The Whole Brain Architecture Initiative
Naoya Arakawa ✉ and Hiroshi Yamakawa()

The Whole Brain Architecture Initiative, Kabukiza Tower 14F, 4-12-15 Ginza,Chuo-ku, Tokyo 104-0061, Japan{naoya.arakawa,ymkw}@wba-initiative.org

Abstract. The Whole Brain Architecture Initiative is a non-proﬁt organization(NPO) founded in Japan in August 2015, whose purpose is to support research
activities aiming for realizing artiﬁcial intelligence with human-like cognitive
capabilities by studying the entire architecture of the brain. It performs educa‐tional activities such as holding seminars and hackathons and compiling educa‐
tional materials, as well as R&D activities such as developing software platforms
to support research in artiﬁcial intelligence and facilitating communication among
research communities.
Keywords: Biologically inspired cognitive architecture · Artiﬁcial general
intelligence · Open software · Non-proﬁt organization

1Introduction

The Whole Brain Architecture Initiative (WBAI) is a non-proﬁt organization (NPO)whose purpose is to support research activities aiming to realize artiﬁcial intelligencewith human-like cognitive capabilities by studying the entire architecture of the brain.
1.1 Whole Brain Architecture Seminars
In the summer of 2013, Hiroshi Yamakawa (the current chairperson of WBAI), Yutaka
Matsuo (vice-chairperson), and Yuuji Ichisugi (The National Institute of Advanced
Industrial Science and Technology of Japan (AIST)) met and agreed that it would bepossible to create advanced artiﬁcial intelligence having cognitive abilities on par withhuman beings by referring to the information processing architecture of the entire brain,with the increase of computational resources and progress in machine learning, as wellas the rapid accumulation of ﬁndings in neuroscience in recent years. They also agreed
that, in order to realize such artiﬁcial intelligence, it would be necessary to gather andfoster human resources in various disciplines such as neuroscience, cognitive science,and machine learning as well as artiﬁcial intelligence. They soon decided on using theterm “whole brain architecture (WBA)” to designate the approach to create artiﬁcial
general intelligence by referring to the architecture of the entire brain, and held theirﬁrst seminar in Tokyo on December 19th in 2013. Since then, whole brain architecture
seminars have been held a few times a year to invite outstanding researchers to speak
about related subjects.
© Springer International Publishing AG 2016
A. Hirose et al. (Eds.): ICONIP 2016, Part I, LNCS 9947, pp. 316–323, 2016.
DOI: 10.1007/978-3-319-46687-3_35

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1.2 Up to the Foundation
In 2014, researchers interested in the whole brain architecture approach held meetingsand discussed methodology to substantiate the approach. By the end of the year, theyagreed that they would create an organization called the Whole Brain Architecture
Initiative, to give their programs a concrete form as a research community. At thebeginning of 2015, they decided to make the organization non-proﬁt, and after necessary
procedures, the NPO was founded on August 21st.
1.3 Financial Support
The operations of WBAI have been ﬁnancially supported by sponsors including privatecompanies. As of July 2016, it has seventeen sponsors consisting of enterprises andindividuals.

2The Whole Brain Architecture Approach

The WBA approach is an engineering approach that aims to create artiﬁcial generalintelligence (AGI) by learning from the architecture of the entire brain.
2.1 Artiﬁcial General Intelligence
AGI is artiﬁcial intelligence that can learn to perform tasks including those not foreseenat the time of its conception, unlike ‘narrow’ artiﬁcial intelligence designed to perform
speciﬁc tasks. Such general intelligence would be necessary because, for instance, arti‐
ﬁcial intelligence, sometimes embodied in robots, will be required to cope with unex‐
pected situations when collaborating with human beings in the real world.
2.2 Whole Brain ArchitectureHuman beings possess general intelligence in the sense that they can learn to perform
previously unforeseen tasks. If the human brain instantiates general intelligence, itwould be reasonable to endeavor to realize AGI by taking inspiration from the humanbrain. As intelligence is not a function of part of the brain but of the entire brain, it isalso reasonable to seek inspiration from the architecture of the entire brain.
2.3 The Central WBA Hypothesis
Observing the development in machine learning technologies in recent years, WBAI
further adopts the following hypothesis:“The brain combines modules, each of which can be modeled with a machine learning algorithm,
to attain its functionalities, so that combining machine learning modules in the way the brain
does enables us to construct a generally intelligent machine with human-level or super-humancognitive capabilities”.

