PROGRAM


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February 28 (Friday)


13:30-Registration
14:00-14:05Opening remarks
Session 1: Recognition & Learning I
14:05-14:30Bio-inspired sparse coding for audio restoration
César D. Salvador (Silicon Integrated Co., Ltd., China)
14:30-14:55Conceptual model of the auditory spatial attention in multi-source listening environment
Ryo Teraoka, Shuichi Sakamoto, Zhenglie Cui, Yoiti Suzuki, Satoshi Shioiri (Tohoku University, Japan)
14:55-15:20Practical and Mathematical investigation for bio-sonar strategy of bats
Yasufumi Yamada (Hiroshima University, Japan)
15:20-15:35Coffee break
Session 2: Brainware LSI Technologies I
15:35-16:00Prefiltering Using Reflectionless Transmission-Line Model for Speech Recognition in Noise Environment
Takemori Orima (Tohoku University, Japan)
16:00-16:25Capacity of fully binarized convolutional neural network
Martin Lukac (School of Science and Technology, Nazarbayev University, Kazakhstan)
16:25-16:50In-Hardware Training Chip Based on CMOS Invertible Logic for Machine Learning
Naoya Onizawa (Tohoku University, Japan)
16:50-17:15Toward efficient training of learning machines using dynamic stochastic computing
Siting Liu (McGill University, Canada)
17:15-18:00Break
18:00-21:00Open discussion

February 29 (Saturday)


Session 3: Recognition & Learning II
09:00-09:25Hierarchical Decentralized Control Mechanism Underlying Brittle Stars’ Locomotion
Takeshi Kano (Tohoku University, Japan)
09:25-09:50The measurement of spatial extent of audiovisual attention by SSR and ERP
Shin Ono, Shuichi Sakamoto, Ryo Teraoka, Yoshiyuki Sato, Yasuhiro Hatori, Chia-huei Tseng, Ichiro Kuriki, Satoshi Shioiri (Tohoku University, Japan)
09:50-10:15Enhancement and suppression in selective visual attention
Søren K. Andersen (University of Aberdeen, UK)
10:15-10:30Coffee break
Session 4: Brainware LSI Technologies II
10:30-10:55A Genetically Encoded Autonomous Bioluminescent Voltage Indicator for Neural Imaging
Luke Theogarajan (UC Santa Barbara, USA)
10:55-11:20Analog circuit implementation of the Izhikevich neuron model
Shigeo Sato (Tohoku University, Japan)
11:20-11:45Training methods of quantum neural networks
Enrico Prati (Consiglio Nazionale delle Ricerche, Italy)
11:45-11:50Closing remarks