#  The whole brain architecture approach: Accelerating the development of
artificial general intelligence by referring to the brain

Hiroshi Yamakawa  The Whole Brain Architecture Initiative, Nishikoiwa 2-19-21,
Edogawa-ku, Tokyo, 133-0057, Japan  The University of Tokyo, 7-3-1 Hongo,
Bunkyo-ku, Tokyo 113-0033, Japan  RIKEN, 6-2-3, Furuedai, Suita, Osaka
565-0874, Japan

###### Abstract

The vastness of the design space created by the combination of a large number
of computational mechanisms, including machine learning, is an obstacle to
creating an artificial general intelligence (AGI). Brain-inspired AGI
development, in other words, cutting down the design space to look more like a
biological brain, which is an existing model of a general intelligence, is a
promising plan for solving this problem. However, it is difficult for an
individual to design a software program that corresponds to the entire brain
because the neuroscientific data required to understand the architecture of
the brain are extensive and complicated. The whole-brain architecture approach
divides the brain-inspired AGI development process into the task of designing
the brain reference architecture (BRA)—the flow of information and the diagram
of corresponding components—and the task of developing each component using
the BRA. This is called BRA-driven development. Another difficulty lies in the
extraction of the operating principles necessary for reproducing the
cognitive-behavioral function of the brain from neuroscience data. Therefore,
this study proposes the Structure-constrained Interface Decomposition (SCID)
method, which is a hypothesis-building method for creating a hypothetical
component diagram consistent with neuroscientific findings. The application of
this approach has begun for building various regions of the brain. Moving
forward, we will examine methods of evaluating the biological plausibility of
brain-inspired software. This evaluation will also be used to prioritize
different computational mechanisms, which should be merged, associated with
the same regions of the brain.

###### keywords:

Brain reference architecture , Structure-constrained Interface Decomposition
method , Brain information flow , Hypothetical component diagram , Brain-
inspired artificial general intelligence , Whole brain architecture

††journal: Neural Networksmytitlenotemytitlenotefootnotetext:

##  1 Introduction

The development of artificial general intelligence (AGI), which is the goal of
advanced artificial intelligence (AI) research in recent years, is to develop
and demonstrate the extensive general intelligence possessed by humans within
a computational system [1, 23]. An exceptionally essential ability of AGI
would perhaps involve solving various problems, including those on unknown
issues, by flexibly combining knowledge gained from experience However, the
method to develop AGI remains unclear. Many AI researchers believe that the
development of deep learning [33] was a launch pad towards this goal.
According to them, this goal can be realized by combining various
computational mechanisms, including machine learning, a method that allows a
machine to learn knowledge from experience. Certainly, there have been
attempts to create a unified theory and principle of intelligence [19, 14,
25]. However, there is no single theory on which the entire sphere of
intelligence can be built. Thus far, the development of AGI has made progress
by repetitively tuning various limited issues. Nevertheless, such an approach
would make it difficult to design an AGI with a flexible problem-solving
ability that would enable it to solve unknown issues. Building an AGI that has
the full extent of human abilities would require an extremely large design
space as a result of combining computational mechanisms. This design space
could also be explored mechanically [12], but for the time being, it is
difficult to secure the required computational complexity.

Brain intelligence is associated with a certain degree of versatility. If we
can narrow down the design space by referring to the architecture of cognitive
and behavioral functions in the brain [40], we can accelerate the development
of an AGI that is comparable to human intelligence. Simply put, even if the
scope of AGI realized by machines (machine kingdom) does not need to be bound
by biological constraints [28], the development of a brain-like architecture
could be an agreed-upon milestone in AGI development [26, 24].

Based on this technological background, we at the Whole Brain Architecture
Initiative (WBAI) have been advocating for a development approach called the
whole brain architecture (WBA) approach since 2015. We define the basic idea
of this AGI development approach as creating ”a human-like artificial general
intelligence by learning from the architecture of the entire brain” [61, 5]

The premise of this approach is that the functions of the brain can be
realized by combining several machine learning devices with well-defined
functions. By imitating the structure of the brain and combining machine
learning devices that imitate each brain function, it is possible to build an
AGI with abilities that are similar to or beyond those of humans. This
approach, therefore, uses a WBA-centered hypothesis. Based on this hypothesis,
a machine learning module is developed for each part of the brain and the
software is constructed by integrating these modules.

Based on these assumptions, the WBA approach aims to build a brain-inspired
AGI based on the following basic ideas. As shown in Fig.1, each brain organ is
implemented as a calculation module, including those that utilize machine
learning, and is integrated based on the architecture of the brain. The brain
organs shown in the figure are fairly coarse, but in reality they are
associated with the brain in units of finer-grained computational modules (see
2.1).

Figure 1: Basic scheme of a whole-brain architecture approach. This is a
revised version of a simplified diagram of the basic idea of the WBA approach,
which has been gradually formed since around 2014. The left side of the figure
highlights major examples of large organs in the brain, including the
neocortex, basal ganglia (BG), hippocampus, and amygdala. An additional brain
architecture is formed by the connections between these organs (not shown). On
the right side of the figure, computational modules, including those utilizing
machine learning, are placed and connected with reference to the architecture
of the brain. Building an artificial intelligence software system that can
operate while interacting with the environment through the body is based on
this scheme.

