Date: Thursday, March 16th, 2017

Venue: RIKEN Wako Campus Map
     Okochi Hall
(Campus map: Bldg. No.C32)


13:00 Opening
Susumu Tonegawa (Director, RIKEN Brain Science Institute)
13:05-13:40 Canonical models for neural information processing and neuromorphic computing
Si Wu (Beijing Normal Univ)

Web site
13:40-14:15 From statistical neurodynamics of Amari-Hopfield model to bi-directional computation in deep generative model
Masato Okada (Univ Tokyo)
Web site
14:15-14:30 Break
14:30-15:05 Estimation of neural connections from multiple spike trains
Noboru Murata (Waseda Univ)
Web site
15:05-15:40 Machine learning and AI for the sciences -- towards understanding 
Klaus-Robert Müller (Technical Univ Berlin)
Web site
15:40-16:10  Coffee break
16:10-17:10 Information geometry of Waaserstein distance
Shun-ichi Amari (RIKEN BSI)
Web site

17:10 Closing
17:30-19:30 Reception at Hirosawa club (Campus map: Bldg. No.C72)

Online registration


Taro Toyoizumi (RIKEN BSI)
Hiroyuki Nakahara (RIKEN BSI)
Tomoki Fukai (RIKEN BSI)

Sponsored by

RIKEN Brain Science Institute


RIKEN BSI Mathematical Neuroscience
Emi Namioka e-mail: emi(at)brain.riken.jp

Estimation of neural connections from multiple spike trains

Noboru Murata

Noboru Murata
Waseda Univ

Estimating neural connections from multiple spike trains is an important task for analyzing mechanisms of information processing in the brain. There are many proposals for estimating connections between observed neurons, but most of them pay little attention on influence from unobserved neurons. By introducing a probabilistic firing model of observed and unobserved neurons, we propose a method of infering the effects of unobserved neurons and estimating the connections of partially observed neurons. Our proposed method is verified an validated with synthetic and real data.

This work is done in collaboration with
Mr. T. Iwasaki at Waseda University,
Dr. S. Akaho at AIST,
Dr. H. Hino at University of Tsukuba,
and Dr. M. Tatsuno at University of Lethbridge.


Information geometry of Waaserstein distance

Shun-ichi Amari

Shun-ichi Amari

Clustering or automatic category formation is one of big topics both in neuroscience and AI.Patterns are clustered based on their similarity (dissimilarity). We regard a pattern as a distribution on a frame (consisting of pixels in the case of pictures). Information geometry studies invariant structure among (probability) distributions, whereas the Wasserstein distance is a structure taking the closeness of pixels into account. Both have developed for long years separately, and both have useful applications to machine learning and neuroscience. Now is the time to have a unified framework for geometry of distributions: Unifying the KL divergence and Wasserstein distance. This will give strong applications in future.