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 Results 1 - 7 of 7  /   
Committee Date Time Place Paper Title / Authors Abstract Paper #
NC, IPSJ-BIO 2012-06-28
11:10
Okinawa OIST, Okinawa A Computational Model for State Value Estimation in the Brain Using Two Kinds of Temporal Differences
Masanari Shinotsuka, Masahiko Morita (Univ. of Tsukuba) NC2012-5
The basal ganglia has been considered to be deeply involved in reinforcement learning since Schultz et al. reported that... [more] NC2012-5
pp.25-30
HCGSYMPO
(2nd)
2011-12-07
- 2011-12-09
Kagawa Sun Port Hall Takamatsu Neurophusiological investigation of shopping behavior (I)
Yasuhiro Kawahara (Open Univ.), Yoshitada Katagiri, Emiko Imai (NICT)
Great interest has been focused on a program of shopping tours in nursing homes. This program provides opportunities for... [more]
NC, MBE
(Joint)
2009-03-12
16:05
Tokyo Tamagawa Univ. A computational model for dopamine activities in uncertain reward prediction
Akiyo Asahina (Nara Inst. of Sci and Tech/Kyoto Univ.), Jun-ichiro Hirayama (Kyoto Univ.), Shin Ishii (Kyoto Univ./Nara Inst. of Sci and Tech) NC2008-148
In order for animals to behave effectively in their surrounding
uncertain environments, it is essentially important to ... [more]
NC2008-148
pp.267-271
OME, EID 2009-03-06
13:25
Tokyo Kikai-Shinko-Kaikan Bldg. Surface Plasmon Resonance Sensor Using Grating Coupling Multimode Excitation
Akira Baba, Ryosuke Yamazaki, Yasuo Ohdaira, Kazunari Shinbo, Keizo Kato, Futao Kaneko (Niigata Univ.), Satya Samanta, Jason Locklin (UGA) EID2008-84 OME2008-95
In this work, we demonstrate a novel dopamine sensor using multimode surface plasmon excitations by irradiating white li... [more] EID2008-84 OME2008-95
pp.7-11
NC 2009-01-19
17:25
Hokkaido Hokkaido Univ. Reinforcement Meta-learning rule solves the distal reword problem
Shojiro Araki (Tamagawa Univ), Yutaka Sakai (Tamagawa Univ. Brain Scie Inst.) NC2008-95
It is known that spike-timing-dependent synaptic plasticity (STDP) epends on the initial strength of the synapse, and th... [more] NC2008-95
pp.79-83
NC 2006-07-14
14:30
Tokyo Waseda University Dynamical System Model of Spatial Reward Learning
Adam Ponzi (RIKEN)
A Basal Ganglia based dynamical system model of reward learning is
presented. Numerical simulations nicely reproduce re... [more]
NC2006-41
pp.19-24
NC 2006-03-16
11:25
Tokyo Tamagawa University Analysis on activity of dopamine neurons encoding prediction of uncertain reward
Hiroki Takahashi, Hideaki Itoh, Shigemi Sawa, Kiyohiko Nakamura (Tokyo Institute of Technology)
Reinforcement learning is one of the machine learning, and is proposed as a computational model of conditioning. It was ... [more] NC2005-129
pp.25-30
 Results 1 - 7 of 7  /   
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