Shigeru Shinomoto

发表

Estimating inputs and an internal neuronal parameter from a single spike train

Shigeru Shinomoto, Hideaki Kim, 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

Estimating time-varying input signals and ion channel states from a single voltage trace of a neuron

Shigeru Shinomoto, Ryota Kobayashi, Yasuhiro Tsubo, 2011, NIPS.

Information Transmission Using Non-Poisson Regular Firing

Robert E. Kass, Shigeru Shinomoto, Shinsuke Koyama, 2013, Neural Computation.

A recipe for optimizing a time-histogram

Shigeru Shinomoto, Hideaki Shimazaki, S. Shinomoto, 2006, NIPS.

A fast-computational spiking neuron model adaptable to any cortical neuron

Shigeru Shinomoto, Ryota Kobayashi, Yasuhiro Tshubo, 2009, BMC Neuroscience.

Independent of Pursuit Eye Movements MST Neurons Code for Visual Motion in Space

Aya Takemura, Shigeru Shinomoto, Shigeru Yamane, 2007 .

A characterization of the time-rescaled gamma process as a model for spike trains

Shigeru Shinomoto, Shinsuke Koyama, Takeaki Shimokawa, 2010, Journal of Computational Neuroscience.

A Recipe for Making a Time Histogram with an Optimal Bin Width from Spike Sequences

Shigeru Shinomoto, Hideaki Shimazaki, 2005 .

Kernel bandwidth optimization in spike rate estimation

Shigeru Shinomoto, Hideaki Shimazaki, S. Shinomoto, 2009, Journal of Computational Neuroscience.

Information classification acquired by organization of neuronic connections

Shigeru Shinomoto, S. Shinomoto, 1990 .

A Method for Selecting the Bin Size of a Time Histogram

Shigeru Shinomoto, Hideaki Shimazaki, S. Shinomoto, 2007, Neural Computation.

Online learning of a decision boundary with and without queries

Yoshiyuki Kabashima, Shigeru Shinomoto, Y. Kabashima, 1999 .

A solution to the controversy between rate and temporal coding

Shigeru Shinomoto, Shinsuke Koyama, S. Shinomoto, 2007, Statistics in medicine.

Emergence of event cascades in inhomogeneous networks

Shigeru Shinomoto, Tomokatsu Onaga, S. Shinomoto, 2016, Scientific Reports.

Identifying exogenous and endogenous activity in social media pdf

Renaud Lambiotte, Shigeru Shinomoto, Shinsuke Koyama, 2018, Physical Review E.

A convolutional neural network for estimating synaptic connectivity from spike trains

Kenji Kawano, Barry J. Richmond, Shigeru Shinomoto, 2020, Scientific Reports.

Neurons as ideal change-point detectors

Barry J. Richmond, Shigeru Shinomoto, Hideaki Kim, 2011, Journal of Computational Neuroscience.

Estimating Instantaneous Irregularity of Neuronal Firing

Shigeru Shinomoto, Takeaki Shimokawa, S. Shinomoto, 2009, Neural Computation.

Relating Neuronal Firing Patterns to Functional Differentiation of Cerebral Cortex

Kikuro Fukushima, Kenji Kawano, Kowa Koida, 2009, PLoS Comput. Biol..

Estimation of Neuronal Firing Rate

Shigeru Shinomoto, S. Shinomoto, 2014, Encyclopedia of Computational Neuroscience.

Elemental Spiking Neuron Model for Reproducing Diverse Firing Patterns and Predicting Precise Firing Times

Shigeru Shinomoto, Hideaki Kim, Satoshi Yamauchi, 2011, Front. Comput. Neurosci..

Predicting spike timings of current-injected neurons

Takeshi Kaneko, Shigeru Shinomoto, Yasuhiro Tsubo, 2004, Neural Networks.

New classification scheme of cortical sites with the neuronal spiking characteristics

Shigeru Shinomoto, Jun Tanji, Keisetsu Shima, 2002, Neural Networks.

Estimation of Time-Dependent Input from Neuronal Membrane Potential

Shigeru Shinomoto, Ryota Kobayashi, Petr Lánský, 2011, Neural Computation.

Made-to-Order Spiking Neuron Model Equipped with a Multi-Timescale Adaptive Threshold

Shigeru Shinomoto, Ryota Kobayashi, Yasuhiro Tsubo, 2009, Front. Comput. Neurosci..

