Natsuhiro Ichinose
University of Tokyo
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Publication
Featured researches published by Natsuhiro Ichinose.
International Journal of Bifurcation and Chaos | 1998
Natsuhiro Ichinose; Kazuyuki Aihara; Kevin Judd
In this paper, we study an excitable and nonoscillating neuron on the basis of a technique of extending the concept of isochrons from oscillatory to excitable systems. The extended isochrons allow reduction of an excitable system described by possibly high dimensional differential equations to a simpler system. We analytically derive a one-dimensional model of an excitable neuron stimulated by instantaneous pulses with the technique of the extended isochrons and show its similarity to an isochronal map numerically obtained from the FitzHugh–Nagumo model. Response characteristics of the one-dimensional model to periodic impulsive stimulations are also analyzed numerically.
Artificial Life and Robotics | 1999
Kazuyuki Aihara; Natsuhiro Ichinose
In this paper, we study nonlinear spatio-temporal dynamics in synchronous and asynchronous chaotic neural networks from the viewpoint of the modeling and complexity of the dynamic brain. First, the possible roles and functions of spatio-temporal neurochaos are considered with a model of synchronous chaotic neural networks composed of a neuron model with a chaotic map. Second, deterministic point-process dynamics with spikes of action potentials is demonstrated with a biologically more plausible model of asynchronous chaotic neural networks. Last, the possibilities of inventing a new brain-type of computing system are discussed on the basis of these models of chaotic neural networks.
Biological Cybernetics | 2001
Natsuhiro Ichinose; Kazuyuki Aihara
Abstract. A method for detecting mutual deterministic dependence between a pair of spike trains is proposed. When it is assumed that a cell assembly, which is a subgroup of neurons processing a common task, is constituted as a dynamical system, then the mutual determinism between constituent neurons may be directly reflected in functional connectivity in the assembly. The deterministic dependence between two spike trains can be measured with statistical significance using a method of nonlinear prediction. Some examples of simulations are demonstrated in both deterministic and stochastic cases.
PLOS ONE | 2017
Natsuhiro Ichinose; Tetsushi Yada; Hiroshi Wada
To estimate gene regulatory networks, it is important that we know the number of connections, or sparseness of the networks. It can be expected that the robustness to perturbations is one of the factors determining the sparseness. We reconstruct a semi-quantitative model of gene networks from gene expression data in embryonic development and detect the optimal sparseness against perturbations. The dense networks are robust to connection-removal perturbation, whereas the sparse networks are robust to misexpression perturbation. We show that there is an optimal sparseness that serves as a trade-off between these perturbations, in agreement with the optimal result of validation for testing data. These results suggest that the robustness to the two types of perturbations determines the sparseness of gene networks.
international symposium on neural networks | 1995
Natsuhiro Ichinose; Kazuyuki Aihara; Yoichi Okabe
A coincidence detector is a neuron model which has a type of dendrites whose time constant is small, then a single pulse is more significant than the case of integrator neurons and the neuron can detect synchronization of pulses from distinct connections. Our aim of this study is to investigate the characteristics not of a single or a few neurons, but of larger networks with the coincidence detectors. Networks responding to one or two sequences of pulses are constructed by selecting suitable connections in random-connected networks. It is shown that the selected networks can response-only to a specific sequence of pulses even if noisy pulses are superimposed on it. It is also shown that the activation of some neurons in the networks can be partitioned for an alternative sequence by observing cross-correlations of output pulses, when two sequences of pulses are supplied to the networks simultaneously.
Neural Networks | 1996
Hiroshi Fujii; Hiroyuki Ito; Kazuyuki Aihara; Natsuhiro Ichinose; Minoru Tsukada
Electronics and Communications in Japan Part Iii-fundamental Electronic Science | 1995
Natsuhiro Ichinose; Kazuyuki Aiharai
Genome Informatics | 2001
Natsuhiro Ichinose; Tetsushi Yada; Toshihisa Takagi
The Brain & Neural Networks | 1997
Natsuhiro Ichinose; Kazuyuki Aihara; Yoichi Okabe
Physical Review E | 2018
Natsuhiro Ichinose; Tetsushi Yada; Hiroshi Wada