Jian-Qin Liu's research while affiliated with National Institute of Information and Communications Technology and other places

In molecular biology of the cell, cell communication is defined as the process carried out by chemical signals within and among cells. The informatics issue of cell communication in this book chapter is to uncover the principles of the bioinformatics of cell communication by means of communication engineering, e.g., the statistical tool for performance analysis of communication processes. As we know well by now, the state of the art of molecular science has been reshaped by advanced technologies since the genome sequencing became a reality. In accordance with nowadays available nanotechnology for molecular signal detection, we apply communication engineering technology in the theoretical analysis of cell communication whose goal is to discover the mechanism of cell communication that determines the cellular functions connected with applications in medicine. Though intensive research has been devoted to the biochemistry of signaling pathways, laying a strong scientific foundation for the informatics study of communication processes of the cell in the form of signaling pathways, the study of the communication mechanism of signaling pathway networks in the cell—cell communication—by means of communication engineering is still a relatively new field, where supporting technologies from multiple disciplines are needed. In this book chapter, the formulation of the cell communication mechanism of signaling pathway networks using martingale measures for random processes is proposed and the performance of the cell communication system constructed by the signaling pathways in simulation studies is evaluated from the viewpoint of communication engineering. From the computational analysis result of the above cell communication process, it is concluded that the modeling method in this study not only is efficient for bioinformatics analysis of biological cell communication processes but also provides a reference framework for brain communication towards its application in molecular biomedical engineering.

The SNARE-regulated exocytosis is an important molecular level mechanism for the persistent inter-neuronal communication via plasticity of neurons. Without considering the dissipation of synaptic vesicle, the explanation of the inter-neuronal communication via plasticity of neurons in existing models cannot fully account for the persistence of neuronal plasticity. In this paper the dissipative factors CPX and SYT corresponding to unbinding neurotransmitters fused with the membrane are estimated using a reverse engineering method where the dynamic flux is embedded in the filtering model of synaptic transmission. With the CPX and SYT in the corresponding regulation mechanism of exocytosis being estimated, the dissipation function of synaptic vesicle for the persistence of neuronal plasticity is explicitly expressed.

Izhikevich neuron model, which combines continuous spike-generation mechanisms and discontinuous resetting process after spiking, can reproduce almost all spiking activities including chaotic spiking in actual neural systems. When the chaotic state is evaluated in this model, it is known that conventional Lyapunov exponent where the continuous trajectory is presupposed cannot be applied due to the state dependent jump in the resetting process. To evaluate Lyapunov exponent in the system with the resetting process, the accurate numerical calculation for the trajectory by Newton method and the consideration for saltation matrix are needed. By virtue of this method, several routes to chaos have been found in Izhikevich neuron model. While on the other hand, in this study,we have proposed the method combining Euler method and Lyapunov exponent on Poincaré section and evaluated the chaotic state in Izhikevich neuron model. As the result, it has been confirmed that this method can also judge the chaotic state by tuning the initial perturbation against the trajectory.

Chaotic resonance (CR) is a phenomenon similar to stochastic resonance (SR), but without stochastic noise. SR, in which the presence of noise helps a nonlinear system in amplifying a weak (under-barrier) signal, has been observed in neural systems. However, there has been no fundamental study that investigates the signal responses of CR in the spiking neural system. In this chapter, focusing on the Izhikevich neuron model, which can reproduce major spike patterns observed experimentally, we reveal the properties of its chaotic dynamical states and the signal response in CR. Through computer simulations, we have confirmed that both strong and weak chaotic states in CR can respond to the weak signal sensitively, and moreover, the weak chaotic state has a much prompter response than the strong chaotic state.

Considering the role of molecular mechanism of cellular signal transduction as information networking for cell communication, the information processing mechanisms of cellular signal transduction and TCP congestion control are compared by the method of information networking.

In stochastic resonance (SR), the presence of noise helps a nonlinear system amplify a weak (sub-threshold) signal. Chaotic resonance (CR) is a phenomenon similar to SR but without stochastic noise, which has been observed in neural systems. However, no study to date has investigated and compared the characteristics and performance of the signal responses of a spiking neural system in some chaotic states in CR. In this paper, we focus on the Izhikevich neuron model, which can reproduce major spike patterns that have been experimentally observed. We examine and classify the chaotic characteristics of this model by using Lyapunov exponents with a saltation matrix and Poincaré section methods in order to address the measurement challenge posed by the state-dependent jump in the resetting process. We found the existence of two distinctive states, a chaotic state involving primarily turbulent movement and an intermittent chaotic state. In order to assess the signal responses of CR in these classified states, we introduced an extended Izhikevich neuron model by considering weak periodic signals, and defined the cycle histogram of neuron spikes as well as the corresponding mutual correlation and information. Through computer simulations, we confirmed that both chaotic states in CR can sensitively respond to weak signals. Moreover, we found that the intermittent chaotic state exhibited a prompter response than the chaotic state with primarily turbulent movement.

Several hybrid neuron models, which combine continuous spike-generation mechanisms and discontinuous resetting process after spiking, have been proposed as a simple transition scheme for membrane potential between spike and hyperpolarization. As one of the hybrid spiking neuron models, Izhikevich neuron model can reproduce major spike patterns observed in the cerebral cortex only by tuning a few parameters and also exhibit chaotic states in specific conditions. However, there are a few studies concerning the chaotic states over a large range of parameters due to the difficulty of dealing with the state dependent jump on the resetting process in this model. In this study, we examine the dependence of the system behavior on the resetting parameters by using Lyapunov exponent with saltation matrix and Poincaré section methods, and classify the routes to chaos.

We present a fundamental model that treats the problem of synchronicity of path switches in a packet flow network. The model assumes a network with multiple concurrent multihop flows. Each flow source monitors its flow path for a certain period of time, and then randomly selects a new flow path if the current path is congested. We examine the effect of having synchronized switch cycles in a basic two-hop network topology. It is shown that there can be a large difference in flow characteristics for flows with synchronized in-phase and antiphase switching cycles. The effect is shown as a large difference in the packet delay time distributions around a critical value of load parameter.

In order to better understand neurodegenerative diseases, characterization of neuronal activities at different regions correlated with the resting state brain activity, which indicates the normality of the spontaneous cognition, to sustain the memory of the brain is needed. The signaling dynamics of these neuronal activities are determined by the so-called default mode network consisting of nodes overlapped with the major functional regions of the brain for task-driven cognition and links among the nodes with correlation coefficients. In the case that the brain's signaling system is an integrated network system, which integrates several sub-systems - molecular, cellular, and cognitive networks, and the signaling network of the brain exists as a complex network that systematically determines the function of the brain, how can the coupling relationship among these interacted sub-systems be modelled and quantitatively analyzed. To answer this question, we propose a new modeling method based on the framework of a systems approach extended from probabilistic computational neuro-genetic modeling (pCNGM), which maps individual neuronal signals at molecular level to the brain's signals at regional level. We use the extended pCNGM model where a new theoretical-physics-inspired data structure is used to quantitatively analyze the region-level correlation of the brain which indicates a kind of synchronization and is used as an indicator of quantitative analysis of neuro-dynamics. The results show that the coupling constrained by structural dynamics with quantum effects embedded in the brain's network plays a pivotal role in the synchronization among regional nodes within the brain's spontaneous signaling network.