A memory with cognitive ability

Shigeru Shinomoto, 1988 .

MST neurons code for visual motion in space independent of pursuit eye movements.

Aya Takemura, Kenji Kawano, Shigeru Shinomoto, 2007, Journal of neurophysiology.

Reverberating activity in a neural network with distributed signal transmission delays.

Shigeru Shinomoto, Takahiro Omi, S. Shinomoto, 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

Differences in Spiking Patterns Among Cortical Neurons

Shigeru Shinomoto, Jun Tanji, Keisetsu Shima, 2003, Neural Computation.

Analog and digital codes in the brain.

Shigeru Shinomoto, Yasuhiro Mochizuki, S. Shinomoto, 2013, Physical review. E, Statistical, nonlinear, and soft matter physics.

Four Types of Learning Curves

Shun-ichi Amari, Shigeru Shinomoto, Naotake Fujita, 1992, Neural Computation.

Difference in modes of firing rate modulation between cortical areas

Shigeru Shinomoto, Yasuhiro Mochizuki, 2013, BMC Neuroscience.

Bursting Activity Spreading through Asymmetric Interactions

Shigeru Shinomoto, Tomokatsu Onaga, 2014, 2014 Tenth International Conference on Signal-Image Technology and Internet-Based Systems.

Reconstructing neuronal circuitry from parallel spike trains

Katsunori Kitano, Markus Diesmann, Barry J. Richmond, 2019, Nature Communications.

Deciphering Elapsed Time and Predicting Action Timing from Neuronal Population Signals

Hajime Mushiake, Shigeru Shinomoto, Jun Tanji, 2011, Front. Comput. Neurosci..

Inference of intrinsic spiking irregularity based on the Kullback-Leibler information

Shigeru Shinomoto, Shinsuke Koyama, 2007, Biosyst..

Spiking mechanisms of cortical neurons

Yutaka Sakai, Shigeru Shinomoto, S. Shinomoto, 1998 .

Estimating nonstationary inputs from a single spike train based on a neuron model with adaptation.

Shigeru Shinomoto, Hideaki Kim, S. Shinomoto, 2013, Mathematical biosciences and engineering : MBE.

Precise hyperacuity estimation of spike timing from calcium imaging

Yuji Ikegaya, Mitsuo Kawato, Shigeru Shinomoto, 2019, bioRxiv.

Mechanical classification of voice quality

Shigeru Shinomoto, Akito Yoshida, S. Shinomoto, 2020, 2003.13033.

Learning a Decision Boundary from Stochastic Examples: Incremental Algorithms with and without Queries

Yoshiyuki Kabashima, Shigeru Shinomoto, Y. Kabashima, 1995, Neural Computation.

Temporally correlated inputs to leaky integrate-and-fire models can reproduce spiking statistics of cortical neurons

Yutaka Sakai, Shigeru Shinomoto, Shintaro Funahashi, 1999, Neural Networks.

Optimizing Time Histograms for Non-Poissonian Spike Trains

Shigeru Shinomoto, Takahiro Omi, S. Shinomoto, 2011, Neural Computation.

Fitting a stochastic spiking model to neuronal current injection data

Shigeru Shinomoto, S. Shinomoto, 2010, Neural Networks.

Histogram binwidth and kernel bandwidth selection for the spike-rate estimation

Shigeru Shinomoto, Hideaki Shimazaki, 2009, BMC Neuroscience.

Computational Neuroscience: Mathematical and Statistical Perspectives.

Sonja Grün, Tomoki Fukai, Eric Shea-Brown, 2018, Annual review of statistics and its application.

Neuronal firing patterns and cerebral cortical functions

Shigeru Shinomoto, Hideaki Kim, Takeaki Shimokawa, 2009, BMC Neuroscience.

Learning Curves for Error Minimum and Maximum Likelihood Algorithms

Yoshiyuki Kabashima, Shigeru Shinomoto, 1992, Neural Computation.

Information classification scheme of feedforward networks organised under unsupervised learning

Shigeru Shinomoto, S. Shinomoto, 1990 .

State space method for predicting the spike times of a neuron.

Shigeru Shinomoto, Ryota Kobayashi, S. Shinomoto, 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

The Ornstein-Uhlenbeck Process Does Not Reproduce Spiking Statistics of Neurons in Prefrontal Cortex

Yutaka Sakai, Shigeru Shinomoto, Shintaro Funahashi, 1999, Neural Computation.