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This is a working hypothesis that constrains the scope of research so that we canconcentrate resources.

3

Our Policies

The mission of WBAI is ‘to create (engineer) a human-like AGI by learning from thearchitecture of the entire brain’. WBA deploys educational and R&D businesses toinstantiate the mission.
The goal of our educational business is to help people conducting research on theWBA approach on a long-term basis. WBAI conducts educational activities in relatedareas such as artiﬁcial intelligence, neuroscience, cognitive science, and machinelearning. In particular, WBAI holds seminars and hackathons; participates and collab‐
orates in academic events; and also collaborates and communicates with related
academic societies.
The goal of our R&D business is to support research using the WBA approach. While
research projects in general may or may not last for a few years, we are committed tosupporting research infrastructures not only for particular projects but also for terms
longer than project lifespans. Such infrastructure includes software for supporting
research and neuroinformatic databases. However, WBAI itself does not conduct
research on the WBA approach and does not compete with researchers in the area.
3.1 WBAI and Open Development of AGI
As an NPO, WBAI aims to make related technical information available to the public to
be used in better ways. To this end, WBAI not only publishes the products of its activi‐ties, but is also determined to facilitate open research and development. For example, itcollaborates and has discussions with other open AGI projects such as OpenCog1. It also
tries to facilitate research by publishing the previously mentioned research infrastructureso that more people can try out or apply published technologies with ease.

4Activities in the First Year

Activities in the ﬁrst year (from September 2015 to July 2016) include educational andR&D endeavors following the previously mentioned policy.
4.1 Educational Business
As noted previously, the goal of the educational business is to help people who canconduct research on the WBA approach on a long-term basis. In the ﬁrst year, WBAI
held the ﬁrst hackathons and WBA seminars; participated in BICA 2015 in Lyon; andstarted creating learning material on the Web (in Japanese).

1

http://opencog.org.

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WBA Seminars and Symposium. As previously noted, WBA seminars have been held
since before the foundation of WBAI. In the ﬁrst year, WBAI held three seminars anda symposium with the following themes and speakers (mainly in Japanese):• 11th Seminar: August 26, 2015, Inside Deep Learning
Masayuki Ohzeki (Assistant Professor, Kyoto University), Yoichi Mototake (Univ.
of Tokyo), Adam Gibson (CTO, Skymind)• 12th Seminar: January 14, 2016, Learning Architecture of the Brain
Kenji Doya (Professor, Okinawa Institute of Science and Technology)• 13th Seminar: March 15, 2016, Connectome and AI
Haruo Mizutani (Harvard University), Hiroki Kurashige (Univ. of Tokyo)• First WBAI Symposium: May 18, 2016, Accelerating AI, Accelerating World• 14th Seminar: May 18, 2016, Neocortical Computational Models beyond Deep
Learning
Takuya Matsuda (NPO Einstein), Manabu Tanifuji (Riken BSI)
• 15th Seminar: June 14, 2016, Evolution, Development, and Learning in Intelligence
Nobuyuki Kawai (Nagoya Univ.), Hiroyuki Okada (Tamagawa Univ.)
The First Hackathon. Together with the Whole Brain Architecture Future Leaders,
WBAI held its ﬁrst hackathon at the Yokohama campus of Keio University for ﬁve days
from September 19. Seven teams consisting of mainly undergraduate and graduate
students participated in this event. Each team set their own task to meet the theme“development of a combined learner” and worked on it while staying in lodging facilities
on campus. As one of WBAI’s educational activities, this event aimed to improve theknowledge and skill of the participants and provide an opportunity for social networking
among students and researchers interested in areas such as neuroscience and machinelearning. The event received support from AIST, the University of Electro-Communi‐cations, and AlpacaDB, Inc., as well as additional backing from the Dwango AI Lab(Dwango Corporation). The products of the hackathon have been published on GitHubin English.2
Participating in BICA 2015. The BICA Society is an academic community on biolog‐
ically inspired cognitive architectures (BICA) that holds international conferences annu‐
ally. As WBA is apparently BICA, their theme accords well with ours. The BICA 2015
conference was held in Lyon, France, where ﬁve regular members and a WBA Future
Leader participated as authors of submitted papers [1, 2]. There was also a WBA special
session in the conference.3 Moreover, WBAI invited three students, as the winners of
the ﬁrst hackathon, to present their work in the WBA session and write a report on theconference.
Compiling Learning Material on the Web. To disseminate basic knowledge for
WBA in areas such as artiﬁcial intelligence, neuroscience, cognitive science, andmachine learning, WBAI has been compiling glossaries (in Japanese) on its Web site.
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https://github.com/wbap/Hackathon2015.
https://liris.cnrs.fr/bica2015/wiki/doku.php/wba.