It is not realistic to construct a brain-inspired AGI software by directly
referring to neuroscientific findings in academic papers and data. This is
because the functions of the brain are diverse, and the neuroscientific
findings about these functions are vast. Furthermore, the number of people who
thoroughly understand neuroscience and can develop software are limited, as it
involves intensive training.

To overcome this problem, WBAI standardizes information corresponding to the
requirements for developing brain-inspired software as brain reference
architecture (BRA) data. The methods of BRA design and utilization are shown
in Fig. 2 [45]. We refer to this as BRA-driven development.

Figure 2: BRA-driven development, which consists of the work of developing
brain-inspired software using the BRA and the work of designing the BRA based
on neuroscientific knowledge (papers and data). The BRA consists of the brain
information flow (BIF) and the hypothetical component diagram (HCD).

The BRA is a mesoscopic-level reference architecture (discussed later). It
contains a description of the brain information flow (BIF) within the
anatomical structures that is consistent with the hypothetical component
diagram (HCD) related to computational functions. The BIF is a directed graph
made up of partial circuits and connections representing the anatomical
structure of neural circuits. The HCD is a representation of a directed graph
that details the dependency relationship of components and can correspond to
any subgraph in BIF (see Subsection 2.3 for details).

Therefore, even a developer with limited knowledge of neuroscience can
implement the software provided that the BRA is given, and the HCD contained
in the BRA is regarded as a requirement.

The development of brain-inspired software requires HCDs that cover all the
regions of the brain. However, creating such HCDs is not an easy feat. Even
with the recent, rapid developments in neuroscience, the operating principles
have been described for only a few regions in the brain. To overcome this
situation, we have developed the Structure-constrained Interface Decomposition
(SCID) method. This method allows the creation of an HCD while compensating
for the lack of information on operating principles. One advantage of SCID is
that it is able to design HCDs consistent with the anatomy of a fairly wide
area of the brain. Therefore, the SCID method is a powerful technique to help
us create a BRA that covers the whole brain. An AGI can make effective
inferences in a situation that it has not directly experienced. Therefore, it
must have the ability to efficiently acquire knowledge common to various tasks
from finite experience, while focusing on various parts of the world, and to
flexibly combine them. However, software developed with reference to different
HCDs can only be implemented for each task that the respective HCD assumes;
therefore, acquiring and using knowledge for tasks apart from those tasks tend
to be difficult. This situation needs to be overcome in AGI development. In
this regard, the WBA approach will use merge development, which integrates
multiple different computational mechanisms related to the same brain regions
with a certain granularity. In the near future, BRA-based developments will
proceed alongside a parallel or iterative development that will merge the
deliverables of BRA-based developments.

As BRA-driven developments progress, their deliverables tend to move away from
the reality of the brain, which is a problem because the WBA approach aims to
explore AGI within a design space close to that of the brain. To circumvent
this problem, it is necessary to continue evaluating how well the implemented
brain-inspired software reproduces the truth of the brain (the parts related
to the level of cognitive behavior) perceived by neuroscience. The evaluation
of such biological plausibility is carried out from two viewpoints. The first
evaluation is adequacy for BRA to assess whether it is consistent with
existing neuroscience findings. The other is the evaluation of fidelity or
whether the software is built according to the BRA [58, 60].

Section 2 delves into the BRA and discusses the description level at which the
brain-inspired AGI should learn about the brain; the BIF format, which is an
element for describing the BRA; and the HCD. Furthermore, we will also provide
an overview of BRA-driven development. Section 3 discusses the collection of
neuroscientific findings related to BRA design, SCID, and the evaluation of
BRA validity. Section 4 discusses the stub-driven development and merge
development using the BRA and methods of evaluating the fidelity of software
from the perspective of the BRA in the future. Finally, these discussions are
summarized.

###  1.1  Related AGI development approaches

At the end of 2020, there exists at least 72 projects involved in AGI
development; looking at the changes since 2017, the overall number has not
changed much. However, 15 new projects, mostly from the private sector, have
been added during this period[17, 7].

In many AI projects, the researcher designs, implements, and evaluates a
combination of computational mechanisms that enable the software to perform
cognitive-behavioral functions. Projects following the traditional flow that
starts with symbolic AI have a large design burden. Examples of these problems
are ACT-R [4], SOAR [32], ICARUS [11], LIDA [18], NARS [55], Sigma [43],
CLARION [47], and CogPrime [22]. Hierarchical Temporal Memory [27, 29] is an
advanced AGI development project that mainly started with mimicking
neocortical functions in the brain using an artificial neural network and the
brain’s learning mechanism [27, 29]. Part of that flow was taken over by the
Vicarious project [21]. Many projects started around 2015, when steady strides
in the development of deep learning began. For example, in addition to OpenAI
[10], which produced remarkable language model results in recent years, there
is GoodAI, NNAISENSE, and WBA. Founded in 2010, DeepMind has been developing
AGI centered on deep learning and reinforcement learning while referencing the
brain with a relatively high level of abstraction [36, 46]. The Nego project
[15, 13] is attempting to mimic the entire cognitive-behavioral function of
the brain at the level of spiking neurons; however, the project is not
considered to be explicitly challenging AGI [17].

