The widespread deployment of 5G networks has drawn growing attention to the optimization of wireless communication systems. Among various techniques, quantum-based approaches are distinguished by their remarkably fast computation time compared to conventional optimization methods. In this paper, we propose a quantum-inspired approach to the wireless resource allocation problem. The problem is formulated as an Ising model and solved using both Coherent Ising Machine (CIM) and D-Wave’s quantum annealer. Furthermore, the performance of these quantum-based methods is compared with that of the Simulated Annealing (SA) algorithm.

Graph neural networks (GNNs) with unsupervised learning can provide high-quality approximate solutions to large-scale combinatorial optimization problems (COPs) with efficient time complexity, making them versatile for various applications. However, since this method maps the COP to the training process of a GNN, and the current mainstream backpropagation-based training algorithms are prone to falling into local minima, the optimization performance is still inferior to the current state-of-the-art (SOTA) COP methods. To address this issue, inspired by the possibility of learning through chaotic dynamics of the real brain, we introduce a chaotic training algorithm, i.e., chaotic graph backpropagation (CGBP), which introduces a local loss function in GNN that makes the training process not only chaotic but also highly efficient. Different from existing methods, we show that the global ergodicity and pseudorandomness with fractal structure of such chaotic dynamics enable CGBP to learn GNNs effectively and globally, thus solving the COP efficiently. We have applied CGBP to solve various COPs, such as the maximum independent set (MIS), maximum cut (MC), and graph coloring (GC). Results on several large-scale benchmark datasets showcase that CGBP can compete with or outperform SOTA methods. In addition, CGBP can be easily integrated into any existing learning method as an additional universal plug-in module to improve the searching ability and performance.

Reservoir Computing (RC) is a time-efficient computational paradigm derived from Recurrent Neural Networks (RNNs). The Simple Cycle Reservoir (SCR) is an RC model that stands out for its minimalistic design, offering extremely low construction complexity and proven capability of universally approximating time-invariant causal fading memory filters, even in the linear dynamics regime. This paper introduces Multiple Simple Cycle Reservoirs (MSCRs), a multi-reservoir framework that extends Echo State Networks (ESNs) by replacing a single large reservoir with multiple interconnected SCRs. We demonstrate that MSCRs optimized with Particle Swarm Optimization (MSCR-PSO) outperform existing multi-reservoir models, achieving competitive predictive performance with a lower-dimensional state space. By modeling interconnections as a weighted Directed Acyclic Graph (DAG), our approach enables flexible, task-specific network topology adaptation. Numerical simulations on three benchmark time-series prediction tasks confirm these advantages over rival algorithms. These findings highlight the potential of MSCR-PSO as a promising framework for optimizing multi-reservoir systems, providing a foundation for further advancements and applications of interconnected SCRs for developing efficient AI devices.

Online classification of multivariate time series is crucial across diverse domains, from healthcare monitoring to industrial systems. Reservoir computing (RC), which employs a fixed, randomly initialized recurrent neural network to encode temporal data sequentially, has emerged as a promising approach due to its efficient training and ability to capture complex temporal patterns. However, standard RC implementations rely on regression-based readouts that produce temporally unstable outputs and limit interpretability. We introduce Gaussian Reservoir State Analysis (GRSA), a novel approach that combines reservoirs’ temporal processing capabilities with distribution-based classification. GRSA models reservoir responses to sequences in each class using multivariate Gaussian distribution and performs classification based on statistical distances. Through comprehensive evaluation using the UEA Multivariate Time Series Classification Archive, we demonstrate that GRSA significantly outperforms the regression-based RC while providing more stable and interpretable outputs. The method enables continuous classification by generating predictions at each time step and maintains robust performance even with shortened test sequences, making it particularly suitable for early classification tasks. GRSA’s architectural simplicity allows for various extensions and adaptations across different application domains.

Spatially Resolved Transcriptomics (SRT) serves as a cornerstone in biomedical research, revealing the heterogeneity of tissue microenvironments. Integrating multimodal data including gene expression, spatial coordinates, and morphological information poses significant challenges for accurate spatial domain identification. Herein, we present the Multi-view Contrastive Graph Autoencoder (MCGAE), a cutting-edge deep computational framework specifically designed for the intricate analysis of spatial transcriptomics (ST) data. MCGAE advances the field by creating multi-view representations from gene expression and spatial adjacency matrices. Utilizing modular modeling, contrastive graph convolutional networks, and attention mechanisms, it generates modality-specific spatial representations and integrates them into a unified embedding. This integration process is further enriched by the inclusion of morphological image features, markedly enhancing the framework's capability to process multimodal data. Applied to both simulated and real SRT datasets, MCGAE demonstrates superior performance in spatial domain detection, data denoising, trajectory inference, and 3D feature extraction, outperforming existing methods. Specifically, in colorectal cancer liver metastases, MCGAE integrates histological and gene expression data to identify tumor invasion regions and characterize cellular molecular regulation. This breakthrough extends ST analysis and offers new tools for cancer and complex disease research.

Quantification of intrahepatic covalently closed circular DNA (cccDNA) is a key for evaluating an elimination of hepatitis B virus (HBV) in infected patients. However, quantifying cccDNA requires invasive methods such as a liver biopsy, which makes it impractical to access the dynamics of cccDNA in patients. Although HBV RNA and HBV core-related antigens (HBcrAg) have been proposed as surrogate markers for evaluating cccDNA activity, they do not necessarily estimate the amount of cccDNA. Here, we employed a recently developed multiscale mathematical model describing intra- and intercellular viral propagation and applied it in HBV-infected patients under treatment. We developed a model that can predict intracellular HBV dynamics by use of extracellular viral markers, including HBsAg, HBV DNA, and HBcrAg in peripheral blood. Importantly, the model prediction of the amount of cccDNA in patients over time was confirmed to be well correlated with the data for quantified cccDNA by paired liver biopsy. Thus, our method combining classic and emerging surrogate markers enables us to predict the decay dynamics of cccDNA in patients undergoing treatment.

Liver transplantation is almost the only way to save patients with end-stage liver disease. Particularly, living donor liver transplantation (LDLT) has gained importance in recent years thanks to the shorter waiting times and better graft quality than with deceased donor liver transplantation (DDLT). However, some patients experience graft loss due to unexpected infections, sepsis, or immune-mediated rejection of the transplanted organ. An urgent need exists to clarify which patients experience graft loss. Several models have been proposed, but most analyze the classic DDLT, and knowledge about LDLT is lacking. In this study, we retrospectively analyzed clinical data from 748 patients who underwent LDLT. By adapting machine learning methods, we predicted early graft loss (within 180 days postoperatively) with better performance than conventional models. The model enabled us to stratify a highly heterogeneous sample of patients into five groups. By focusing on survival time, we next categorized the patients into three groups with early, intermediate, and late or no graft loss. Notably, we identified the intermediate-loss group as a distinct population similar to the early-loss population but with different survival times. Additionally, by proposing a hierarchical prediction method, we developed an approach to distinguish these populations using data up to 30 days postoperatively. Our findings will enable the early identification of individuals at risk of graft loss, particularly those in the early- and intermediate-loss groups. This will allow for appropriate patient care, such as switching to DDLT, identifying other living donors for LDLT, or preparing for re-transplantation, leading to a bottom-up improvement in transplant success rates.

We propose a novel backpropagation algorithm for training spiking neural networks (SNNs) that allows each neuron to fire multiple times, unlike conventional methods where each neuron fires at most once. The proposed algorithm inherits the advantages of conventional timing-based methods because it computes accurate gradients for spike timing, which promotes efficient temporal coding. Our SNN model outperformed comparable SNN models and achieved as high accuracy as non-convolutional artificial neural networks. The spike count property of our networks was altered depending on the time constant of the postsynaptic current and membrane potential. Moreover, we show that there exists an optimal time constant with the maximum test accuracy. That was not seen in conventional SNNs with single-spike restrictions on time-to-fast-spike (TTFS) coding. This result demonstrates the computational properties of SNNs that encode information into the multi-spike timing of individual neurons.