Reconstructing neuronal circuitry from parallel spike trains

Katsunori Kitano, Barry J. Richmond, Shigeru Shinomoto, 2018, Nature Communications.

Inferring objects from a multitude of oscillations

Shigeru Shinomoto, Masahiro Furukawa, 2016, Neural Computing and Applications.

Bayesian estimation of nonstationary parameters of a single spike train

Shigeru Shinomoto, Takeaki Shimokawa, 2009, Neuroscience Research.

Inter-spike Interval Statistics of Cortical Neurons

Yutaka Sakai, Shigeru Shinomoto, S. Shinomoto, 1999, IWANN.

Predicting spike times from subthreshold dynamics of a neuron

Shigeru Shinomoto, Ryota Kobayashi, 2006, NIPS.

Acceleration of learning in binary choice problems

Yoshiyuki Kabashima, Shigeru Shinomoto, 1993, COLT '93.

Bursting transition in a linear self-exciting point process.

Shigeru Shinomoto, Tomokatsu Onaga, 2014, Physical review. E, Statistical, nonlinear, and soft matter physics.

A non-universal aspect in the temporal occurrence of earthquakes

Shigeru Shinomoto, Takahiro Omi, Nanae Matsuno, 2010 .

Kernel Width Optimization in the Spike-rate Estimation

Shigeru Shinomoto, Hideaki Shimazaki, S. Shinomoto, 2007 .

Yutaka Sakai, Sonja Grün, Rie Kimura, 2016, The Journal of Neuroscience.

of Statistics and Its Application Computational Neuroscience : Mathematical and Statistical Perspectives

Emery N. Brown, Sonja Grün, Tomoki Fukai, 2018 .

Phase Transitions and Their Bifurcation Analysis in a Large Population of Active Rotators with Mean-Field Coupling

Shigeru Shinomoto, Hidetsugu Sakaguchi, Yoshiki Kuramoto, 1988 .

Estimating nonstationary input signals from a single neuronal spike train.

Shigeru Shinomoto, Hideaki Kim, S. Shinomoto, 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

Stimulus‐dependent induction of long‐term potentiation in CA1 area of the hippocampus: Experiment and model

Takeshi Aihara, Minoru Tsukada, Shigeru Shinomoto, 1997, Hippocampus.

Inhibitory neurons can facilitate rhythmic activity in a neural network.

Shigeru Shinomoto, Takeaki Shimokawa, S. Shinomoto, 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

Local and Grobal Self-Entrainments in Oscillator Lattices

Shigeru Shinomoto, Hidetsugu Sakaguchi, Yoshiki Kuramoto, 1987 .

Detection limit for rate fluctuations in inhomogeneous Poisson processes.

Shigeru Shinomoto, Toshiaki Shintani, 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

Histogram bin width selection for time-dependent Poisson processes

Shigeru Shinomoto, Shinsuke Koyama, S. Shinomoto, 2004 .

Phase transitions in the estimation of event rate: a path integral analysis

Shigeru Shinomoto, Shinsuke Koyama, Takeaki Shimokawa, 2007 .

Estimating the Firing Rate

Shigeru Shinomoto, S. Shinomoto, 2010 .

The Ornstein-Uhlenbeck process does not reproduce spiking statistics of cortical neurons

Shigeru Shinomoto, Shintaro Funahashi, S. Shinomoto, 1997 .

Empirical Bayes interpretations of random point events

Shigeru Shinomoto, Shinsuke Koyama, S. Shinomoto, 2005 .

Optimal observation time window for forecasting the next earthquake.

Ido Kanter, Shigeru Shinomoto, Takahiro Omi, 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

Mutual Entrainment in Oscillator Lattices with Nonvariational Type Interaction

Shigeru Shinomoto, Hidetsugu Sakaguchi, Yoshiki Kuramoto, 1988 .

Cooperative Phenomena in Two-Dimensional Active Rotator Systems

Shigeru Shinomoto, Yoshiki Kuramoto, Y. Kuramoto, 1986 .

Recording site dependence of the neuronal spiking statistics.

Yutaka Sakai, Hiroshi Ohno, Shigeru Shinomoto, 2002, Bio Systems.