Related research areas to explore cognitive systems include biologically
inspired cognitive architecture [44, 24] and cognitive computational
neuroscience [30], an interdisciplinary field of cognitive science and
computational neuroscience, among others. However, these fields have not made
progress in accumulating design data in a standardized way like the BRA.

Given that the brain is a type of natural object, it is rational to design AI
by imitating physical processes that are similar to physical simulation and to
evaluate it by how well the physical stimulation has been reproduced. In
practice, studies on whole-brain simulation have been carried out with this
perspective [9, 34]. If the physical processes that support cognitive-
behavioral function can be fully elucidated and a basic equation can be
developed, it is highly possible that human-like intelligence can be
reproduced through such an approach. However, neuroscience, as it is right
now, has not yet reached this level of detail, and it is hard to tell when it
will achieve this. This is because it is still difficult to observe the neural
activity of the whole brain with sufficient spatiotemporal resolution. Even if
the activity can be observed, it remains difficult to connect complex
processes in the living body to functional operating principles.

##  2 Brain Reference Architecture

The BRA is a reference model that aggregates neuroscientific findings (on
anatomical structures or physiological phenomena) on the mesoscopic-level
architecture of the brain and gives a hypothesis of computational function
consistent with those findings. Specifically, as shown in Fig. 3, the data is
a combination of BIF (see Subsection 2.2) and HCD (see Subsection 2.3) [45].

Figure 3: Brain reference architecture. The BRA consists of the BIF and the
HCD. The BIF is the mesoscopic level information flow in anatomical
structures. The HCD is a diagram that organizes functions in a way that is
consistent with the anatomy of a given circuit. There can be multiple HCDs for
a single circuit for the BIF. Each software development project is essentially
based on a specific HCD.

The current WBA approach involves BRA-driven development. This development
consists of a BRA design based on neuroscientific findings and BRA utilization
that implements and evaluates its processes by referring to the HCD in the
BRA.

After describing the BRA at the mesoscopic level, we will discuss the BIF as a
component of the BRA and HCD, which are designed to be consistent with the
BIF. We will also discuss BRA-driven developments

###  2.1  Referenced mesoscopic level

What part of neuroscience should the WBA approach refer to? Generally, the
following can be said about the granularity that should be referred to when
trying to reproduce the cognitive-behavioral function of the brain. Coarse-
grained references to large regions of the brain (e.g., neocortex,
hippocampus, basal ganglia, and cerebellum) are too weak to recreate
cognitive-behavioral functions of the brain. However, conversely, trying to
reproduce the details inside the neuron (e.g., metabolism) complicates the
construction of the design and increases the computational resources required
to run a model that reflects this information, resulting in great
disadvantages. Therefore, the reference granularity in the WBA approach should
be assumed to be at the mesoscopic level.

####  2.1.1 Uniform circuit: Finest reference granularity for brain
architecture

In component diagrams that attempt to associate neural circuits, the smallest
unit to be described is the argument specified in the interface of each
component (see the right side of Fig. 6). As an important property of this
argument, the transmitting side and the receiving side have a transmitted
signal in common. In communication between living neurons, the receiving
neuron biochemically identifies its type or subtype by coming into contact
with the axon tip of the transmitting neuron (see Fig. 4). In this regard, a
mostly homogenous neuron population belonging to the same brain region is
defined as a uniform circuit. Then, because it is a uniform circuit, it can
function as an argument in the software.

Figure 4: Uniform circuits. Uniform circuits are composed of the same type or
subtype of neurons that are spatially localized. Each neuron in the
transmitting uniform circuit that extends axons to the receiving neuron will
be recognized as physiologically identical on the receiving side.

In well-studied regions of the brain, there are some known examples wherein
variables that describe computational models related to cognitive-behavioral
function match the uniform circuit. It is often suggested that this level of
anatomical granularity has been implicated in the construction of cognitive
and behavioral functions across species [8, 39]. Therefore, in the WBA
approach, we decided to essentially refer to neuroscientific findings at the
mesoscopic level with the uniform circuit as the lower limit of descriptive
granularity.

###  2.2  Brain information flow

The BIF describes the anatomical structure of the whole brain at the
mesoscopic level [6] (see Fig. 5). As such, it is not intended for specific
tasks in the environment. BIF is a graph, and its basic structure consists of
a node called a ”circuit” and a directed link called a ”connection.” The
smallest unit of the graph is the uniform circuit defined in Subsection 2.1,
and this unit is also the ideal starting point for a connection. A circuit is
also a graph containing multiple uniform circuits. Furthermore, multiple
circuits may have overlapping portions.

Figure 5: Brain information flow. BIF is the flow of information at the
mesoscopic level in anatomical structures. Circuits are arbitrary brain organs
or regions and the assemblies to which they are connected. The smallest
circuit to be described is, in principle, a uniform circuit. Because a uniform
circuit is a collection of neurons of the same type, its properties, such as
transmitters, are specified. A connection can be characterized by a particular
uniform circuit that is the origin of the connection output from a circuit.

The circuits and connections are discussed below.

