Inter-spike Interval Statistics of Cortical Neurons. - Shigeru Shinomoto; Yutaka Sakai (IWANN (1), 1999)

The Ornstein-Uhlenbeck Process Does Not Reproduce Spiking Statistics of Neurons in Prefrontal Cortex. - Shigeru Shinomoto; Yutaka Sakai; Shintaro Funahashi (Neural Comput., 1999)

Temporally correlated inputs to leaky integrate-and-fire models can reproduce spiking statistics of cortical neurons. - Yutaka Sakai; Shintaro Funahashi; Shigeru Shinomoto (Neural Networks, 1999)

Neuronal integration mechanisms have little effect on spike auto-correlations of cortical neurons. - Yutaka Sakai (Neural Networks, 2001)

Synchronous Firing and Higher-Order Interactions in Neuron Pool. - Shun-ichi Amari; Hiroyuki Nakahara; Si Wu 001; Yutaka Sakai (Neural Comput., 2003)

Synaptic regulation on various STDP rules. - Yutaka Sakai; Kaoru Nakano; Shuji Yoshizawa (Neurocomputing, 2004)

Different Responses of Two Types of Class II Neurons for Fluctuated Inputs. - Ryosuke Hosaka; Yutaka Sakai; Tohru Ikeguchi; Shuji Yoshizawa (ICCSA (1), 2006)

BCM-Type Synaptic Plasticity Model Using a Linear Summation of Calcium Elevations as a Sliding Threshold. - Hiroki Kurashige; Yutaka Sakai (ICONIP (1), 2006)

A new classification of neuron models for random inputs on bifurcation structures. - Ryosuke Hosaka; Tohru Ikeguchi; Yutaka Sakai; Shuji Yoshizawa (ISCAS, 2006)

Computational algorithms and neuronal network models underlying decision processes. - Yutaka Sakai; Hiroshi Okamoto; Tomoki Fukai (Neural Networks, 2006)

The Actor-Critic Learning Is Behind the Matching Law: Matching Versus Optimal Behaviors. - Yutaka Sakai; Tomoki Fukai (Neural Comput., 2008)

Strange Responses to Fluctuating Inputs in the Hindmarsh-Rose Neurons. - Ryosuke Hosaka; Yutaka Sakai; Kazuyuki Aihara (ICONIP (2), 2009)

Bootstrap Node-Centered Proximity Identifier Selection for Structured Overlays in Private Networks. - Masashi Yoshida; Koichi Yagishita; Yusuke Dohi; Kumiko Kobayashi; Yutaka Sakai; Shigeyuki Asami (IEICE Trans. Commun., 2011)

A theoretical approach to animal's impulsive preference: Impulsive choice behavior is interpreteded as a result of reward-maximization failure. - Yoshiya Yamaguchi; Yutaka Sakai (SCIS&ISIS, 2012)

Reinforcement learning for discounted values often loses the goal in the application to animal learning. - Yoshiya Yamaguchi; Yutaka Sakai (Neural Networks, 2012)

Distance- and Direction-Dependent Synaptic Weight Distributions for Directional Spike Propagation in a Recurrent Network: Self-actuated Shutdown of Synaptic Plasticity. - Toshikazu Samura; Yutaka Sakai; Hatsuo Hayashi; Takeshi Aihara (ICONIP (3), 2013)

Immediate return preference emerged from a synaptic learning rule for return maximization. - Yoshiya Yamaguchi; Takeshi Aihara; Yutaka Sakai (Neural Networks, 2015)

Roles of Gap Junctions in Organizing Traveling Waves in a Hippocampal CA3 Network Model. - Toshikazu Samura; Yutaka Sakai; Hatsuo Hayashi; Takeshi Aihara (ICONIP (1), 2016)

A balanced motor primitive framework can simultaneously explain motor learning in unimanual and bimanual movements. - Ken Takiyama; Yutaka Sakai (Neural Networks, 2017)

Goal-oriented inference of environment from redundant observations. - Kazuki Takahashi; Tomoki Fukai; Yutaka Sakai; Takashi Takekawa (Neural Networks, 2024)

Inferring source of learning by chimpanzees in cognitive tasks using reinforcement learning theory. - Satoshi Hirata; Yutaka Sakai (Artif. Life Robotics, 2024)