arXiv subjects

Kenji Nagata

Publications and source records attributed to Kenji Nagata.

23 records · Page 2Linked to original sources

arXiv2018

Bayesian Spectral Deconvolution Based on Poisson Distribution: Bayesian Measurement and Virtual Measurement Analytics (VMA)

In this paper, we propose a new method of Bayesian measurement for spectral deconvolution, which regresses spectral data into the sum of unimodal basis function such as Gaussian or Lorentzian functions. Bayesian measurement is a framework for considering not only the target physical model but also the measurement model as a probabilistic model, and enables us to estimate the parameter of a physical model with its confidence interval through a Bayesian posterior distribution given a measurement data set. The measurement with Poisson noise is one of the most effective system to apply our proposed method. Since the measurement time is strongly related to the signal-to-noise ratio for the Poisson noise model, Bayesian measurement with Poisson noise model enables us to clarify the relationship between the measurement time and the limit of estimation. In this study, we establish the probabilistic model with Poisson noise for spectral deconvolution. Bayesian measurement enables us to perform virtual and computer simulation for a certain measurement through the established probabilistic model. This property is called "Virtual Measurement Analytics(VMA)" in this paper. We also show that the relationship between the measurement time and the limit of estimation can be extracted by using the proposed method in a simulation of synthetic data and real data for XPS measurement of MoS$_2$.

eess.SP↗

arXiv2016

Simultaneous Estimation of Noise Variance and Number of Peaks in Bayesian Spectral Deconvolution

The heuristic identification of peaks from noisy complex spectra often leads to misunderstanding of the physical and chemical properties of matter. In this paper, we propose a framework based on Bayesian inference, which enables us to separate multipeak spectra into single peaks statistically and consists of two steps. The first step is estimating both the noise variance and the number of peaks as hyperparameters based on Bayes free energy, which generally is not analytically tractable. The second step is fitting the parameters of each peak function to the given spectrum by calculating the posterior density, which has a problem of local minima and saddles since multipeak models are nonlinear and hierarchical. Our framework enables the escape from local minima or saddles by using the exchange Monte Carlo method and calculates Bayes free energy via the multiple histogram method. We discuss a simulation demonstrating how efficient our framework is and show that estimating both the noise variance and the number of peaks prevents overfitting, overpenalizing, and misunderstanding the precision of parameter estimation.

physics.data-an↗

arXiv2014

Oscillations in Spurious States of the Associative Memory Model with Synaptic Depression

The associative memory model is a typical neural network model, which can store discretely distributed fixed-point attractors as memory patterns. When the network stores the memory patterns extensively, however, the model has other attractors besides the memory patterns. These attractors are called spurious memories. Both spurious states and memory states are equilibrium, so there is little difference between their dynamics. Recent physiological experiments have shown that short-term dynamic synapse called synaptic depression decreases its transmission efficacy to postsynaptic neurons according to the activities of presynaptic neurons. Previous studies have shown that synaptic depression induces oscillation in the network and decreases the storage capacity at finite temperature. How synaptic depression affects spurious states, however, is still unclear. We investigate the effect of synaptic depression on spurious states through Monte Carlo simulation. The results demonstrate that synaptic depression does not affect the memory states but mainly destabilizes the spurious states and induces the periodic oscillations.

q-bio.NC↗

arXiv2011

Influence of synaptic depression on memory storage capacity

Synaptic efficacy between neurons is known to change within a short time scale dynamically. Neurophysiological experiments show that high-frequency presynaptic inputs decrease synaptic efficacy between neurons. This phenomenon is called synaptic depression, a short term synaptic plasticity. Many researchers have investigated how the synaptic depression affects the memory storage capacity. However, the noise has not been taken into consideration in their analysis. By introducing "temperature", which controls the level of the noise, into an update rule of neurons, we investigate the effects of synaptic depression on the memory storage capacity in the presence of the noise. We analytically compute the storage capacity by using a statistical mechanics technique called Self Consistent Signal to Noise Analysis (SCSNA). We find that the synaptic depression decreases the storage capacity in the case of finite temperature in contrast to the case of the low temperature limit, where the storage capacity does not change.

cond-mat.dis-nn↗

arXiv2010

Instabilities in associative memory model with synaptic depression and switching phenomena among attractors

We investigated how the stability of macroscopic states in the associative memory model is affected by synaptic depression. To this model, we applied the dynamical mean-field theory, which has recently been developed in stochastic neural network models with synaptic depression. By introducing a sublattice method, we derived macroscopic equations for firing state variables and depression variables. By using the macroscopic equations, we obtained the phase diagram when the strength of synaptic depression and the correlation level among stored patterns were changed. We found that there is an unstable region in which both the memory state and mixed state cannot be stable and that various switching phenomena can occur in this region.

cond-mat.dis-nn↗