####  2.2.1 Circuits (nodes)

A circuit is a component that becomes a node in the graph structure of the
BIF. A uniform circuit is a special circuit that is the lower limit of the BIF
granularity. A circuit can generally be any sub-circuit in the brain. It may
indicate areas such as the entire visual cortex or only V1 (i.e., the primary
visual cortex), or it may correspond to the neocortex-basal ganglia loop. The
attributes possessed by all circuits include labels, partial circuits, animal
species, and references. Furthermore, unique attributes of the uniform circuit
include neurotransmitters, excitatory and inhibitory modes, and cell count.

####  2.2.2 Connections (links)

Connections correspond to a bundle of axons responsible for signal
transduction between circuits in the brain and are represented by a direct
link. The main attributes possessed by one connection is the input circuit,
which is a uniform circuit that transmits signals, and the output circuit,
which sends signals from the connection. Furthermore, animal species, the size
corresponding to the number of axons, neurotransmitters, and references can be
added to the connection’s description if necessary.

###  2.3  Hypothetical component diagram

The component diagram of the Unified Modeling Language [3] is used to model
and explain the structure of any complex object-oriented software. This is a
diagram that shows the structural aspects of the operating principle of
software as a network using socket labels to indicate the components that are
responsible for the computing functions; it also shows the interface of the
call relationship between those components (see the right image in Fig. 6).
This diagram is used to visualize, specify, document, and build an executable
system by forward or reverse engineering.

Figure 6: Example of BRA description that associates the BIF for the basal
ganglia with the HCD for actor-critic reinforcement learning. The blue letters
A, B, C, and D represent uniform circuits in the region of interest (ROI) in
the BIF (left panel). The corresponding components in the HCD (right panel)
are marked with the same letters, and the HCD components are marked with their
functions. The meaning of the signal to be transmitted is indicated with the
interface between the components. In the BIF, TRN indicates a thalamic relay
neuron. This diagram was adapted from the diagram of [50, 56].

The HCD that makes up the BRA is a type of component diagram. This diagram
assigns components that match the function of the brain region of interest
(ROI) with an anatomical structure at a mesoscopic level; however, this
diagram is hypothetical in the sense that there is no guarantee that it is
consistent with the truth of the brain function. The assignment is performed
using the SCID method, which will be discussed later. Each component that
makes up an HCD is a module that encapsulates a set of related functions (or
data) 111The term ”component” is also used in software engineering to mean
software packages, web services, web resources, and similar entities; however,
in this paper, we use it to refer to a module that encapsulates a set of
related functions (or data). and corresponds to the behavior and structure of
specific organs and regions of the brain.

As a typical example of a BRA, the association between BIF and the HCD is
shown in Fig. 6, which shows the well-known example [50] of the basal
ganglia’s actor-critic reinforcement learning function. In the left diagram
showing the basal ganglia loop, the basal ganglia circuit is the ROI. The
corresponding HCD that decomposes the actor-critic reinforcement learning
function is shown on the right side. The uniform circuit named striatum
(matrix) and indicated by the letter A in the BIF diagram corresponds to the
action value calculator component indicated by the letter A in the HCD. The
uniform circuits in BIF similarly correspond to the components in the HCD, as
indicated by other letters (B, C, and D). The following is an example of
mapping the links between the two diagrams. The signal path (labeled dopamine)
output from the SNc, indicated by the letter D in the BIF, maps to the signal
path (labeled TD error) output from the TD error calculator component,
indicated by the letter D in the HCD. Note that in this example, the circuits
indicated by A, B, C, and D in the BIF are all uniform circuits. Therefore,
the label names given to the respective components in the HCD correspond to
the label names of the arguments they provide.

In this way, the availability of an HCD, which shows the structural aspect of
operating principles, increases the likelihood that even developers without
profound expertise in neuroscience would be capable of implementing software
closer to the truth of the brain. Network machine learning systems that are
often used in current AI research (e.g., artificial neural networks and
Bayesian networks) are made compatible with development according to component
diagrams.

###  2.4  Prototype of BRA database

The brain is a fairly closely linked system. Therefore, the accumulation of
standard neuroscientific findings related to cognitive behavior will not only
optimize the development of brain-inspired software but also help to
comprehensively grasp mesoscopic findings in the whole brain.

In this regard, the WBA approach examines databases to improve reusability by
integratively handling the constructed BRA. In the present study, a prototype
of the BIF database was made using Semantic MediaWiki.

The data flow was as follows. First, one of the authors with expertise in
neuroscience reviewed academic papers and collected relevant data in a
spreadsheet. Next, the data were registered in a database using a conversion
tool. Thereafter, when the developers implemented the brain-inspired software,
a prototype of a tool was made, which could not only directly browse the data
but also visualize BRA data as a graph in the ROIs.

Such activity also can be positioned as part of the field of neuroinformatics
[2, 42], which develops data and knowledge bases for neuroscience. Currently,
that field is vigorously registering experimental data on anatomical
structures [31] and physiological phenomena [41]. However, there has been no
progress in accumulating data for designing cognitive and behavioral
functions, such as a BRA.

###  2.5  BRA-driven development

BRA-driven development is a developmental approach that builds brain-inspired
AGI through the following processes using a standardized BRA (see Fig. 7).

