Committee |
Date Time |
Place |
Paper Title / Authors |
Abstract |
Paper # |
NLP |
2010-11-19 13:55 |
Miyagi |
Tohoku University (RIEC) |
Dynamical behavior of bursting oscillation in terms of spatiotemporal pattern of potential with active areas Koji Kurose (Tohoku Univ.), Yoshihiro Hayakawa (Sendai National College of Technology), Shigeo Sato, Koji Nakajima (Tohoku Univ.) NLP2010-100 |
Various type models including Hodgkin-Huxley model express the firing dynamics of the biological neuron. These neuron mo... [more] |
NLP2010-100 pp.7-10 |
NLP |
2010-11-19 14:20 |
Miyagi |
Tohoku University (RIEC) |
Solving Method of Conbinatorial Optimization Problems Based on Quartic Form Energy Function for Solving Larger Problems Takahiro Sota (Tohoku Univ.), Yoshihiro Hayakawa (Sendai National College of Tech.), Shigeo Sato, Koji Nakajima (Tohoku Univ.) NLP2010-101 |
We have proposed the Inverse function Delayed network with Higher order synaptic Connection (HC-ID network) to solve var... [more] |
NLP2010-101 pp.11-16 |
NLP |
2010-11-20 10:30 |
Miyagi |
Tohoku University (RIEC) |
Probability Calculation using Hopfield Networks Kouta Tanno, Kenichi Takahashi, Yoshihiro Hayakawa (Sendai NCT) NLP2010-109 |
We applied the methods for solving a combinatorial optimization problem by using neural networks to a famous game, mine... [more] |
NLP2010-109 pp.49-54 |
NLP |
2010-11-20 10:55 |
Miyagi |
Tohoku University (RIEC) |
A Modular Neural Network for Parallel Computation Daisuke Sasaki, Yoshihiro Hayakawa (Sendai NCT) NLP2010-110 |
Some of combinatorial optimization problems cause exponential increases of calculation time in terms of a problem size. ... [more] |
NLP2010-110 pp.55-60 |
NC, MBE (Joint) |
2010-03-11 10:40 |
Tokyo |
Tamagawa University |
The Solving method of Sudoku using Inverse Delayed Neural Networks Yoshihiro Hayakawa (Sendai N.C.T.), Koji Nakajima (Tohoku Univ.) NC2009-163 |
[more] |
NC2009-163 pp.443-448 |
NLP |
2010-03-10 10:00 |
Tokyo |
|
Neural Networks and the Application to the 4-Queen Problem Yusuke Maenami, Takeshi Onomi (Tohoku Univ), Yoshihiro Hayakawa (Sendai Nat Coll. of Tech.), Shigeo Sato, Koji Nakajima (Tohoku Univ) NLP2009-172 |
A combination optimization problem is generally NP difficulty or NP completeness. When problem size becomes large, it is... [more] |
NLP2009-172 pp.81-85 |
NLP |
2010-03-10 13:25 |
Tokyo |
|
Behavior of coupled oscillators in a quadratic potential with active area Koji Kurose, Takahiro Sota (Tohoku Univ.), Yoshihiro Hayakawa (Sendai Nat Coll. of Thech.), Shigeo Sato, Koji Nakajima (Tohoku Univ.) NLP2009-177 |
Neuron models express the dynamics of the biological neuron and there are various neuron models that are proposed and st... [more] |
NLP2009-177 pp.109-113 |
NLP |
2010-03-10 15:15 |
Tokyo |
|
Discrete Time Inverse Function Delayed Network with Higher-Order Connections Takahiro Sota, Koji Kurose (Tohoku Univ.), Yoshihiro Hayakawa (Sendai Nat Coll. of Tech.), Shigeo Sato, Koji Nakajima (Tohoku Univ.) NLP2009-181 |
The Inverse function Delayed network with higher-order connection (HCID network) has been proposed to solve combinatoria... [more] |