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Furthermore, WBAI is collaborating with university educators in each area. For the time
being, a glossary in machine learning is getting ready.

4.2 R&D Business
As noted in Sect. 3, the goal of the R&D business is to support research activities on theWBA approach.
R&D at WBAI. WBAI is actively working to develop research infrastructure such assoftware and databases to be used in research and make it public. In particular, WBAI
is working on a generic software platform for constructing WBA, evaluation methodsof AGI, learning environments for WBA, and infrastructure for neuroinformatics.
• Generic Software Platform [1]The generic software platform for constructing WBA supports a mechanism thatperforms cognitive functions while machine learning modules corresponding to brain
parts communicate each other, according to the core WBA hypothesis described inSect. 2.3. In particular, platform modules communicate with numeric vector values
corresponding to signals transmitted in axons. In collaboration with Riken and Keio
University, the eﬀort to create this platform began in 2014. It was named BriCA(Brain-inspired Computing Architecture) and implemented in Java (Version 0), thenPython (Version 1), and currently in C++ (Version 2).
Together with the BriCA platform, a language that describes its architecture has also
been designed and implemented. This BriCA Language describes modules and connec‐tions among them for WBA platforms. Such an architecture description language facil‐
itates the understanding and module modification of architecture implementation, andwould work well with open, collaborative development. While the current implemen‐tation of BriCA Language uses the BriCA platform, it can also use other platforms suchas ROS, Brain Simulator TM, or Nengo, in which modules pass messages to eachother. Dwango AI Lab has offered support also for the design and implementation ofthis BriCA Language.
• Learning Environments
Human-like AGI is expected to learn and acquire skills in a world similar to that inwhich human beings live. Though such a learning agent could be implemented as aphysical robot, such a design may also require electro-mechanical engineering
besides artiﬁcial intelligence. Therefore, in cooperation with the Dwango AI Lab,WBAI is working on robot simulators in the virtual world as learning environment
for AGI. Some results from the ﬁrst quarter of 2016 have been published on the Web.
The ﬁrst result is the creation of environments with the Gazebo robot simulator
combined with BriCA, Nengo, or Brain Simulator TM (Fig. 1), and the second shows
the Unity game engine combined with machine learning modules from Chainer APIs
(Fig. 2). The latter is the ﬁrst of the software series called Life in Silico (LIS).44

https://github.com/wbap/lis.