  * 1.

BRA design: Designing BIF by collecting and organizing neuroscientific
findings. Furthermore, creating an HCD using the SCID method (discussed
later).

  * 2.

BRA use: Developing brain-inspired software by referring to the HCD in the
BRA.

Figure 7: Activities of BRA-driven development. The major activities of BRA-
driven development include three types of activities for designing BRA and
three types of activities for using BRA.

By doing so, the developer can develop brain-inspired software guided by HCDs
in the BRA and can compensate for the lack of people who have expertise in
both neuroscience and software engineering.

Here, we will give an overview of the three activities involved in BRA design.
The first activity is laying the foundation for accumulating BRAs useful for
brain-inspired software. Specifically, this involves deciding on the
description format (BIF, Subsection 2.2) and examining databases for
integrated sharing of BRAs (Subsection 2.4). (① What can be learned in a
brain?) The next activity is acquiring and formulating the knowledge necessary
for the BRA. Specifically, knowledge of the anatomic structures and
psychological phenomena in some region of the brain is collected and amassed
in BIF form. Those BIF data are used to construct HCDs through the SCID
method. (② How can knowledge be obtained from the brain?) The third activity
is evaluating the adequacy of BRA data. In this activity, review criteria are
determined, and one judges whether the created BRA data satisfy the necessary
requirements as a reference model of brain-inspired software. (③ How to assess
the resemblance to a brain.)

Furthermore, we will give an overview of three activities associated with BRA
utilization. First, in the development of brain-inspired software, the HCD
associated with a specific task in the BRA is implemented as a requirement. (④
Build the software like a brain.) Next, in the future, we plan to carry out
merge development wherein components in separately developed programs are
associated with each other based on BRA and integrated. (⑤ Merge disparate
functions as the brain does.) Furthermore, to estimate how well the
implemented software represents the brain, the fidelity (biological
plausibility) is evaluated by comparing the BRA and the program. (⑥ Evaluate
the resemblance to a brain.)

As previously mentioned, to complete the brain-inspired AGI, it is necessary
to integrate multiple computational mechanisms that correspond to the same
brain regions and were created according to, for example, diversity of tasks,
in each BRA-based development project. Therefore, we believe that the entire
development based on the WBA approach in the near future will proceed in
parallel or iteratively with BRA-based developments and merge developments. In
that case, the fidelity evaluation of the software will prevent development
results from veering away from brain architecture.

##  3 Designing BRA

In this section, we discuss the BRA design. Among the three activities related
to the BRA design, the first part, ”① What can be learned in a brain?” was
already discussed in Section 2. As for ”② How can knowledge be obtained from a
brain?” we will explain the collection of anatomical findings in the
neuroscientific field and the SCID method for HCD construction. Furthermore,
we will also touch on the evaluation of adequacy in the context of ”③ How to
assess resemblance to a brain.”

###  3.1  Neuroscientific findings available for BIF creation

We now discuss the process of acquisition of information related to the
anatomical structure required to describe the BIF. The main requirement is
information for building directed graphs with circuits as nodes. Therefore, it
is ideal to acquire information on uniform circuits of the whole brain and the
connections between these circuits. With the current state of neuroscience, we
are still far from acquiring ideal information. In this regard, if necessary,
circuits larger than the uniform circuit are defined, and a BIF graph is
constructed between these circuits.

Information to be acquired for each uniform circuit includes the brain region
labels, animal species, neurotransmitters, excitatory and inhibitory modes,
cell count, and information source (references). The information to be
acquired regarding the connection includes the input circuit, output circuit,
animal species, size (number of axons), neurotransmitters, and sources
(references). For the neocortex, the orientation of the hierarchy between
territories (including feedforward/feedback) is required.

Furthermore, the data described in the BIF is used to implement software using
an artificial neural network. Thus, it is ideal to know the number of neurons
in a circuit and the approximate connection sizes (number of axons).

A BRA used to build human-like intelligence should, of course, be based on the
structure of the human brain. However, streamlining the construction of the
BIF may be possible by referring to the findings in other animals,
particularly rodents. For this reason, in reality, the BIF mainly uses human
data for the neocortex, which is unique to humans; however, for other brain
regions, there are several references based on non-human primates and rodents
[38]. Therefore, although similar to humans as a whole, the BIF seems to
contain chimeric data that combines mesoscopic-level anatomical findings from
multiple mammals.

####  3.1.1 Information sources

The main source of information for building a BIF is data on anatomical
structures (such as connectomes) and related literature.

####  3.1.2 Region labels

In principle, the Allen Developing Human Brain Atlas ontology, which is one of
the Allen Brain Reference Atlases (https://atlas.brain-map.org/), is used as a
circuit label, and if necessary, a label with a level roughly corresponding to
the granularity of the uniform circuit is added.

####  3.1.3  Number of neurons

The number of neurons for each region in the mouse brain is stored in the Blue
Brain Cell Atlas [16]. These regions are defined by the Allen Mouse Brain
Reference Atlas [31]. However, to date, no comprehensive data on the number of
neurons in humans are available.

####  3.1.4 Connection

As for the connections between circuits, we would like to gather information
on the presence of the connection, its direction, and the approximate number
of projection axons for all combinations of areas.

