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Home > Introduction of our tenure-track faculties > Takiyama Ken

Introduction of our tenure-track faculties

Takiyama Ken

Affiliation Organization for Promotion of Tenure-track System / Institute of Engineering
Division Division of Advanced Electrical and Electronics Engineering
Research field Computational Neuroscience
Keyword(S) Neural network, motor learning & control,        behavioral experiment, machine learning
Url https://sites.google.com/site/takiyama1106/
Research experience

・2013.4.1-2015.8.31: JSPS research fellow (SPD)
・2015.9.1-present: Associate Professor, Tokyo University of Agriculture and Technology

Educational background

・2008.3: The University of Tokyo, Department of Education
・2010.3: The University of Tokyo, Department of Complexity Science and Engineering, master course
・2013.3: The University of Tokyo, Department of Complexity Science and Engineering, doctoral course

Awards

・2009, Best presentation award, Japan Neural Network Society
・2010, IEEE Computational Intelligence Society Japan Chapter Young Research Award
・2010, Dean Prize, The University of Tokyo, Graduate school of Frontier Science (master course)
・2011, Best presentation award, the 5th Japan Motor Control meeting
・2012, Best presentation award, the 6th Japan Motor Control meeting
・2013, Dean Prize, The University of Tokyo, Graduate school of Frontier Science (doctoral course)
・2013, Young research award, summer workshop of Comprehensive Brain Science Network
・2013, Young research award, the 7th Japan Motor Control meeting
・2014, Best research award, Japan Neural Network Society

Selected papers and publications

・Ken Takiyama, Masaya Hirashima, Daichi Nozaki, Prospective errors determine motor learning, Nature Communications, 2015, 6, 5925 1-12
・Ken Takiyama, Sensorimotor transformation via sparse coding, Scientific Reports, 2015, 5, 9648 1-7
・Ken Takiyama, Context-dependent memory decay is evidence of effort minimization in motor learning: A computational study, Frontiers in Computational Neuroscience, 2015, 9(4), 1-10
・Ken Takiyama, Masato Okada, Recovery in Stroke Rehabilitation through the Rotation of Preferred Directions Induced by Bimanual Movements: A Computational Study, PLoS One, 7(5), 2012, e37594 1-10
・Ken Takiyama, Masato Okada, Maximization of learning speed in the motor cortex due to neuronal redundancy, PLoS Computational Biology, 8(1), 2012, e1002348 1-12
・Ken Takiyama, Masato Okada, Detection of hidden structures from nonstationary spike trains, Neural Computation, 23, 2011, 1205–1233
・Yasushi Naruse, Ken Takiyama, Masato Okada, Hiroaki Umehara, Statistical method for detecting phase shifts in alpha rhythm from human electroencephalogram data, Physical Review E, 87(4), 2013, 042708-1 - 042708-7
・Ryota Hasegawa, Ken Takiyama, Masato Okada, Seiji Miyoshi, Image Segmentation and Restoration Using Switching State-Space Model and Variational Bayesian Method, Journal of Physical Society of Japan, 81, 2012, 094802-1 - 094802-7
・Yasushi Naruse, Ken Takiyama, Masato Okada, Tsutomu Murata, Inference in alpha rhythm phase and amplitude modeled on Markov random field using belief propagation from electroencephalograms, Physical Review E, 82(1), 2010, 011912-1 - 011912-11

Research Description

When we challenge an unknown instruments, we cannot play it well. When we challenge an unknown sport, we cannot give a good performance. However, we can achieve desired performance of the instrument or in the sport after practicing those. These are examples of motor learning in which we gradually improve our motor skill by practicing. Examples of motor learning are playing instruments, doing sports, rehabilitation, or robotics, which suggests that results of motor learning researches can be applied to various kinds of situations. The motor learning is considered to be originated from plasticity in our brain. Based on mathematical modeling and human behavioral experiments, our laboratory investigate the following three topics: 1. "Information processing in our brain during motor learning process", 2. "how to evaluate motor learning ability in individual", and 3. "training paradigm to efficiently improve motor skill". We plan to use electroencephalograms (EEG) or machine learning technique to approach those topics from different perspectives.

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About TUAT's tenure-track program

This progam gives young researchers, including me, precious, attractive, and challenging experiences of managing laboratory. In the last month, I cleaned remained desks, chairs, and shelfs, and ordered new desks, chairs. I did not have those kinds of experiences and those experiences let me know how difficult it is to manage laboratoty. From the next year, I will have some lectures and educate some students. There are many young researched all over the world, but most of them cannot have such experiences. This program let us to have many precious experiences, I think.

Future aspirations

Since I am a principal investigator, I want to do many researches with which I can feel satisfied. In particular, since I attempt to propose a unified model of motor learning which can explain various motor learning related phenomena within a single framework, I continue to improve the unified model. Additionally, my son is 11 months old and I love him so much. I want to take care of not only my jobs but my families. Further, I want to make a win-win relation between me and students in my laboratory.