A model for feature linking via collective oscillations in the primary visual cortex. - Tsuyoshi Chawanya; Toshio Aoyagi; Ikuko Nishikawa; Koji Okuda; Yoshiki Kuramoto (Biol. Cybern., 1993)

Retrieval Dynamics in Oscillator Neural Networks. - Toshio Aoyagi; Katsunori Kitano (Neural Comput., 1998)

A bursting mechanism of chattering neurons based on Ca2+-dependent cationic currents. - Toshio Aoyagi; Nobuhiko Terada; Youngnam Kang; Takeshi Kaneko; Tomoki Fukai (Neurocomputing, 2001)

Synchronous and asynchronous activities in a network model of the striatal spiny projection neurons. - Katsunori Kitano; Toshio Aoyagi; Tomoki Fukai (Neurocomputing, 2001)

Modeling the layer V cortical pyramidal neurons showing theta-rhythmic firing in the presence of muscarine. - Tomoki Fukai; Katsunori Kitano; Toshio Aoyagi; Youngnam Kang (Neurocomputing, 2002)

Gamma Rhythmic Bursts: Coherence Control in Networks of Cortical Pyramidal Neurons. - Toshio Aoyagi; Takashi Takekawa; Tomoki Fukai (Neural Comput., 2003)

Synchrony of Fast-Spiking Interneurons Interconnected by GABAergic and Electrical Synapses. - Masaki Nomura; Tomoki Fukai; Toshio Aoyagi (Neural Comput., 2003)

Two-level hierarchy with sparsely and temporally coded patterns and its possible functional role in information processing. - Masaki Nomura; Toshio Aoyagi; Masato Okada (Neural Networks, 2003)

Possible role of synchronous input spike trains in controlling the function of neural networks. - Toshio Aoyagi; Takaaki Aoki (Neurocomputing, 2004)

Gamma frequency synchronization in a local cortical network model. - Masaki Nomura; Toshio Aoyagi; Tomoki Fukai (Neurocomputing, 2004)

Ordering Process of Self-Organizing Maps Improved by Asymmetric Neighborhood Function. - Takaaki Aoki; Kaiichiro Ota; Koji Kurata; Toshio Aoyagi (ICONIP (1), 2007)

Synchronous and asynchronous bursting states: role of intrinsic neural dynamics. - Takashi Takekawa; Toshio Aoyagi; Tomoki Fukai (J. Comput. Neurosci., 2007)

Synchrony-Induced Attractor Transition in Cortical Neural Networks Organized by Spike-Timing Dependent Plasticity. - Takaaki Aoki; Toshio Aoyagi (J. Robotics Mechatronics, 2007)

Analysis of Multineuron Activity Using the Kernel Method. - Masaki Nomura; Yoshio Sakurai; Toshio Aoyagi (J. Robotics Mechatronics, 2007)

Self-Organizing Maps with Asymmetric Neighborhood Function. - Takaaki Aoki; Toshio Aoyagi (Neural Comput., 2007)

Synchrony-Induced Switching Behavior of Spike Pattern Attractors Created by Spike-Timing-Dependent Plasticity. - Takaaki Aoki; Toshio Aoyagi (Neural Comput., 2007)

Recurrent Infomax Generates Cell Assemblies, Neuronal Avalanches, and Simple Cell-Like Selectivity. - Takuma Tanaka; Takeshi Kaneko; Toshio Aoyagi (Neural Comput., 2009)

Bayesian estimation of phase response curves. - Ken Nakae; Yukito Iba; Yasuhiro Tsubo; Tomoki Fukai; Toshio Aoyagi (Neural Networks, 2010)

Replicating Receptive Fields of Simple and Complex Cells in Primary Visual Cortex in a Neuronal Network Model with Temporal and Population Sparseness and Reliability. - Takuma Tanaka; Toshio Aoyagi; Takeshi Kaneko (Neural Comput., 2012)

A biologically plausible learning rule for the Infomax on recurrent neural networks. - Takashi Hayakawa; Takeshi Kaneko; Toshio Aoyagi (Frontiers Comput. Neurosci., 2014)

Evaluation of the Phase-Dependent Rhythm Control of Human Walking Using Phase Response Curves. - Tetsuro Funato; Yuki Yamamoto; Shinya Aoi; Takashi Imai; Toshio Aoyagi; Nozomi Tomita; Kazuo Tsuchiya (PLoS Comput. Biol., 2016)

Bayesian Estimation of Phase Dynamics Based on Partially Sampled Spikes Generated by Realistic Model Neurons. - Kento Suzuki; Toshio Aoyagi; Katsunori Kitano (Frontiers Comput. Neurosci., 2017)

Use of recurrent infomax to improve the memory capability of input-driven recurrent neural networks. - Hisashi Iwade; Kohei Nakajima; Takuma Tanaka; Toshio Aoyagi (CoRR, 2018)

A dynamical systems approach for estimating phase interactions between rhythms of different frequencies from experimental data. - Takayuki Onojima; Takahiro Goto; Hiroaki Mizuhara; Toshio Aoyagi (PLoS Comput. Biol., 2018)

Neuronal correlations shape the scaling behavior of memory capacity and nonlinear computational capability of recurrent neural networks. - Shotaro Takasu; Toshio Aoyagi (CoRR, 2025)