Although that is not necessarily an exhaustive brain region, we can estimate
the projection ratio from one particular area to another using the Allen Mouse
Brain Connectivity Atlas [39]. As mentioned above, because the number of
neurons in an area can be obtained for mice, the number of axons to be
projected can be estimated by multiplying the projection ratio by the number
of neurons.

In humans, the Multilevel Human Brain Atlas by EBRAIN
(https://ebrains.eu/service/human-brain-atlas/ . Accessed: 2021-2-26.) can be
used to obtain data, including hierarchical relationships
(feedforward/feedback), for the entire neocortex.

####  3.1.5  Neurotransmitters

The data on the distribution of neurotransmitters throughout the brain is
currently available via Drosophilidae studies [35], but there appears to be no
data for mammals. However, in the regions involved in higher intelligence
processing, such as the neocortex, thalamus, basal ganglia, hippocampus, and
cerebellum, similar anatomical structures appear frequently in each brain
region. Moreover, because the neurotransmitters in these sites have been
studied in detail, the lack of data does not pose a major problem in the
construction of the BIF. Nevertheless, data for subcortical brain regions may
be needed.

###  3.2  Structure-constrained Interface Decomposition method

The SCID method involves consistently decomposing the computational functions
of a specific brain region into mesoscopic-level anatomy to obtain the HCD
required for the development of brain-inspired software. In software
development, designing through a process of decomposing higher-level functions
is common; however, the SCID method also considers consistency with the
anatomical structure of the brain.

Furthermore, decomposing the functions of the natural brain as if it were an
artifact might not yield desired results. However, because the brain is an
organ that has undergone evolutionary selection, its physical mechanism often
serves an intended purpose. For example, when computational neuroscience
derives ”algorithms and expressions” for brain function, this action is
premised on a clear purposefulness.

####  3.2.1 Process of SCID method

In the SCID method, an HCD consistent with the anatomical structure in the ROI
is obtained by performing the following three-step process (see Fig. 8).

Figure 8: Procedure of SCID method. The SCID method is a technique for
obtaining the HCDs required for software development. The method uses a three-
step process to decompose the top-level functions (TLFs) of a specific brain
region (ROI) into components under mesoscopic-level anatomical constraints. In
Step 1, three exploratory tasks (1-A, 1-B, and 1-C) are performed in parallel
as appropriate. After that, we proceed to Steps 2 and 3.

In step 1, the findings of various studies related to cognitive behaviors of
humans and animals are used to establish the premise that the SCID method is
applicable. Specifically, the three processes are performed in parallel. While
investigating the anatomical structure around the ROI and registering it as a
BIF (1-A), the existence of a component diagram that realizes the ROI
input/output (1-B) is confirmed. A valid brain ROI and the top-level function
(TFL) that it performs are determined (1-C).

In step 2, while considering the association between circuits and connections
in the ROI of the BIF, the TLF is decomposed into detailed functions in as
many conceivable patterns as possible. This step enumerates candidate HCDs.

In step 3, HCDs that are logically inconsistent according to scientific
findings in various fields, such as neuroscience, cognitive psychology,
evolution, and biological development, are rejected. Then, the function of
components and meaning of connections of the remaining HCDs can be assigned to
the BIF.

####  3.2.2 Advantages of SCID method

In neuroscience, the traditional way to experimentally identify the function
of a neural circuit of interest is as follows. This is a method of finding
neural activity that has an intelligible correlation with some external
stimulus, and giving a functional interpretation to it based on the nature of
that stimulus. However, this is only possible if there are brain regions close
to the sensor/actuator or neural activity that has a clear correlation with
the behavior, such as reward/place cells. In general, it is not easy to obtain
interpretable correlations from neural activities that are mixed with various
types of external and temporal information in most parts of the brain’s neural
circuitry. Thus, there is a weakness in that the range of neural circuits with
functions that can be identified by this method tends to be limited (see Table
1).

The SCID method can be applied to a quite wide area of the brain. This is
because anatomical structure information at the mesoscopic level, which is key
to the SCID method, can be obtained from almost the entire brain, including
that of rodents (see Subsection 3.1).

Another advantage of the SCID method is that an HCD is easy to use directly in
software development because it is obtained through a process based on the
design theory of software development. In contrast, when neural activity
phenomena correlated with external information are used as a reference for
software development, they need to be reinterpreted as a requirement. In other
words, the functions obtained through the traditional phenomenon-based
approach [59] often are indirect information and need preparation for software
development.

Table 1: Advantages of the SCID method Method |  SCID method |  Conventional method
---|---|---
Key clues |  Structure and top-level function (also physiological phenomena) |  Neural phenomena correlating with the environment (e.g., reward and place cells)
Coverage in the brain |  Almost all of the brain (to the extent that mesoscopic structures are known) |  Limited to areas where physiological clues exist.
Features |  Functional descriptions that are easy to use for development |  Phenomenal interpretations are indirect and software specific.

The first HCD developed using the SCID method identified the site responsible
for path integration in the entorhinal cortex [20]. Following this, it was
also used to identify the meaning of signals between neocortical regions [57].
At present, there are several ongoing studies on the application of the SCID
method to study brain regions, such as the brain stem, which is responsible
for eye movements [54]; the claustrum; and functions, such as imagination.