Chronic infection with hepatitis B virus (HBV) is caused by the persistence of closed circular DNA (cccDNA) in the nucleus of infected hepatocytes. Despite available therapeutic anti-HBV agents, eliminating the cccDNA remains challenging. Thus, quantifying and understanding the dynamics of cccDNA are essential for developing effective treatment strategies and new drugs. However, such study requires repeated liver biopsy to measure the intrahepatic cccDNA, which is basically not accepted because liver biopsy is potentially morbid and not common during hepatitis B treatment. We here aimed to develop a noninvasive method for quantifying cccDNA in the liver using surrogate markers in peripheral blood. We constructed a multiscale mathematical model that explicitly incorporates both intracellular and intercellular HBV infection processes. The model, based on age-structured partial differential equations, integrates experimental data from in vitro and in vivo investigations. By applying this model, we roughly predicted the amount and dynamics of intrahepatic cccDNA within a certain range using specific viral markers in serum samples, including HBV DNA, HBsAg, HBeAg, and HBcrAg. Our study represents a significant step towards advancing the understanding of chronic HBV infection. The noninvasive quantification of cccDNA using our proposed method holds promise for improving clinical analyses and treatment strategies. By comprehensively describing the interactions of all components involved in HBV infection, our multiscale mathematical model provides a valuable framework for further research and the development of targeted interventions.

Excitatory/inhibitory (E/I) balance is significantly associated with cognitive function. Its imbalance impairs cognitive function, particularly in patients with psychiatric disorders. Recent physiological and modeling findings show that excitatory postsynaptic potentials (EPSPs) have a long-tailed distribution and contribute to the generation of spontaneous activity. Moreover, this spontaneous activity and its response to the external stimulus significantly alternate under the different E/I balance. However, the effects of the E/I balance under long-tailed EPSPs at the functional level remain unknown. Hence, to elucidate this relationship, we constructed a reservoir computing (RC) model generating the long-tailed distribution of EPSPs and investigated the effect of the E/I balance on the learning performance of RC in the memory capacity (MC) task, which measures how correctly delayed input signals can be reproduced. The results revealed that an appropriate E/I balance maximized the MC. This high MC was realized by recurrent spike propagation under long-tailed EPSPs. These findings contribute to the understanding of the effect of the E/I balance in physiologically relevant neural networks.

Advances in single-cell multi-omics technology provide an unprecedented opportunity to fully understand cellular heterogeneity. However, integrating omics data from multiple modalities is challenging due to the individual characteristics of each measurement. Here, to solve such a problem, we propose a contrastive and generative deep self-expression model, called single-cell multimodal self-expressive integration (scMSI), which integrates the heterogeneous multimodal data into a unified manifold space. Specifically, scMSI first learns each omics-specific latent representation and self-expression relationship to consider the characteristics of different omics data by deep self-expressive generative model. Then, scMSI combines these omics-specific self-expression relations through contrastive learning. In such a way, scMSI provides a paradigm to integrate multiple omics data even with weak relation, which effectively achieves the representation learning and data integration into a unified framework. We demonstrate that scMSI provides a cohesive solution for a variety of analysis tasks, such as integration analysis, data denoising, batch correction and spatial domain detection. We have applied scMSI on various single-cell and spatial multimodal datasets to validate its high effectiveness and robustness in diverse data types and application scenarios.

We examine the efficiency of information processing in a balanced excitatory and inhibitory (E-I) network during the developmental critical period, when network plasticity is heightened. A multimodule network composed of E-I neurons was defined, and its dynamics were examined by regulating the balance between their activities. When adjusting E-I activity, both transitive chaotic synchronization with a high Lyapunov dimension and conventional chaos with a low Lyapunov dimension were found. In between, the edge of high-dimensional chaos was observed. To quantify the efficiency of information processing, we applied a short-term memory task in reservoir computing to the dynamics of our network. We found that memory capacity was maximized when optimal E-I balance was realized, underscoring both its vital role and vulnerability during critical periods of brain development.

Spiking neural networks (SNNs) are brain-inspired mathematical models with the ability to process information in the form of spikes. SNNs are expected to provide not only new machine-learning algorithms but also energy-efficient computational models when implemented in very-large-scale integration (VLSI) circuits. In this article, we propose a novel supervised learning algorithm for SNNs based on temporal coding. A spiking neuron in this algorithm is designed to facilitate analog VLSI implementations with analog resistive memory, by which ultrahigh energy efficiency can be achieved. We also propose several techniques to improve the performance on recognition tasks and show that the classification accuracy of the proposed algorithm is as high as that of the state-of-the-art temporal coding SNN algorithms on the MNIST and Fashion-MNIST datasets. Finally, we discuss the robustness of the proposed SNNs against variations that arise from the device manufacturing process and are unavoidable in analog VLSI implementation. We also propose a technique to suppress the effects of variations in the manufacturing process on the recognition performance.

Spiking neural networks (SNNs) are expected to be energy efficient when implemented on dedicated hardware. However, fully exploiting SNN’s characteristics such as event-driven communications challenges on circuit designers and manufacturers. In this paper, inspired by the recent success of an artificial neural network (ANN) based system, known as charge-domain computing (CDC), we propose a novel framework for SNNs called “RC-Spike.” As CDC, RC-Spike uses a two-phase system: input spikes are received in the accumulation phase, and a neuron produces a spike in the spike generation phase. In RC-Spike, synaptic currents are accumulated with resistively coupled synapses, with which circuit implementation can be simplified compared with CDC circuits. Because of this resistive coupling effect, a neuron in RC-Spike does not compute an exact dot product. However, RC-Spike can be successfully trained in the framework of SNNs, and we show that the learning performance of RC-Spike is as high as ANNs on the MNIST and Fashion-MNIST datasets.

This paper proposes a high energy efficiency CMOS mixed-signal spiking neural network circuit using a time-domain digital-to-analog converter (TDAC) for realizing online and on-chip brainmorphic learning hardware. The circuit consists of a mixed-signal synapse circuit and an analog leaky integrate-and-fire neuron circuit. The TDAC converts synaptic weights held by digital memory into an analog current that realizes a biologically plausible synaptic response, which is employed as an output stage for our synapse circuit. To evaluate online and on-chip learning operation, the remote supervised method (ReSuMe) was implemented using TSMC 40-nm (1-poly, 8-metal) CMOS technology, and this circuit was evaluated by a Spectre circuit simulator. The circuit simulation results show that energy per synaptic event in our circuit was 20.1 fJ for multiply-accumulation operation and 92.1 fJ for ReSuMe.

We previously proposed, on theoretical grounds, that the cerebellum must regulate the dimensionality of its neuronal activity during motor learning and control to cope with the low firing frequency of inferior olive neurons, which form one of two major inputs to the cerebellar cortex. Such dimensionality regulation is possible via modulation of electrical coupling through the gap junctions between inferior olive neurons by inhibitory GABAergic synapses. In addition, we previously showed in simulations that intermediate coupling strengths induce chaotic firing of inferior olive neurons and increase their information carrying capacity. However, there is no in vivo experimental data supporting these two theoretical predictions. Here, we computed the levels of synchrony, dimensionality, and chaos of the inferior olive code by analyzing in vivo recordings of Purkinje cell complex spike activity in three different coupling conditions: carbenoxolone (gap junctions blocker), control, and picrotoxin (GABA-A receptor antagonist). To examine the effect of electrical coupling on dimensionality and chaotic dynamics, we first determined the physiological range of effective coupling strengths between inferior olive neurons in the three conditions using a combination of a biophysical network model of the inferior olive and a novel Bayesian model averaging approach. We found that effective coupling co-varied with synchrony and was inversely related to the dimensionality of inferior olive firing dynamics, as measured via a principal component analysis of the spike trains in each condition. Furthermore, for both the model and the data, we found an inverted U-shaped relationship between coupling strengths and complexity entropy, a measure of chaos for spiking neural data. These results are consistent with our hypothesis according to which electrical coupling regulates the dimensionality and the complexity in the inferior olive neurons in order to optimize both motor learning and control of high dimensional motor systems by the cerebellum.

This study proposes a method that speeds up computing persistent homology of time series data. Persistent homology is recently used for clutering time series data and detecting periodicity of them. The proposed method uses line segments to approximate a trajectory in delay-coordinate space. Cubic Bézier curves are fitted to given data and divided into line segments. The distance between line segments is defined and calculated to construct the Vietoris-Rips complex of segments. Exploiting the Vietoris-Rips complex enables us to use fast software like Ripser. In experiments, the performance of the proposed method is compared with that of the ordinary method. The proposed method was 30 times or more faster than the ordinary method. It also smooths noisy data and produces more precise persistent homology.