The Whole Brain Architecture Initiative321• Infrastructure for Neuroinformatics
Knowledge of the architecture of the entire brain is required to create artiﬁcial general
intelligence on the WBA approach. In particular, the knowledge (information) on theparts of the human/mammalian brain, microstructure of the parts, and interconnection
between parts (connectome) are all required. Much of this knowledge already exists butis scattered across countless academic papers. To use it eﬃciently, this information
should be integrated in one place. WBAI is working to centralize this information forimproved knowledge integration.

Fig. 1. Gazebo simulator + BriCA

Fig. 2. LIS: unity game engine + Chainer

WBAI is also developing a software prototype of a WBA viewer called BICAmon(Brain-Inspired Cognitive Architecture monitor), which shows the activities of parts ofcognitive architecture as if they are parts of a brain.5 It interactively displays virtual
brain parts and connections on a Web browser, while active parts in the corresponding
cognitive architecture are highlighted. The viewer has been developed together with theDwango AI Lab.
R&D on WBA. As noted in Sect. 3, WBAI does not conduct research on whole brainarchitecture itself, but supports others doing research using the WBA approach. One
form of support is sharing discussion among researchers in areas related to WBA; WBAI
hold discussion meetings inviting interested researchers and students. Areas of interest
include aﬀects and the hippocampus.
4.3 Forming an Open Community for AI Development
In June 2016, WBAI created a community of engineers for open AI development onSlack. This occurred after mini-hackathons held in the same month, where participants
succeeded in implementing Deep Predictive Coding Networks (Deep PredNet)6,
proposed by William Lotter et al. in May as a promising model of the neocortex [3].

56https://github.com/kiyomaro927/bicamon.
e.g., https://github.com/quadjr/PredNet.

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The community integrates engineering activities around WBAI including thosedescribed in Sect. 4.2.

WBAI intends to augment this community in quality and quantity with the supportof researchers in related areas to work on issues such as:•••••

Development of learning environment
Development and evaluation of machine learning algorithms
Experiments with AI agents that learns behaviors in interesting environments
Implementation of new cognitive functions for AI agents
Data analysis and tool development in neuroscience for the WBA approach

4.4 WBAI Activities and Volunteering
WBAI activities like WBA seminars and hackathons have been conducted with nonpaid volunteers, except for two paid part-time workers at the secretariat and honorarium
paid for hackathon tutors. PR activities focused on constructing and maintaining theWeb site have been performed on a voluntary basis. WBAI has been collaborating withanother volunteer organization called WBA Future Leaders for activities including
WBA seminars and hackathons.

Fig. 3. Relations among stakeholders around WBAIThe Whole Brain Architecture Initiative

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Future Direction

The current development in AI and machine learning is quite rapid, and the advent ofcertain AGI is becoming more plausible every day. Thus, WBAI has set a goal to ensurethat AGI is beneficial to all of humanity, with the realization of AGI in the near future inmind. To achieve this goal, WBAI is promoting and popularizing the use and develop‐
ment of technology inspired by WBA by holding events such as hackathons and providing
tools for related technologies so that AGI development will be democratized. Figure 3
summarizes the relationships among stakeholders around WBAI in this direction.
Acknowledgments. The activities of WBAI have been made possible with support fromsponsors and volunteers. We appreciate their support and invite more people to join this promisingendeavor.

References
1. Takahashi, K., Itaya, K., Nakamura, M., Koizumi, M., Arakawa, N., Masaru Tomita, M.,
Yamakawa, H.: A generic software platform for brain-inspired cognitive computing. In: BICA
2015 Proceedings (2015)
2. Omori, T., Kasumi Abe, K., Nagai, T.: Modeling of stress/interest state controlling in robotchild play situation. In: BICA 2015 Proceedings (2015)
3. Lotter, W., Kreiman, G., Cox, D.: Deep Predictive Coding Networks for Video Prediction andUnsupervised Learning. arXiv:1605.08104v2[cs.LG] (2016)