###  3.3  Evaluation of adequacy (biological plausibility)

####  3.3.1 Need to evaluate biological plausibility

When developing brain-inspired software, it is necessary to evaluate
biological plausibility—in other words, to estimate how close the implemented
brain-inspired software is to the reality of the brain captured by current
neuroscience findings.

The evaluation of biological plausibility in BRA-driven development involves
the following two methods (see Fig. 9). The first method is the evaluation of
adequacy, which estimates the consistency between existing neuroscientific
findings and BRA. The second method involves the evaluation of fidelity, which
estimates the reproducibility of BRA in brain-inspired software.

Figure 9: Creation and evaluation in BRA-driven development. In BRA-driven
development, the biological plausibility is evaluated in the direction
opposite to that of creation. Contrary to the SCID method of designing BRAs,
the adequacy of BRAs to neuroscientific findings is assessed. The fidelity of
the software to the BRA will be assessed, contrary to the development of the
software. Although not shown in the diagram, in merge development, an HCD is
used for both development referencing and fidelity evaluation.

####  3.3.2 Need for certified registration

The created BRA is used as a functional requirement to be referred to when
implementing software and as a subject for comparison when evaluating
biological plausibility (fidelity). However, most BRA users have little
knowledge of neuroscience, and therefore cannot judge whether the BRAs created
are trustworthy. To ensure the adequacy of BRA data, there needs to be a
workflow that inspects and certifies the data before they are registered.

Often in neuroscience, there are parallel hypotheses that are contradictory to
each other but cannot be ruled out. From the viewpoint of brain-inspired
software development, it is not possible to immediately determine which
hypothesis is ideal. Therefore, provided that it meets the inspection
criteria, the BRA data should be registered even if they contradict other
data.

.

####  3.3.3 Evaluation of adequacy and inspection criteria

The evaluation of adequacy is further divided into two parts as shown in Fig.
9. .

1) Adequacy evaluation of BIF

This method evaluates the consistency of the anatomical structures and neural
activity described in the BIF with those described in neuroscientific papers
and data.

Two main inspection criteria are used to certify that the BIF description is
sufficiently adequate. One is that the description element of the structure or
phenomenon described in the data submitted for registration is not already
registered in the BRA database (novelty). The other is that the element must
be directly or indirectly supported by any current neuroscientific finding
(authenticity). As a rule, authenticity of facts is guaranteed by their
inclusion in one or more peer-reviewed articles.

2) Adequacy evaluation of HCD

The functionality of and consistency with the BIF are evaluated for the HCD.
The evaluation of functionality determines whether the process generated by
the behavior of structured components in an HCD constitutes a mechanism of
action that can achieve the goals of the ROI.

The evaluation of consistency determines whether the HCD corresponds to the
description of the BIF from three aspects: (1) the dependency structure of the
HCD corresponds to the anatomical structure contained in the ROI of the BIF,
(2) the behavior of the components within the HCD is consistent with the
physiological findings described in the BIF, and (3) the goal achieving
process of HCD is consistent with the physiological process described in the
BIF.

##  4 Development and Evaluation using BRA

In this section, we discuss three activities associated with BRA use in BRA-
driven development. As shown in Fig. 9, the development and evaluation
performed here is carried out with reference to the HCD in the BRA; as such,
the programmer does not need to possess profound knowledge of neuroscience.

###  4.1  Stub-driven development

To create brain-inspired software, BRA-driven development implements and
connects all components based on the requirements HCD associated with a
particular task.

Generally, machine learning devices often behave differently from the
architecture imagined at the design phase to some extent. The difficulty of
controlling this behavior increases rapidly if the system is composed of
several machine learning components. To address this difficulty, the WBA
approach uses stub-driven development.

In stub-driven development, during the early stages of development, a system
is constructed by combining components that do not have a learning function
and are described by rule-based processes. Following this, by gradually
replacing each component with machine learning components, the system is
improved so that it approaches the behavior expected in the HCD.

To proceed with development using a BRA, an integrated execution platform is
required as a management mechanism for computational resources to run and
train multiple components. Candidates include recent deep neural network
platforms, such as TensorFlow, PyTorch, and Keras. The Brain-inspired
Computing Architecture is a platform developed to take into account the
asynchronous nature of the brain and other characteristics [49]. Furthermore,
an HCD can be constrained and converted into a probabilistic generative model
(PGM), SERKET [37, 52], and Pixyz [48]. Recently, there has been a growing
movement called whole-brain PGM, which attempts to construct a PGM
corresponding to the whole brain [53]. Construction of a PGM of the
hippocampal formation has been started [51]

###  4.2  Evaluation of fidelity (biological plausibility)

The biological plausibility of brain-inspired software is evaluated by
comparing it with the BIF and HCD in the BRA data. The estimated degree of
consistency between the software and the BRA is called fidelity.

To date, four methods have been explored for evaluating fidelity.

  * 1.

Structural similarity: An evaluation of how well the static structure of the
software matches the BIF in the BRA.

  * 2.