Millions of people are affected by neurological disorders. Brain-Machine Interfaces (BMIs) and neuroprosthesis have been the object of extensive research and may represent a valid treatment for diverse neurological diseases. The realization of neuroprostheses requires technologies to interact with neuronal cell assemblies in the nervous system, and to drive them into a desired state or to produce a specific behavior. One of the best solutions to design this kind of neuroprostheses is to implement a real-time hardware Spiking Neural Network (SNN). Not only will it mimic the electrical behavior of one biological neuron but also the dynamic of a neural network with plasticity rules. In this work, we present digital hardware implementation of biomimetic SNNs for stimulating in vitro cells. By connecting living neurons with electronic SNNs, we are intending to develop a model system for a brain-electronics interface.

A temporal point process is a mathematical model for a time series of discrete events, which covers various applications. Recently, recurrent neural network (RNN) based models have been developed for point processes and have been found effective. RNN based models usually assume a specific functional form for the time course of the intensity function of a point process (e.g., exponentially decreasing or increasing with the time since the most recent event). However, such an assumption can restrict the expressive power of the model. We herein propose a novel RNN based model in which the time course of the intensity function is represented in a general manner. In our approach, we first model the integral of the intensity function using a feedforward neural network and then obtain the intensity function as its derivative. This approach enables us to both obtain a flexible model of the intensity function and exactly evaluate the log-likelihood function, which contains the integral of the intensity function, without any numerical approximations. Our model achieves competitive or superior performances compared to the previous state-of-the-art methods for both synthetic and real datasets.

Modeling cell differentiation from omics data is an essential problem in systems biology research. Although many algorithms have been established to analyze scRNA-seq data, approaches to infer the pseudo-time of cells or quantify their potency have not yet been satisfactorily solved. Here, we propose the Landscape of Differentiation Dynamics (LDD) method, which calculates cell potentials and constructs their differentiation landscape by a continuous birth-death process from scRNA-seq data. From the viewpoint of stochastic dynamics, we exploited the features of the differentiation process and quantified the differentiation landscape based on the source-sink diffusion process. In comparison with other scRNA-seq methods in seven benchmark datasets, we found that LDD could accurately and efficiently build the evolution tree of cells with pseudo-time, in particular quantifying their differentiation landscape in terms of potency. This study provides not only a computational tool to quantify cell potency or the Waddington potential landscape based on scRNA-seq data, but also novel insights to understand the cell differentiation process from a dynamic perspective.

Dynamic network biomarkers (DNB) can identify the critical state or tipping point of a disease, thereby predicting rather than diagnosing the disease. However, it is difficult to apply the DNB theory to clinical practice because evaluating DNB at the critical state required the data of multiple samples on each individual, which are generally not available, and thus limit the applicability of DNB. In this study, we developed a novel method, i.e., single-sample DNB (sDNB), to detect early-warning signals or critical states of diseases in individual patients with only a single sample for each patient, thus opening a new way to predict diseases in a personalized way. In contrast to the information of differential expressions used in traditional biomarkers to "diagnose disease", sDNB is based on the information of differential associations, thereby having the ability to "predict disease" or "diagnose near-future disease". Applying this method to datasets for influenza virus infection and cancer metastasis led to accurate identification of the critical states or correct prediction of the immediate diseases based on individual samples. We successfully identified the critical states or tipping points just before the appearance of disease symptoms for influenza virus infection and the onset of distant metastasis for individual patients with cancer, thereby demonstrating the effectiveness and efficiency of our method for quantifying critical states at the single-sample level.

This paper presents a coordination scheme of automated vehicles at an intersection without using any traffic lights. Using a two-way communication network, vehicles approaching the intersection from all sections are globally coordinated, by considering their states all together in a model predictive control framework, in order to achieve smooth traffic flows at the intersection. The optimal trajectories of the vehicles are computed based on avoidance of their cross-collision risks around the intersection under relevant constraints and preferences. The scheme efficiently utilizes the intersection area by preventing each pair of conflicting vehicles from approaching their cross-collision point at the same time, instead of reserving the whole intersection area for the conflicting vehicles one after another. The scheme also enables left- or right-turning movements of vehicles under constrained velocity without using any auxiliary lanes. The proposed vehicle-intersection coordination scheme is evaluated through numerical simulation in a typical test intersection consisting of both multilanes and single-lane approaches with turning movements of vehicles. Observations under different traffic flow conditions reveal that the proposed scheme significantly improves intersection performance compared with the traditional signalized intersection scheme.

The Separable Property (SP) condition for time and frequency synchronization compels us to determine phase correction terms in the Gabor Division/Spread Spectrum System (GD/S3). The phase correction technique has led us to design a GD/S3receiver equipped with phase-tuned layers for Mary phase shift keying (MPSK). The resulting signal points of the time domain (TD) and frequency domain (FD) correlator outputs of each layer for M = 16 consist of only three lines, called a main-lobe with a layer-dependent phase angle and sidelobes with phase angle ±2π/16. The two sidelobes suggesting bit errors are removed by using code division multiple target (CDMT), inspired by code division multiple-access (CDMA) philosophy. The simulation result for 16PSK is confirmed.

MOTIVATION: Unlike traditional diagnosis of an existing disease state, detecting the pre-disease state just before the serious deterioration of a disease is a challenging task, because the state of the system may show little apparent change or symptoms before this critical transition during disease progression. By exploring the rich interaction information provided by high-throughput data, the dynamical network biomarker (DNB) can identify the pre-disease state, but this requires multiple samples to reach a correct diagnosis for one individual, thereby restricting its clinical application. RESULTS: In this article, we have developed a novel computational approach based on the DNB theory and differential distributions between the expressions of DNB and non-DNB molecules, which can detect the pre-disease state reliably even from a single sample taken from one individual, by compensating insufficient samples with existing datasets from population studies. Our approach has been validated by the successful identification of pre-disease samples from subjects or individuals before the emergence of disease symptoms for acute lung injury, influenza and breast cancer.

We study dynamical mechanisms responsible for changes of the firing rate during four different bifurcation transitions in the two-dimensional Hindmarsh-Rose (2DHR) neuron model: the saddle node on an invariant circle (SNIC) bifurcation to the supercritical Andronov-Hopf (AH) one, the SNIC bifurcation to the saddle-separatrix loop (SSL) one, the AH bifurcation to the subcritical AH (SAH) one, and the SSL bifurcation to the AH one. For this purpose, we study slopes of the firing rate curve with respect to not only an external input current but also temperature that can be interpreted as a timescale in the 2DHR neuron model. These slopes are mathematically formulated with phase response curves (PRCs), expanding the firing rate with perturbations of the temperature and external input current on the one-dimensional space of the phase [Formula: see text] in the 2DHR oscillator. By analyzing the two different slopes of the firing rate curve with respect to the temperature and external input current, we find that during changes of the firing rate in all of the bifurcation transitions, the calculated slope with respect to the temperature also changes. This is largely dependent on changes in the PRC size that is also related to the slope with respect to the external input current. Furthermore, we find phase transition-like switches of the firing rate with a possible increase of the temperature during the SSL-to-AH bifurcation transition.

Cognitive radio technology improves radio resource usage by reconfiguring the wireless connection settings according to the optimum decisions, which are made on the basis of the collected context information. This paper focuses on optimization algorithms for decision making to optimize radio resource usage in heterogeneous cognitive wireless networks. For networks with centralized management, we proposed a novel optimization algorithm whose solution is guaranteed to be exactly optimal. In order to avoid an exponential increase of computational complexity in large-scale wireless networks, we model the target optimization problem as a minimum cost-flow problem and find the solution of the problem in polynomial time. For the networks with decentralized management, we propose a distributed algorithm using the distributed energy minimization dynamics of the Hopfield–Tank neural network. Our algorithm minimizes a given objective function without any centralized calculation. We derive the decision-making rule for each terminal to optimize the entire network. We demonstrate the validity of the proposed algorithms by several numerical simulations and the feasibility of the proposed schemes by designing and implementing them on experimental cognitive radio network systems.

Traffic management on road networks is an emerging research field in control engineering due to the strong demand to alleviate traffic congestion in urban areas. Interaction among vehicles frequently causes congestion as well as bottlenecks in road capacity. In dense traffic, waves of traffic density propagate backward as drivers try to keep safe distances through frequent acceleration and deceleration. This paper presents a vehicle driving system in a model predictive control framework that effectively improves traffic flow. The vehicle driving system regulates safe intervehicle distance under the bounded driving torque condition by predicting the preceding traffic. It also focuses on alleviating the effect of braking on the vehicles that follow, which helps jamming waves attenuate to in the traffic. The proposed vehicle driving system has been evaluated through numerical simulation in dense traffic.

