Brain Inspired Cognitive Computing System
Kosuke NISHIHARA Norihiko TAYA Toshiyuki KANOH
Osaka University NEC Brain Inspired Computing Research Alliance Laboratories
1-5 Yamadaoka, Suita-city, Osaka, 585-0871, Japan
Email: k-nishihara@ist.osaka-u.ac.jp n-taya@ist.osaka-u.ac.jp t-kanoh@ist.osaka-u.ac.jp

Abstract– Neural network based artificial intelligencelike deep learning research makes remarkable progress byusing huge and high performance computing resource. On
the other hand, brain science research clarifies that humanbrain realize a multimodal, flexible and efficient cognitive
function by only 20 watts power consumption and about
2kg weight body. This paper introduces our challenge
realizes the artificial cognitive system by following theresult of brain function and structure analysis.
Keyword– Artificial Intelligence, Brain Science, Brain
Inspired Computing

resources to this extension processing [5]. These findings
show that the brain has taken a strategy to make the bestuse of limited resources.
We follow the cutting-edge results of human cognitive
functions to establish computationally executable models
which by reassembling human cognitive abilities as BrainInspired Cognitive Computing System.
3. Brain Inspired Cognitive Computing System
3.1. Brain Inspired Cognitive Computing SystemFeatures

1. Introduction
Human brain is a system with about 2kg and 20 watts
power consumption, and the compact body has
sophisticated cognitive ability. In recent year, brain
science research is showing significant progress in
elucidation of brain functions and brain structures. We
have been studying to realize a new artificial intelligencesystem by following brain research results and its
knowledge [1]. This paper introduces our challenge to aim
for realization of a brain inspired artificial intelligencesystem with a different approach from Deep Learning.
2. Approach following Cognitive Function
This paper focuses on efficiency and flexibility ofhuman cognitive functions and applies these research
results to realize “Brain Inspired Cognitive ComputingSystem”. Recent researches are making it revealing thatbrains effectively use the limited resources to realize
flexible and high-performance computing.
For example, the presence of two cognitive pathways inprocessing such as facial recognition [2], bio-logicalmotion recognition [3] and others, has become apparent.
Thus, mechanisms for selecting efficient resources andpathways in response to situations, are being elucidated.
In addition, in the process of acquiring a representation ofmotion, the possibility that the representation is being
reconstruct using discretized data rather than originalcontinuous input is shown [4]. In the process to acquire
new functions by extending the body , it is pointed outthat the brain can re-allocate the other already used

We defined a system as Brain Inspired Cognitive
Computing System that meets the following requirements.
 Optimization of resource usage Multimodality for diversified perspective
 Autonomy to behave on goal-oriented
 Visibility of internal processing
One of the important points to optimize usage oflimited resource is knowledge that human brains use
properly two cognitive pathway. One is a quickly androughly processed pathway, another is a slowly and finely
processed pathway. For example the former is used foravoidance of danger or defense against attack whilesaving resource with low load. On the other hand the latter
is used to recognize face expression in detail with
sufficient resource. We believe the proper usage ofresource is one of the reason that human brains caneffectively perform with much lower power compared to a
today’s computer. The mechanism could be helpful toestablish an overwhelmingly effective system which
respond in real time depending on the purpose of use.
Brains handle different kind of sensory input in thesame manner. The process by neuron firing does notdepend on sensory perceptions. The common handlingacross multi-modalities of sensory input makes it possible
to compliment damaged information of a modality byusing information from the others. The diversified
perspective in the basis of multimodal information could
be beneficial to build a robust system from ambiguous
input.

