News @en

Optically Pumped Magnetometer preprocessed data available (November 30, 2023)

We uploaded preprocessed data to the optically pumped magnetometer dataset: OPM-MEG, SQUID-MEG, and EEG (OSE) dataset.

Tutorial: OPM processing

SORM toolbox is released (November 13, 2023)

Software for designing sensor arrays of optical pumping magnetometers (SORM toolbox) is released.

Software and Datasets

OPM-MEG, SQUID-MEG, and EEG (OSE) dataset (May 10, 2023)

We released The OPM-MEG, SQUID-MEG, and EEG (OSE) dataset.
The OPM-MEG, SQUID-MEG, and EEG (OSE) dataset aims to disseminate a new type of magnetoencephalography (MEG) device, an optically pumped magnetometer (OPM), which enables a wearable MEG measurement system. This dataset provides OPM-MEG, SQUID-MEG, and electroencephalography (EEG) data.

CBI Software and Datasets

Young Researcher Award (IEEE CIS)

Young Researcher Award from IEEE Computational Intelligence Society Japan Chapter has been awarded to Takeaki Shimokawa for his paper and presentation entitled “Removing the Scalp Blood Flow Artifact for Functional Near-Infrared Imaging” presented in the Neurocomputing Technical Group Meeting held at Tamagawa University on March 7, 8 and 9, 2011.

VBMEG has been released

ATR Neural Information Analysis Laboratories, Kyoto, Japan, has released the VBMEG (variational Bayesian multimodal encephalography) software, which integrates multimodal brain imaging data, i.e., MEG and fMRI, and visualizes brain activities in high time and space resolution. Visit the VBMEG webpage.

JNNS Best Paper Award

The Best Paper Award from Japanese Neural Network Society (JNNS) has been awarded to Prof. Keisuke Toyama (ATR Invited Researcher), Prof. Shin Ishii (ATR Department Head),  Atsunori Kanemura (ATR Researcher), and Takeaki Shimokawa (ATR Researcher), who contributed to the following three papers.

  • K. Okada, K. Toyama, & Y. Kobayashi, “Different pedunculopontine tegmental neurons signal predicted and actual task rewards, J. Neurosci., 29(15): 4858–70, 2009.
    • Reinforcement learning optimizes actions by minimizing predicted reward errors (the difference between predicted and actual rewards).  This research has proved the existence of the cell in PPTN that encodes the predicted reward errors, marking a leap for uncovering the neural mechanism of reinforcement learning.
  • A. Kanemura, S. Maeda, & S. Ishii, “Superresolution with compound Markov random fields via the variational EM algorithm,” Neural Netw., 22(7): 1025–1034, 2009.
    • This research dealt with the reconstruction-type superresolution problem and the accompanying registration problem.  We used a compound Markov random field for the prior, and proposed a Bayesian estimation method that marginalizes unknown variables, and showed its effectiveness not only in avoiding overfitting, but also in edge-preserving superresolution.
  • S. Shinomoto, H. Kim, T. Shimokawa, N. Matsuno, S. Funahashi, K. Shima, I. Fujita, H. Tamura, T. Doi, K. Kawano, N. Inaba, K. Fukushima, S. Kurkin, K. Kurata, M. Taira, K. Tsutsui, H. Komatsu, T. Ogawa, K. Koida, J. Tanji, & K. Toyama, “Relating neuronal firing patterns to functional differentiation of cerebral cortex,” PLOS Comput. Biol., 5(7): e1000433, 2009.
    • By analysing spike times series measured in various areas in the brain, we have uncovered that the irregularity in the signal correlates with the functions including the sensation, association, and movement.

Japanese Neural Network Society (JNNS), List of awardees (in Japanese)

Website renewal

The webpage for the CBI Department has been renewed due to the reorganization of the former ATR Computational Neuroscience Labs. into ATR Brain Information Communication Research Lab. Group, in which CBI belongs to ATR Neural Information Analysis Labs.