A non-binary analog-to-digital converter (ADC) based on β-expansion, called a β-encoder, can reportedly achieve robustness against large process variation and widespread environment change. The quantization error of the β-encoder is not uniformly distributed, which makes mean squared error (MSE) evaluation difficult. In this work, an analysis method for giving the upper bound of the MSE of the quantization error is proposed. We also gave an evaluation of signal-to-noise-ratio (SNR), which is effective for designing β-encoders.

Delay and Doppler determination of a time-delayed and frequency-shifted signal is one of fundamental problems in communication. The two-parameter estimation is reduced to two one-parameter estimation problems in time- and frequency-domain signals. Motivated by Gabor's communication theory, we proceed further parallelism between time- and frequency-domain signals and solve the two problems individually and cooperatively. Simulation results without prescribed information are reported.

Recently proposed new Time-Domain (TD) synchronization using frequency integration and TD Spread Spectrum (SS) codes has been shown to be robust to frequency offset, that has its dual Frequency-Domain (FD) synchronization using time integration and FD SS codes which is robust to timing offset. Separable Property (SP) is defined for time-frequency synchronization under the condition containing time and frequency deviations to be performed separately and cooperatively. The SP compels us to design phase correction on SS codes and transmitted data.

Non-coherent signal with unknown delay and Doppler is recovered by Gabor division/spread spectrum system. Separable property (SP) condition for time and frequency synchronization leads us to determine delay and Doppler precisely. This paper gives an enlargement of acceptable delay and Doppler region by using multiple codes and their associated receivers simultaneously without increasing computation time. As its application, two-target simultaneous determination is discussed.

We study a realistic model of a cortical column taking into account short-term plasticity between pyramidal cells and interneurons. The simulation of leaky integrate-and-fire neurons shows that low-frequency oscillations emerge spontaneously as a result of intrinsic network properties. These oscillations are composed of prolonged phases of high and low activity reminiscent of cortical up and down states, respectively. We simplify the description of the network activity by using a mean field approximation and reduce the system to two slow variables exhibiting some relaxation oscillations. We identify two types of slow oscillations. When the combination of dynamic synapses between pyramidal cells and those between interneurons accounts for the generation of these slow oscillations, the end of the up phase is characterized by asynchronous fluctuations of the membrane potentials. When the slow oscillations are mainly driven by the dynamic synapses between interneurons, the network exhibits fluctuations of membrane potentials, which are more synchronous at the end than at the beginning of the up phase. Additionally, finite size effect and slow synaptic currents can modify the irregularity and frequency, respectively, of these oscillations. Finally, we consider possible roles of a slow oscillatory input modeling long-range interactions in the brain. Spontaneous slow oscillations of local networks are modulated by the oscillatory input, which induces, notably, synchronization, subharmonic synchronization, and chaotic relaxation oscillations in the mean field approximation. In the case of forced oscillations, the slow population-averaged activity of leaky integrate-and-fire neurons can have both deterministic and stochastic temporal features. We discuss the possibility that long-range connectivity controls the emergence of slow sequential patterns in local populations due to the tendency of a cortical column to oscillate at low frequency.

A new method for improving the storage capacity of associative memory models on a neural network is proposed. The storage capacity of the network increases in proportion to the network size in the case of random patterns, but, in general, the capacity suffers from correlation among memory patterns. Numerous solutions to this problem have been proposed so far, but their high computational cost limits their scalability. In this paper, we propose a novel and simple solution that is locally computable without any iteration. Our method involves XNOR masking of the original memory patterns with random patterns, and the masked patterns and masks are concatenated. The resulting decorrelated patterns allow higher storage capacity at the cost of the pattern length. Furthermore, the increase in the pattern length can be reduced through blockwise masking, which results in a small amount of capacity loss. Movie replay and image recognition are presented as examples to demonstrate the scalability of the proposed method.

Frequency division spread spectrum system (FD/S3) has been recently proposed, which uses frequency domain spreading codes and can allow frequency offsets between users. Motivated by the conventional time-domain code acquisition, we propose a frequency (F)-domain code acquisition method using time (T)-domain integrator, which permits frequency offset, which leads to a new T-domain code acquisition using F-domain integrator. Recently proposed Gabor division (GD)/S3system permits us to use both F- and T-domain code acquisitions separately and cooperatively*.

In this paper, we analyze effectiveness of a new metaheuristic approach, which utilizes ideal spatio-temporal chaotic dynamics generated by Lebesgue Spectrum Filter (LSF). In the previous researches on the additive chaotic noise to the heuristic searches for combinatorial optimization problems, it has been shown that the chaotic sequences with negative autocorrelation improve the performance of asynchronously updated algorithms, such as mutually connected neural networks, asynchronous heuristic searches and so on. The effectiveness of chaos can be understood by the conventional theory of the chaotic CDMA, which showed that the cross-correlation between the sequences with negative autocorrelation becomes lowest. The spatio-temporal chaotic searching dynamics with such lowest cross-correlation has been shown effective to improve asynchronously updated combinatorial optimization algorithms. In this paper, as such asynchronously updated combinatorial optimization algorithms, we introduce the 2-exchange method, the k-opt method, the Lin-Kernighan method, and the Or-opt method, and improve the performance of them by applying the LSF. Our numerical simulation results show that the performances of all above heuristic methods could be improved by using negative autocorrelation.

An energy saving control of unstable periodic orbits embedded in a hybrid chaotic system is proposed. The conventional controlling chaos methods utilize small perturbations of states or parameters as control input, however, quick time responses cannot be expected since the corresponding basins of attractions for higher periodic solutions become tiny. While If one allows a large perturbation to improve the time response, rather the total controlling energy which is proposed to the distance between the target orbit and the current orbit may increases. In this paper, when we consider the chaotic hybrid system, we noticed that we could utilize the perturbation of the referenced value for controlling, i.e., only a threshold value (Poincaré mapping surface) is updated in control. No control input as a perturbation of the state or parameter value is applied to the system. In fact, the threshold value is used instantly when the feedback system determines the next updated threshold value. The variation of the threshold value can be obtained numerically by computing variational equations, and the control matrix is designed with the linear control theory. Since no affection to the state and parameters, it is emphasized that the total behavior of the controlled system is different from the conventional methods, especially it is unlike the impulsive control methods. We demonstrate this control method in a simple hybrid system and show that a large basin of attraction for the control is realized.

In this paper, we propose a numerical method to calculate the grazing bifurcation points in an impact oscillator with periodic boundaries. First, we illustrate the n-dimensional autonomous impact oscillator with the moving boundaries. The boundaries consist of the scalar function involving the periodic function. When the trajectory hits one boundary, the solution jumps to the other immediately. Next, we construct the composite Poincaré map and show its derivatives. Using the derivatives, we calculate the location of the periodic point, bifurcation parameter and time that elapses before the trajectory hitting the boundary. Finally, we apply the method to a Rayleigh-type oscillator with the sinusoidal boundaries to confirm its validity.

We design oscillators controlled by optical input, which we call "LED fireflies," and study their synchronization phenomena. The most distinctive feature of LED fireflies is their collective behavior. The LED fireflies produce a huge variety of synchronous patterns. We demonstrate an optical art work that behaves like living entities by making use of their property.

The dependence of the dynamics of pulse-coupled neural networks on random rewiring of excitatory and inhibitory connections is examined. When both excitatory and inhibitory connections are rewired, periodic synchronization emerges with a Hopf-like bifurcation and a subsequent period-doubling bifurcation; chaotic synchronization is also observed. When only excitatory connections are rewired, periodic synchronization emerges with a saddle node-like bifurcation, and chaotic synchronization is also observed. This result suggests that randomness in the system does not necessarily contaminate the system, and sometimes it even introduces rich dynamics to the system such as chaos.

We propose a Gabor division (GD)-CDMA system which permits time and frequency offsets. Negatively correlated time- and frequency-domain spreading codes have shown to reduce the variances of mutual- and self-interferences. In this paper, we show that such negatively correlated spreading codes have another important property that they flatten the time-frequency energy distributions of signals in GD-CDMA system.