Encoding
ModuleIntegration
ModuleDecision
ModuleExtraction

Inference

Creation

Decomposition

Selection

Categorization

InterfaceFig. 1 Conceptual Diagram
People analyze environmental stimuli
while
decomposing the input into primitive elements. Then therepresentations are constructed through the combinationof the primitive elements depending on respective goals sothat we understand the environment. It is believed thateven a new representation can be constructed with thecombination. The adaptable attribute to novel
environment could be used to realize an autonomously
adaptive system.
Although current AI technologies like Deep Learning
are gaining popularity, it is pointed out that the process isa black box. Features for internal representation areautomatically optimized by using large amount of learning
data. Eventually visibility in the internal process degrades
because human-friendly features are not always extracted.
An externally controllable and transparent system ispreferable in practical use.
3.2. Conceptual Design of Brain Inspired Cognitive
Computing System
The conceptual diagram of Brain Inspired Computing
System was designed (Fig. 1). It consists of 3 parts,
encoding module which inputs environmental stimuli asinternal information, integration module which integrates
features extracted with respect to each modality anddecision module which handles manipulation on
categories. Each module is composed of sub modules.
The encoding module translates system inputs intofeature vectors following a common format. First, themodule decomposes its inputs into primitive elements.
Then it extracts features from respective elements. The
format of the vector does not depend on the difference ofmodality.
The integration module integrates selected element
representation. The module infers the element

representation from feature vectors while complementing
the damaged information. It selects appropriate
representations across multi-modalities according thesystem purpose.
The decision module outputs a category suited for thesystem inputs. The module makes a novel category ifnecessary, when the input is confusing and cannot becategorized.
In recent years, brain science research is showing
significant progress in the development of observation
technology such as fMRI, NIRS and others. We
instantaneously utilize the cutting-edge results towardrealization of Brain Inspired Cognitive System.
4. Architecture Design
We designed a system architecture in the basis of thepreviously described conceptual diagram. Above all, weplace emphasis on a manner to easily implement theresearch result into the system. Brain function’s research
is progressing day by day. It is important that researchers
can try their models in a moment of inspiration on afeasible platform. We show our proposed platform in Fig.
2.
4.1. Features of Platform Architecture
The features of our proposed architecture are as follows.
 Event-driven module execution optimized across thewhole system Data handling structure sharing any data via Brain
Information Database Graphical user interface (GUI) with internal
information monitors and query-based commands forBrain Information Data

5. Conclusion
We considered the cooperation between brain scienceand information science and discussed realization of a
new computing system based on the research results ofhuman cognitive functions. The previous computing
system has evolved with the evolution of semiconductor
technology increasing the amount of computational
resources. The realization of computing systems that
follow the brain would change the perspective ofimprovement from "Quantity" to "Quality". In the future,
the interaction between two science regions will
accelerate the development of both sides. We hope thatour proposed platform can contribute to research beyond
the fields of information science and brain science. We
believe the new paradigm brought by Brain Inspired
Cognitive Computing is able to solve hard problems wewill face in near future.
Fig. 2 Platform Architecture
Acknowledgments
Human brains feature modularity that separated regions
work cooperating with each other. Multiple sensory inputs
can be processed in parallel modules. In spite of theindependent and parallel behavior of modules without
central control, gathering of the modules look to beappropriately optimized as a whole. If the optimizationmanner can be applied, we believe the total system canperform more than summation of functional modules
although even each module is independently designed.
A functional modules is driven by specific events andshares data with other modules via “Brain Information
Database (BIDB)”. A functional module is a process that
implements the aforementioned modules of Fig. 1. Eventmessages such as completion of a specific module arecommunicated between modules through “MessagingBus”. The chain of events forms the processing flow. An
event message can be received in multiple modules.
Modules do not share data directly. Data stream optimized
as a whole system is communicated between functional
modules via DB we named as BIDB. Any data is stored
into BIDB with history and can be loaded from anymodules. Indirect communication between modules
keeps the independence of the module and facilitates theaddition and recombination of modules.
Data communicated between modules can be visualized
by GUI. Any data such as extracted features, inferredelement representation, category classification history andothers from the past to the present are stored into BIDB
according to the published specifications. The data for any
moment can be accessed, so that queries can be issued toobserve the internal state and to analyze the processes
leading to cognition.
The platform makes it easy to implement models ofvarious status and to connect each model. We believe theeasy-to-implement manner boosts the cycle of brain
function’s research and its model implementation.

We would like to thank those involved in OsakaUniversity Graduate School of Information Science andTechnology, Frontier Bioscience, Engineering andMedicine, and NICT Center for information and NeuralNetworks, who offered valuable discussion and support.
This research and development work was supported bythe MIC.

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