NLP2009-181 pp.131-136 |
NLP, CAS |
2008-10-14 14:45 |
Miyagi |
|
Inverse Function Delayed Network with Higher-Order Connections Takahiro Sota, Yoshihiro Hayakawa, Koji Nakajima (Tohoku Univ.) CAS2008-40 NLP2008-52 |
The Inverse function Delayed (ID) model has been proposed as one of novel neural models. The ID model has the negative r... [more] |
CAS2008-40 NLP2008-52 pp.35-40 |
NC, MBE (Joint) |
2008-03-13 15:00 |
Tokyo |
Tamagawa Univ |
Appling dynamic representation to solving QAP in ID model Takahiro Sota, Yoshihiro Hayakawa, Koji Nakajima (Tohoku Univ.) NC2007-171 |
We have solved combinatorial optimization problems by using the Inverse function Delayed model (ID model). When obtainin... [more] |
NC2007-171 pp.349-354 |
NC |
2007-11-18 16:20 |
Saga |
Saga Univ. |
[Invited Talk]
An integrated circuit and characteristics of the Burst ID neural network Koji Nakajima, Shinya Suenaga, Akari Sato, Takahiro Sota, Yoshihiro Hayakawa (Tohoku Univ.) NC2007-63 |
We report the characteristics of a neural network comprising neuron units with bursting, and its integrated circuit that... [more] |
NC2007-63 pp.49-54 |
NC |
2007-11-19 10:15 |
Saga |
Saga Univ. |
Inversed Function Delayed Network for Traveling Salesman Problem Takahiro Sota, Yoshihiro Hayakawa, Koji Nakajima (Tohoku Univ.) NC2007-64 |
Many researchers have attempted to solve the combinatorial optimization problems by using neural networks which have hig... [more] |
NC2007-64 pp.55-60 |
NLP |
2007-03-05 17:35 |
Miyagi |
|
Retrieval Properties of a Hopfield Type Associative Neural Network with Hysteretic Transfer Function Erik Oberg, Yoshihiro Hayakawa, Koji Nakajima (Tohoku Univ.) |
[more] |
NLP2006-153 pp.59-64 |
NLP |
2005-12-16 15:35 |
Ibaraki |
Ibaraki Univ. |
Discrete ID model based on asynchronous update Hirokazu Nagashima, Yoshihiro Hayakawa, Koji Nakajima (RIEC Tohoku Univ.) |
It is a problem not escaping from a local minimum when the combinatiorial optimization problems are solved by ordinary n... [more] |
NLP2005-94 pp.51-56 |
NLP |
2005-12-16 16:00 |
Ibaraki |
Ibaraki Univ. |
Burst firing ID model and its application Shinya Suenaga, Yoshihiro Hayakawa, Koji Nakajima (R.I.E.C., Tohoku Univ.) |
[more] |
NLP2005-95 pp.57-62 |
NLP |
2005-05-17 15:20 |
Yamagata |
Yamagata Univ (Yonezawa) |
Discrete ID model for optimization problems Hirokazu Nagashima, Yoshihiro Hayakawa, Koji Nakajima (RIEC Tohoku Univ.) |
[more] |
NLP2005-6 pp.29-34 |
NLP |
2005-05-17 15:45 |
Yamagata |
Yamagata Univ (Yonezawa) |
Parameter dependence on performance of ID model for optimization problems Akari Sato, Yoshihiro Hayakawa, Koji Nakajima (R.I.E.C., Tohoku Univ.) |
Inverse Function Delayed model used in out reserch has high performance of seaeching answer of optimization problems. Th... [more] |
NLP2005-7 pp.35-39 |
NLP |
2004-11-27 11:40 |
Fukuoka |
Kyushu Inst. Tech. |
The design of Inverse Function Delayed neurochip with the learning function Jun Fukuhara, Shinya Suenaga, Yoshihiro Hayakawa, Koji Nakajima (R.I.E.C.,Tohoku Univ.) |
[more] |
NLP2004-83 pp.43-48 |