MOTIVATION: In the developing nervous system, the expression of proneural genes, i.e. Hes1, Neurogenin-2 (Ngn2) and Deltalike-1 (Dll1), oscillates in neural progenitors with a period of 2-3 h, but is persistent in post-mitotic neurons. Unlike the synchronization of segmentation clocks, oscillations in neural progenitors are asynchronous between cells. It is known that Notch signaling, in which Notch in a cell can be activated by Dll1 in neighboring cells (trans-activation) and can also be inhibited by Dll1 within the same cell (cis-inhibition), is important for neural fate decisions. There have been extensive studies of trans-activation, but the operating mechanisms and potential implications of cis-inhibition are less clear and need to be further investigated. RESULTS: In this article, we present a computational model for neural fate decisions based on intertwined dynamics with trans-activation and cis-inhibition involving the Hes1, Notch and Dll1 proteins. In agreement with experimental observations, the model predicts that both trans-activation and cis-inhibition play critical roles in regulating the choice between remaining as a progenitor and embarking on neural differentiation. In particular, trans-activation is essential for generation of oscillations in neural progenitors, and cis-inhibition is important for the asynchrony between adjacent cells, indicating that the asynchronous oscillations in neural progenitors depend on cooperation between trans-activation and cis-inhibition. In contrast, cis-inhibition plays more critical roles in embarking on neural differentiation by inactivating intercellular Notch signaling. The model presented here might be a good candidate for providing the first qualitative mechanism of neural fate decisions mediated by both trans-activation and cis-inhibition.

The prefrontal cortex (PFC) plays a crucial role in flexible cognitive behavior by representing task relevant information with its working memory. The working memory with sustained neural activity is described as a neural dynamical system composed of multiple attractors, each attractor of which corresponds to an active state of a cell assembly, representing a fragment of information. Recent studies have revealed that the PFC not only represents multiple sets of information but also switches multiple representations and transforms a set of information to another set depending on a given task context. This representational switching between different sets of information is possibly generated endogenously by flexible network dynamics but details of underlying mechanisms are unclear. Here we propose a dynamically reorganizable attractor network model based on certain internal changes in synaptic connectivity, or short-term plasticity. We construct a network model based on a spiking neuron model with dynamical synapses, which can qualitatively reproduce experimentally demonstrated representational switching in the PFC when a monkey was performing a goal-oriented action-planning task. The model holds multiple sets of information that are required for action planning before and after representational switching by reconfiguration of functional cell assemblies. Furthermore, we analyzed population dynamics of this model with a mean field model and show that the changes in cell assemblies' configuration correspond to those in attractor structure that can be viewed as a bifurcation process of the dynamical system. This dynamical reorganization of a neural network could be a key to uncovering the mechanism of flexible information processing in the PFC.

This work presents novel methodology for the simultaneous modelling and forecasting of three-dimensional (3D) wind fields. This is achieved based on a quaternion domain wind model, which naturally accounts for the coupling between the dimensions of the 3D wind field. The proposed quaternion valued processing also facilitates the fusion of external atmospheric parameters, such as air temperature, exhibiting more degrees of freedom and enhanced accuracy. The quaternion least mean square (QLMS) algorithm and its variants are used for short term adaptive forecasting, and a rigorous comparative study with the corresponding algorithms in ℝ4is performed. Simulations for different wind regimes and over a range of prediction horizons support the approach.

The roles of inhibitory neurons in synchronous firing are examined in a network of excitatory and inhibitory neurons with Watts and Strogatz's rewiring. By examining the persistence of the synchronous firing that exists in the random network, it was found that there is a probability of rewiring at which a transition between the synchronous state and the asynchronous state takes place, and the dynamics of the inhibitory neurons play an important role in determining this probability.

Fast-spiking (FS) cells in the neocortex are interconnected both by inhibitory chemical synapses and by electrical synapses, or gap-junctions. Synchronized firing of FS neurons is important in the generation of gamma oscillations, at frequencies between 30 and 80 Hz. To understand how these synaptic interactions control synchronization, artificial synaptic conductances were injected in FS cells, and the synaptic phase-resetting function (SPRF), describing how the compound synaptic input perturbs the phase of gamma-frequency spiking as a function of the phase at which it is applied, was measured. GABAergic and gap junctional conductances made distinct contributions to the SPRF, which had a surprisingly simple piecewise linear form, with a sharp midcycle break between phase delay and advance. Analysis of the SPRF showed how the intrinsic biophysical properties of FS neurons and their interconnections allow entrainment of firing over a wide gamma frequency band, whose upper and lower frequency limits are controlled by electrical synapses and GABAergic inhibition respectively.

We propose a control strategy of chaotic dynamics to stabilize periodic orbits in nonlinear discrete-time dynamical systems (maps) and apply it to a chaotic neuron map model not only numerically but also experimentally by analog circuit implementation. The control method is based on an adaptive feedback adjustment of a control parameter of the system, which uses typical bifurcation structures of nonlinear dynamical systems. We can observe a clear fractal structure in the sets composed of controlled states with respect to different initial conditions. We also discuss possible applications of the controlled system as an analog-valued memory with high-capacity.

We propose a multi-hysteresis voltage controlled current source (multi-hysteresis VCCS). The multi-hysteresis VCCS consists of multiple single-hysteresis VCCSs in parallel. The multi-hysteresis VCCS can exhibit various kinds of i - v characteristics. In addition, we introduce a chaotic oscillator by applying the multi-hysteresis VCCSs. The proposed oscillator is suitable for the IC implementation. A fully-differential multiscroll chaotic oscillator circuit is designed. The SPICE simulation results confirm the multi-hysteresis characteristics and various chaotic attractors.

A widely used complex-valued activation function for complex-valued multistate Hopfield networks is revealed to be essentially based on a multilevel step function. By replacing the multilevel step function with other multilevel characteristics, we present two alternative complex-valued activation functions. One is based on a multilevel sigmoid function, while the other on a characteristic of a multistate bifurcating neuron. Numerical experiments show that both modifications to the complex-valued activation function bring about improvements in network performance for a multistate associative memory. The advantage of the proposed networks over the complex-valued Hopfield networks with the multilevel step function is more outstanding when a complex-valued neuron represents a larger number of multivalued states. Further, the performance of the proposed networks in reconstructing noisy 256 gray-level images is demonstrated in comparison with other recent associative memories to clarify their advantages and disadvantages.

A novel method for online tracking of the changes in the non- linearity within complex-valued signals is introduced. This is achieved by a collaborative adaptive signal processing approach by means of a hybrid filter. By tracking the dynamics of the adaptive mixing parameter within the employed hybrid filtering architecture, we show that it is possible to quantify the degree of nonlinearity within complex-valued data. Simulations on both benchmark and real world data support the approach.

We propose a new habituation detection system using a cross-modal computation. The cross-modal sensory data comprised of eye-movement and skin potential level (SPL) for our habituation detection system has the substantial temporal/spatial nonstationarity. Therefore, it was difficult for conventional classification methods to detect the boundary of the habituation state from the sensory data. Hence, we introduced an Allen-Cahn type partial differential equation (PDE) method to deal with the uncertainty, and developed a new real-time habituation detection system. The result demonstrates that our proposed method performs better classification of the nonstationary data than conventional methods even with a small amount of data.

Data representation methods related to ICA and sparse coding have successfully been used to model neural representation. However, they are highly abstract methods, and the neural encoding does not correspond to a detailed neuron model. This limits their power to provide deeper insight into the sensory systems on a cellular level. We propose here data representation where the encoding happens with a spiking neuron. The data representation problem is formulated as an optimization problem: Encode the input so that it can be decoded from the spike train, and optionally, so that energy consumption is minimized. The optimization leads to a learning rule for the encoder and decoder which features synergistic interaction: The decoder provides feedback affecting the plasticity of the encoder while the encoder provides optimal learning data for the decoder.

The complex-signum function has been widely used as an activation function in complex-valued recurrent neural networks for multistate associative memory. This paper presents two alternative activation functions with circularity. One is the complex-sigmoid function based on a multilevel sigmoid function defined on a circle. The other is a characteristic of a bifurcating neuron represented by a circle map. The performance of the complex-valued neural networks with the two kinds of activation functions is investigated in multistate associative memory tests. In both networks, the connection weights to store the memory patterns are determined by the generalized projection rule. We also demonstrate gray-level image reconstruction as a possible application of the proposed methods.

