uniformly biased Boolean messages is investigated via statistical mechanics techniques. We utilize a treelike committee machine (committee tree) and a treelike parity machine (parity tree) whose transfer functions are nonmonotonic. The scheme performance at the infinite code length limit is analyzed using the replica method.

MAP estimation algorithm for phase response curves based on analysis of the observation process.

Many research groups have sought to measure phase response curves (PRCs) from real neurons. However, methods of estimating PRCs from noisy spike-response data have yet to be established. In this paper, we propose a Bayesian approach for estimating PRCs.

Estimated distribution of specific membrane resistance in hippocampal CA1 pyramidal neuron.

It has been suggested that dendritic membrane properties play an important role in a synaptic integration. In particular, the specific membrane resistance, one of membrane properties, has been reported to be non-uniformly distributed in a single neuron, although the spatial distribution of the specific membrane resistance is still unclear. To reveal its non-uniformity in dendrite, we estimated the spatial distribution of specific membrane resistance in a single neuron, based on voltage imaging data, observed optically in hippocampal CA1 slices.