Yutaka Sakai
According to our database1,
Yutaka Sakai
authored at least 21 papers
between 1999 and 2024.
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Bibliography
2024
Inferring source of learning by chimpanzees in cognitive tasks using reinforcement learning theory.
Artif. Life Robotics, August, 2024
Neural Networks, 2024
2017
A balanced motor primitive framework can simultaneously explain motor learning in unimanual and bimanual movements.
Neural Networks, 2017
2016
Roles of Gap Junctions in Organizing Traveling Waves in a Hippocampal CA3 Network Model.
Proceedings of the Neural Information Processing - 23rd International Conference, 2016
2015
Immediate return preference emerged from a synaptic learning rule for return maximization.
Neural Networks, 2015
2013
Distance- and Direction-Dependent Synaptic Weight Distributions for Directional Spike Propagation in a Recurrent Network: Self-actuated Shutdown of Synaptic Plasticity.
Proceedings of the Neural Information Processing - 20th International Conference, 2013
2012
Reinforcement learning for discounted values often loses the goal in the application to animal learning.
Neural Networks, 2012
A theoretical approach to animal's impulsive preference: Impulsive choice behavior is interpreteded as a result of reward-maximization failure.
Proceedings of the 6th International Conference on Soft Computing and Intelligent Systems (SCIS), 2012
2011
Bootstrap Node-Centered Proximity Identifier Selection for Structured Overlays in Private Networks.
IEICE Trans. Commun., 2011
2009
Proceedings of the Neural Information Processing, 16th International Conference, 2009
2008
The Actor-Critic Learning Is Behind the Matching Law: Matching Versus Optimal Behaviors.
Neural Comput., 2008
2006
Neural Networks, 2006
Proceedings of the International Symposium on Circuits and Systems (ISCAS 2006), 2006
BCM-Type Synaptic Plasticity Model Using a Linear Summation of Calcium Elevations as a Sliding Threshold.
Proceedings of the Neural Information Processing, 13th International Conference, 2006
Proceedings of the Computational Science and Its Applications, 2006
2004
2003
Neural Comput., 2003
2001
Neuronal integration mechanisms have little effect on spike auto-correlations of cortical neurons.
Neural Networks, 2001
1999
Temporally correlated inputs to leaky integrate-and-fire models can reproduce spiking statistics of cortical neurons.
Neural Networks, 1999
The Ornstein-Uhlenbeck Process Does Not Reproduce Spiking Statistics of Neurons in Prefrontal Cortex.
Neural Comput., 1999
Proceedings of the Foundations and Tools for Neural Modeling, 1999