BACKGROUND: In general, gene function prediction can be formalized as a classification problem based on machine learning technique. Usually, both labeled positive and negative samples are needed to train the classifier. For the problem of gene function prediction, however, the available information is only about positive samples. In other words, we know which genes have the function of interested, while it is generally unclear which genes do not have the function, i.e. the negative samples. If all the genes outside of the target functional family are seen as negative samples, the imbalanced problem will arise because there are only a relatively small number of genes annotated in each family. Furthermore, the classifier may be degraded by the false negatives in the heuristically generated negative samples. RESULTS: In this paper, we present a new technique, namely Annotating Genes with Positive Samples (AGPS), for defining negative samples in gene function prediction. With the defined negative samples, it is straightforward to predict the functions of unknown genes. In addition, the AGPS algorithm is able to integrate various kinds of data sources to predict gene functions in a reliable and accurate manner. With the one-class and two-class Support Vector Machines as the core learning algorithm, the AGPS algorithm shows good performances for function prediction on yeast genes. CONCLUSION: We proposed a new method for defining negative samples in gene function prediction. Experimental results on yeast genes show that AGPS yields good performances on both training and test sets. In addition, the overlapping between prediction results and GO annotations on unknown genes also demonstrates the effectiveness of the proposed method.

The synchronous firing of neurons in a pulse-coupled neural network composed of excitatory and inhibitory neurons is analyzed. The neurons are connected by both chemical synapses and electrical synapses among the inhibitory neurons. When electrical synapses are introduced, periodically synchronized firing as well as chaotically synchronized firing is widely observed. Moreover, we find stochastic synchrony where the ensemble-averaged dynamics shows synchronization in the network but each neuron has a low firing rate and the firing of the neurons seems to be stochastic. Stochastic synchrony of chaos corresponding to a chaotic attractor is also found.

One of the major challenges for postgenomic biology is to understand how genes, proteins, and small molecules dynamically interact to form molecular networks which facilitate sophisticated biological functions. In this paper, we present a survey on recent developments on modelling molecular networks and analyzing synchronization of bio-oscillators in multicellular systems from the viewpoint of systems biology. Attention will be focused on deriving general theoretical results to understand the dynamical behaviors of biological systems based on nonlinear dynamical and control theory. Specifically, we first describe the stochastic and deterministic approaches to model molecular networks and give a brief comparison between them. Then, we explain how to construct a molecular network, in particular, a gene regulatory network with specific functions, e.g., switches and oscillators, in individual cells at the molecular level by using feedback systems, and how to model a general multicellular system with the consideration of external fluctuations and intercellular coupling to study the general cooperative behaviors for a population of bio-oscillators. Finally, as an illustrative example, a synthetic multicellular system is designed to show how synchronization is effectively achieved and how dynamics of individual cells is efficiently controlled. Some recent developments and perspectives of analysis on biological oscillations in future are also discussed.

We propose a method for the classification of matrices. We use a linear classifier with a novel regularization scheme based on the spectral l1-norm of its coefficient matrix. The spectral regularization not only provides a principled way of complexity control but also enables automatic determination of the rank of the coefficient matrix. Using the Linear Matrix Inequality technique, we formulate the inference task as a single convex optimization problem. We apply our method to the motor-imagery EEG classification problem. The method not only improves upon conventional methods in the classification performance but also determines a subspace in the signal that concentrates discriminative information without any additional feature extraction step. The method can be easily generalized to regression problems by changing the loss function. Connections to other methods are also discussed.

The chaotic neural network is studied under the threshold activated coupling, which provides a controlled output patterns. In general the chaotic neural network constructed with chaotic neurons exhibits very rich dynamic behaviors with a nonperiodic associative memory. In the chaotic neural network, it is difficult to distinguish the stored patterns from others, because of the chaotic states of output of the network. In order to apply the nonperiodic associative memory into information search and pattern recognition, etc, it is necessary to control chaos in this chaotic neural network. Chaos in the chaotic neural network is controlled with threshold activated coupling method and the network converges on one of its stored patterns or their reverses which has the smallest Hamming distance with the initial state of the network or provides a controlled neural network with spatio-temporal patterns depending upon the nature of control.

Repetitions of precise spike patterns observed both in vivo and in vitro have been reported for more than a decade. Studies on the spike volley (a pulse packet) propagating through a homogeneous feedforward network have demonstrated its capability of generating spike patterns with millisecond fidelity. This model is called the synfire chain and suggests a possible mechanism for generating repeated spike patterns (RSPs). The propagation speed of the pulse packet determines the temporal property of RSPs. However, the relationship between propagation speed and network structure is not well understood. We studied a feedforward network with Mexican-hat connectivity by using the leaky integrate-and-fire neuron model and analyzed the network dynamics with the Fokker-Planck equation. We examined the effect of the spatial pattern of pulse packets on RSPs in the network with multistability. Pulse packets can take spatially uniform or localized shapes in a multistable regime, and they propagate with different speeds. These distinct pulse packets generate RSPs with different timescales, but the order of spikes and the ratios between interspike intervals are preserved. This result indicates that the RSPs can be transformed into the same template pattern through the expanding or contracting operation of the timescale.

With spatially organized neural networks, we examined how bias and noise inputs with spatial structure result in different network states such as bumps, localized oscillations, global oscillations, and localized synchronous firing that may be relevant to, for example, orientation selectivity. To this end, we used networks of McCulloch-Pitts neurons, which allow theoretical predictions, and verified the obtained results with numerical simulations. Spatial inputs, no matter whether they are bias inputs or shared noise inputs, affect only firing activities with resonant spatial frequency. The component of noise that is independent for different neurons increases the linearity of the neural system and gives rise to less spatial mode mixing and less bistability of population activities.

Inspired by recent studies regarding dendritic computation, we constructed a recurrent neural network model incorporating dendritic lateral inhibition. Our model consists of an input layer and a neuron layer that includes excitatory cells and an inhibitory cell; this inhibitory cell is activated by the pooled activities of all the excitatory cells, and it in turn inhibits each dendritic branch of the excitatory cells that receive excitations from the input layer. Dendritic nonlinear operation consisting of branch-specifically rectified inhibition and saturation is described by imposing nonlinear transfer functions before summation over the branches. In this model with sufficiently strong recurrent excitation, on transiently presenting a stimulus that has a high correlation with feed- forward connections of one of the excitatory cells, the corresponding cell becomes highly active, and the activity is sustained after the stimulus is turned off, whereas all the other excitatory cells continue to have low activities. But on transiently presenting a stimulus that does not have high correlations with feedforward connections of any of the excitatory cells, all the excitatory cells continue to have low activities. Interestingly, such stimulus-selective sustained response is preserved for a wide range of stimulus intensity. We derive an analytical formulation of the model in the limit where individual excitatory cells have an infinite number of dendritic branches and prove the existence of an equilibrium point corresponding to such a balanced low-level activity state as observed in the simulations, whose stability depends solely on the signal-to-noise ratio of the stimulus. We propose this model as a model of stimulus selectivity equipped with self-sustainability and intensity-invariance simultaneously, which was difficult in the conventional competitive neural networks with a similar degree of complexity in their network architecture. We discuss the biological relevance of the model in a general framework of computational neuroscience.

We study a computational model of audiovisual integration by setting a Bayesian observer that localizes visual and auditory stimuli without presuming the binding of audiovisual information. The observer adopts the maximum a posteriori approach to estimate the physically delivered position or timing of presented stimuli, simultaneously judging whether they are from the same source or not. Several experimental results on the perception of spatial unity and the ventriloquism effect can be explained comprehensively if the subjects in the experiments are regarded as Bayesian observers who try to accurately locate the stimulus. Moreover, by adaptively changing the inner representation of the Bayesian observer in terms of experience, we show that our model reproduces perceived spatial frame shifts due to the audiovisual adaptation known as the ventriloquism aftereffect.

We studied the hypothesis that synaptic dynamics is controlled by three basic principles: (1) synapses adapt their weights so that neurons can effectively transmit information, (2) homeostatic processes stabilize the mean firing rate of the postsynaptic neuron, and (3) weak synapses adapt more slowly than strong ones, while maintenance of strong synapses is costly. Our results show that a synaptic update rule derived from these principles shares features, with spike-timing-dependent plasticity, is sensitive to correlations in the input and is useful for synaptic memory. Moreover, input selectivity (sharply tuned receptive fields) of postsynaptic neurons develops only if stimuli with strong features are presented. Sharply tuned neurons can coexist with unselective ones, and the distribution of synaptic weights can be unimodal or bimodal. The formulation of synaptic dynamics through an optimality criterion provides a simple graphical argument for the stability of synapses, necessary for synaptic memory.

We propose a novel framework for the classification of single trial ElectroEncephaloGraphy (EEG), based on regularized logistic regression. Framed in this robust statistical framework no prior feature extraction or outlier removal is required. We present two variations of parameterizing the regression function: (a) with a full rank symmetric matrix coefficient and (b) as a difference of two rank=1 matrices. In the first case, the problem is convex and the logistic regression is optimal under a generative model. The latter case is shown to be related to the Common Spatial Pattern (CSP) algorithm, which is a popular technique in Brain Computer Interfacing. The regression coefficients can also be topographically mapped onto the scalp similarly to CSP pro jections, which allows neuro-physiological interpretation. Simulations on 162 BCI datasets demonstrate that classification accuracy and robustness compares favorably against conventional CSP based classifiers.

MOTIVATION: Although there are significant advances on elucidating the collective behaviors on biological organisms in recent years, the essential mechanisms by which the collective rhythms arise remain to be fully understood, and further how to synchronize multicellular networks by artificial control strategy has not yet been well explored. RESULTS: A control strategy is developed to synchronize gene regulatory networks in a multicellular system when spontaneous synchronization cannot be achieved. We first construct an impulsive control system to model the process of periodically injecting coupling substances with constant or random impulsive control amounts into the common extracellular medium, and further study its effects on the dynamics of individual cells. We derive the threshold of synchronization induced by the periodic substance input. Therefore, we can synchronize the multicellular network to a specific collective behavior by changing the frequency and amplitude of the periodic stimuli. Moreover, a two-stage scheme is proposed to facilitate the synchronization in this paper. We show that the presence of the external input may also initiate different dynamics. The multicellular network of coupled repressilators is used to show the effectiveness of the proposed method. The results not only provide a perspective to understand the interactions between external stimuli and intrinsic physiological rhythms, but also may lead to development of realistic artificial control strategy and medical therapy. CONTACT: [email protected].

The study of synchronization in biological systems is essential for the understanding of the rhythmic phenomena of living organisms at both molecular and cellular levels. In this paper, by using simple dynamical systems theory, we present a novel mechanism, named transient resetting, for the synchronization of uncoupled biological oscillators with stimuli. This mechanism not only can unify and extend many existing results on (deterministic and stochastic) stimulus-induced synchrony, but also may actually play an important role in biological rhythms. We argue that transient resetting is a possible mechanism for the synchronization in many biological organisms, which might also be further used in the medical therapy of rhythmic disorders. Examples of the synchronization of neural and circadian oscillators as well as a chaotic neuron model are presented to verify our hypothesis.

In this paper, we improve performance of a chaotic neuro-computer in solving quadratic assignment problems (QAPs) by adopting an output-coding which constructs a feasible solution from analog internal-states of neurons at each iteration. Through measurements from the chaotic neuro-computer hardware, we show that we constantly obtain the optimum solution for size-10 QAPs. Furthermore, chaotic search dynamics through chaotic itinerancy is confirmed from time evolutions of a cost function and an energy function. Moreover, we observe internal states of arbitrary three neurons in a network to extract useful information on network dynamics that is effective in solving the QAPs.

Many studies of man-machine interaction using eye trackers have been tackled over recent decades. In this paper, we present a new learning system to estimate user interest with gaze sensory information. In short, a statistical learning scheme, especially the variational Bayes (VB), is incorporated for building probabilistic model parameters, dealing with the uncertainty of estimated user interest. Several computational results show how the VB can cope with user interest estimation, by selectively modeling their uncertainty.

MOTIVATION: All cell components exhibit intracellular noise on account of random births and deaths of individual molecules, and extracellular noise because of environment perturbations. Gene regulation in particular, is an inherently noisy process with transcriptional control, alternative splicing, translation, diffusion and chemical modification reactions, all of which involve stochastic fluctuations. Such stochastic noises may not only affect the dynamics of the entire system but may also be exploited by living organisms to actively facilitate certain functions, such as cooperative behavior and communication. RESULTS: We have provided a general model and an analytic tool to examine the cooperative behavior of a multicell system with both intracellular and extracellular stochastic fluctuations. A multicell system with a synthetic gene network is adopted to demonstrate the effects of noises and coupling on collective dynamics. These results establish not only a theoretical foundation but also a quantitative basis for understanding essential roles of noises on cooperative dynamics, such as synchronization and communication among cells.

We report on deterministic and stochastic evolutions of firing states through a feedforward neural network with Mexican-hat-type connectivity. The prevalence of columnar structures in a cortex implies spatially localized connectivity between neural pools. Although feedforward neural network models with homogeneous connectivity have been intensively studied within the context of the synfire chain, the effect of local connectivity has not yet been studied so thoroughly. When a neuron fires independently, the dynamics of macroscopic state variables (a firing rate and spatial eccentricity of a firing pattern) is deterministic from the law of large numbers. Possible stable firing states, which are derived from deterministic evolution equations, are uniform, localized, and nonfiring. The multistability of these three states is obtained where the excitatory and inhibitory interactions among neurons are balanced. When the presynapse-dependent variance in connection efficacies is incorporated into the network, the variance generates common noise. Then the evolution of the macroscopic state variables becomes stochastic, and neurons begin to fire in a correlated manner due to the common noise. The correlation structure that is generated by common noise exhibits a nontrivial bimodal distribution. The development of a firing state through neural layers does not converge to a certain fixed point but keeps on fluctuating.

Oscillatory and synchronized neural activities are commonly found in the brain, and evidence suggests that many of them are caused by global feedback. Their mechanisms and roles in information processing have been discussed often using purely feedforward networks or recurrent networks with constant inputs. On the other hand, real recurrent neural networks are abundant and continually receive information-rich inputs from the outside environment or other parts of the brain. We examine how feedforward networks of spiking neurons with delayed global feedback process information about temporally changing inputs. We show that the network behavior is more synchronous as well as more correlated with and phase-locked to the stimulus when the stimulus frequency is resonant with the inherent frequency of the neuron or that of the network oscillation generated by the feedback architecture. The two eigenmodes have distinct dynamical characteristics, which are supported by numerical simulations and by analytical arguments based on frequency response and bifurcation theory. This distinction is similar to the class I versus class II classification of single neurons according to the bifurcation from quiescence to periodic firing, and the two modes depend differently on system parameters. These two mechanisms may be associated with different types of information processing.

We have constructed a nerve membrane using MOSFET circuitry, which can be a basic element of an FET-based neural system. Its mechanism of action potentials generation is designed to reproduce that of the Hodgkin-Huxley equations. The responses to singlet, doublet, repetitive pulse, and sustained stimuli are analyzed to show that it exhibits similar properties to the Hodgkin-Huxley equations; namely, 1) excitable dynamics with generation of action potentials, 2) the existence of a chaotic response to periodic stimuli, and 3) Class 2 excitability. It is known that Class 2 excitability is generated by an inverted Hopf bifurcation. We have applied Hopf bifurcation theory to our nerve membrane's system equations and have shown a routine for ascertaining whether a certain parameter set generates an inverted Hopf bifurcation.

Repeated spike patterns have often been taken as evidence for the synfire chain, a phenomenon that a stable spike synchrony propagates through a feedforward network. Inter-spike intervals which represent a repeated spike pattern are influenced by the propagation speed of a spike packet. However, the relation between the propagation speed and network struc- ture is not well understood. While it is apparent that the propagation speed depends on the excitatory synapse strength, it might also be related to spike patterns. We analyze a feedforward network with Mexican-Hat- type connectivity (FMH) using the Fokker-Planck equation. We show that both a uniform and a localized spike packet are stable in the FMH in a certain parameter region. We also demonstrate that the propagation speed depends on the distinct firing patterns in the same network.

We derive an optimal learning rule in the sense of mutual information maximization for a spiking neuron model. Under the assumption of small fluctuations of the input, we find a spike-timing dependent plas- ticity (STDP) function which depends on the time course of excitatory postsynaptic potentials (EPSPs) and the autocorrelation function of the postsynaptic neuron. We show that the STDP function has both positive and negative phases. The positive phase is related to the shape of the EPSP while the negative phase is controlled by neuronal refractoriness.

It has been a matter of debate how firing rates or spatiotemporal spike patterns carry information in the brain. Recent experimental and theoretical work in part showed that these codes, especially a population rate code and a synchronous code, can be dually used in a single architecture. However, we are not yet able to relate the role of firing rates and synchrony to the spatiotemporal structure of inputs and the architecture of neural networks. In this article, we examine how feedforward neural networks encode multiple input sources in the firing patterns. We apply spike-time-dependent plasticity as a fundamental mechanism to yield synaptic competition and the associated input filtering. We use the Fokker-Planck formalism to analyze the mechanism for synaptic competition in the case of multiple inputs, which underlies the formation of functional clusters in downstream layers in a self-organizing manner. Depending on the types of feedback coupling and shared connectivity, clusters are independently engaged in population rate coding or synchronous coding, or they interact to serve as input filters. Classes of dual codings and functional roles of spike-time-dependent plasticity are also discussed.

We propose a simple neural network model to understand the dynamics of temporal pulse coding. The model is composed of coincidence-detector neurons with uniform synaptic efficacies and random pulse propagation delays. We also assume a global negative feedback mechanism which controls the network activity, leading to a fixed number of neurons firing within a certain time window. Due to this constraint, the network state becomes well defined and the dynamics equivalent to a piecewise nonlinear map. Numerical simulations of the model indicate that the latency of neuronal firing is crucial to the global network dynamics; when the timing of postsynaptic firing is less sensitive to perturbations in timing of presynaptic spikes, the network dynamics become stable and periodic, whereas increased sensitivity leads to instability and chaotic dynamics. Furthermore, we introduce a learning rule which decreases the Lyapunov exponent of an attractor and enlarges the basin of attraction.

The emergence of apparently deterministic and reproducible behaviors from highly fluctuating components in genetic networks has been attracting great attentions. However, only little has been known on the mechanisms of such emergence because of the complexity and the digital nature in genetic networks, which make it hard and untractable to analyze them by usual dynamical and stochastic methods. We propose a new method that employs a numerical evaluation of cumulants and a graphical representation. This method visually describes propagations of fluctuations in genetic networks and facilitates intuitive understanding of stochastic properties of the networks. In addition, this method works well even if the networks consist of many components and reactions and the copy numbers of some components are low.

We propose a mixed analog/digital system architecture of a chaos driven exponential tabu search for quadratic assignment problems. We construct a small size evaluation system using switched-capacitor chaotic neuron ICs and programmable logic devices. The experimental results from the system verify the validity and hardware compatibility of the proposed architecture.

We consider the decentralized motor control in units of motor units. This idea is motivated by the concept of Sherrington, so that "our movements are made with various spinal reflexes", which seems well-suited to the result in physiology and is, in some sense, to the point of understanding the functions or cerebellum motor control. We propose a decentralized version of feedback error learning method and analyze its stability. The efficiency of the proposed algorithm is shown by numerical simulations.

We propose an integrated neuron circuit for an asynchronous pulse neural network model. This circuit is suitable for an implementation of a wide range of spatio-temporal coding networks since the circuit can function as both a coincidence detector and an integrator of the input pulses by properly setting bias voltage. We fabricate a prototype chip for the proposed circuit using MOSIS HP/Agilient 0.5 /spl mu/m CMOS semiconductor process. The experimental measurements from the chip confirm that the integrated neuron circuit qualitatively replicates the behavior of the model. Especially, coincidence detection of input pulses in a short-time window, and further, complex behavior including chaos in the internal state value and in the interspike intervals of the output pulses are illustrated.

A functional role for precise spike timing has been proposed as an alternative hypothesis to rate coding. We show in this article that both the synchronous firing code and the population rate code can be used dually in a common framework of a single neural network model. Furthermore, these two coding mechanisms are bridged continuously by several modulatable model parameters, including shared connectivity, feedback strength, membrane leak rate, and neuron heterogeneity. The rates of change of these parameters are closely related to the response time and the timescale of learning.

Neuronal information processing is often studied on the basis of spiking patterns. The relevant statistics such as firing rates calculated with the peri-stimulus time histogram are obtained by averaging spiking patterns over many experimental runs. However, animals should respond to one experimental stimulation in real situations, and what is available to the brain is not the trial statistics but the population statistics. Consequently, physiological ergodicity, namely, the consistency between trial averaging and population averaging, is implicitly assumed in the data analyses, although it does not trivially hold true. In this letter, we investigate how characteristics of noisy neural network models, such as single neuron properties, external stimuli, and synaptic inputs, affect the statistics of firing patterns. In particular, we show that how high membrane potential sensitivity to input fluctuations, inability of neurons to remember past inputs, external stimuli with large variability and temporally separated peaks, and relatively few contributions of synaptic inputs result in spike trains that are reproducible over many trials. The reproducibility of spike trains and synchronous firing are contrasted and related to the ergodicity issue. Several numerical calculations with neural network examples are carried out to support the theoretical results.

We propose a neuron-synapse integrated circuit (IC) chip-set for large-scale chaotic neural networks. We use switched-capacitor (SC) circuit techniques to implement a three-internal-state transiently-chaotic neural network model. The SC chaotic neuron chip faithfully reproduces complex chaotic dynamics in real numbers through continuous state variables of the analog circuitry. We can digitally control most of the model parameters by means of programmable capacitive arrays embedded in the SC chaotic neuron chip. Since the output of the neuron is transfered into a digital pulse according to the all-or-nothing property of an axon, we design a synapse chip with digital circuits. We propose a memory-based synapse circuit architecture to achieve a rapid calculation of a vast number of weighted summations. Both of the SC neuron and the digital synapse circuits have been fabricated as IC forms. We have tested these IC chips extensively, and confirmed the functions and performance of the chip-set. The proposed neuron-synapse IC chip-set makes it possible to construct a scalable and reconfigurable large-scale chaotic neural network with 10000 neurons and 10000/sup 2/ synaptic connections.

Interspike intervals of spikes emitted from an integrator neuron model of sensory neurons can encode input information represented as a continuous signal from a deterministic system. If a real brain uses spike timing as a means of information processing, other neurons receiving spatiotemporal spikes from such sensory neurons must also be capable of treating information included in deterministic interspike intervals. In this article, we examine functions of neurons modeling cortical neurons receiving spatiotemporal spikes from many sensory neurons. We show that such neuron models can encode stimulus information passed from the sensory model neurons in the form of interspike intervals. Each sensory neuron connected to the cortical neuron contributes equally to the information collection by the cortical neuron. Although the incident spike train to the cortical neuron is a superimposition of spike trains from many sensory neurons, it need not be decomposed into spike trains according to the input neurons. These results are also preserved for generalizations of sensory neurons such as a small amount of leak, noise, inhomogeneity in firing rates, or biases introduced in the phase distributions.

Chaotic dynamics and its possible applications are considered from the viewpoint of engineering. Various applications, even to consumer products such as household appliances, are developing in the field of chaos engineering. In particular we review parallel distributed processing with spatio-temporal chaos on the basis of a model of chaotic neural networks.

Although various means of information representation in the cortex have been considered, the fundamental mechanism for such representation is not well understood. The relation between neural network dynamics and properties of information representation needs to be examined. We examined spatial pattern properties of mean firing rates and spatiotemporal spikes in an interconnected spiking neural network model. We found that whereas the spatiotemporal spike patterns are chaotic and unstable, the spatial patterns of mean firing rates (SPMFR) are steady and stable, reflecting the internal structure of synaptic weights. Interestingly, the chaotic instability contributes to fast stabilization of the SPMFR. Findings suggest that there are two types of network dynamics behind neuronal spiking: internally-driven dynamics and externally driven dynamics. When the internally driven dynamics dominate, spikes are relatively more chaotic and independent of external inputs; the SPMFR are steady and stable. When the externally driven dynamics dominate, the spiking patterns are relatively more dependent on the spatiotemporal structure of external inputs. These emergent properties of information representation imply that the brain may adopt a dual coding system. Recent experimental data suggest that internally driven and externally driven dynamics coexist and work together in the cortex.

Defines a model formulated by differential-difference-algebraic equations (DDA) as a hybrid dynamical system (HDS) or constrained sampled-data model. So far, both continuous-time and discrete-time nonlinear systems have attracted considerable attention and a variety of techniques have been developed. In contrast, however, the researches for the hybrid systems are mainly for the systems defined by both differential equations with continuous states and logical-discrete-event equations with discrete states. On the other hand, the sampled-data models or differential-difference equations where all of the states are continuous are investigated mostly for the linear systems. Less attention has been focused on the nonlinear analysis of the DDA or the hybrid dynamical systems where the differential and the difference equations not only have continuous states but also are constrained by algebraic equations. As part of a continuing series of works which attempt to elucidate the properties of HDS following the analysis of the asymptotical stability in previous papers, this paper aims at analysing bifurcations of HDS and further applying the theoretical results to digital control of power systems.