Functional similarity: An evaluation of how well the behavior of a particular
component implemented during the execution of a specific task matches the
behavior (e.g., behavior timing) designed in the HCD in the BRA.

  * 3.

Activity reproducibility: An evaluation of how well the behavior of a specific
component implemented during the execution of a specific task given to a
circuit in the BRA reproduces the features of neural activity (e.g., activity
timing).

  * 4.

Performance: An evaluation of the performance and ability of the software as a
whole (integration test).

Of these evaluation methods, structural similarity and performance are easy to
use for evaluation of the entire software. However, functional similarity and
activity reproducibility are useful not only for unit tests for each component
but also for merge development, which will be discussed later. Furthermore, it
is possible to consider an evaluation method wherein dysfunction states are
induced by intentionally destroying/ablating a part of the software and
comparing it with brain function under conditions such as mental illness or
brain injury.

###  4.3 Merge development

A particular circuit on the BIF is associated with a component that is
included in various HCDs. As already mentioned, the HCD is a structure of
functions broken down into components to realize TLFs, including tasks.
Therefore, even if a component is implemented to realize the same circuit, the
function it performs may be different depending on the HCD to which it refers.

However, for a piece of software to show its true value as an AGI, it needs to
be able to apply knowledge to different tasks. To that end, if there are
components that correspond to the same brain region in separately developed
programs, merge development is performed by associating and integrating these
components. The idea of promoting the integration of components by using
constraints of the brain is called brain-inspired refactoring.

####  4.3.1 Concept of merge development

Herein, we explain that merge development is possible by imposing the BIF as a
development constraint.

As shown in the left side of Fig. 10, when developing two tasks, the
development is done independently if a BRA is not used as a reference. In task
1, input 1 is given to component A, and after processing, output 1 is obtained
from component E. Similarly, in task 2, input 2 is given to component B, and
after processing, output 2 is obtained from component F. The two
implementations created this way are completely different in essence and
cannot be merged.

Figure 10: Components to be merged are identified by BRA constraints. This
figure compares software development unconstrained by BRA (left panel) and
software development constrained by BRA (right panel). Task 1 produces output
1 from input 1, and Task 2 produces output 2 from input 2. Comparing the
component diagrams dealing with these two tasks, it is not easy to find common
points simply by developing them separately, even if there is a possibility of
overlap. However, if they are developed with reference to the BRA, the BIF
will constrain the HCD that the software follows. Then, components C and D,
which are responsible for common processing in the two tasks, can be
identified as components to be merged.

Next, as shown in the right side of Fig. 10, the case where two tasks are
developed with BRA as the constraints are discussed. In this case, components
C and D, which are responsible for intermediate processing, are associated
with the same circuit on the BIF, respectively. To merge components C and D
contained in different implementations, two approaches are typically
envisioned. The first is to compare the fidelity ratings of the two
corresponding components and select the higher-evaluated implementation. The
second is to redesign and implement an integrated algorithm that combines the
advantages of both.

##  5 Discussion

###  5.1 Structure and behavior

In BRA-driven development, we decided to use HCD as the main source of design
information because it expresses the structural aspects of functions.
Moreover, the accumulation of findings has progressed because the mesoscopic-
level structure in the brain, which is the basis of an HCD, embodies invariant
information that is not task-dependent. However, not only the structural
aspect but also the behavioral aspect is important in software design [3].
Furthermore, the intelligence finally evaluated in AGI relates to behavior.
Therefore, an important task in the future is to develop behavior-related
evaluations that are similar to functional similarity and activity
reproducibility used for fidelity evaluation.

###  5.2 Roadmap for reaching AGI

As mentioned above, BRA-driven development consists of BRA design comprising
the BIF and an HCD and development using them. Given the characteristics of
this development methodology, the following four milestones need to be
achieved on the way in the roadmap leading to the realization of a brain-
inspired AGI. The first milestone is the construction of a BIF for almost the
whole brain. The second milestone is the construction of an HCD that covers
almost the whole brain, and the third milestone is the construction of
integrated software that covers almost the whole brain. Finally, the fourth
milestone is to achieve a situation where AGI solutions can be explored in a
design space where constraints continue to be updated as neuroscience
progresses, primarily by running and testing integrated software in a virtual
environment. However, something may still be lacking even when all the
computational mechanisms that make up the brain seem to work together.
Regardless, we expect that the lacking technical elements will become apparent
once we arrive at that stage.

###  5.3 Applications of AI systems based on BRA

AI systems developed based on BRA can be expected to almost exactly replicate
human cognitive and behavioral capabilities. Therefore, it has several
practical applications, including the following: Its greatest use is that it
allows us to construct an AI that has familiarity with humans when
communicating with them. Furthermore, it can also be computationally applied
to research fields that deal with mental illness and cognitive impairment.
Conversely, findings regarding human cognitive impairment may be used for the
problematic behavior seen in brain-inspired AI. Moreover, we believe this can
also be used as a computational model that will serve as a device for mind
uploading.

##  6 Conclusions

This article introduces the current WBA approach and focuses on BRA-driven
development to accelerate the development of brain-inspired AGI. The BRA
includes standardized data that reflect the brain architecture for the purpose
of limiting the huge design space required for a human-level AGI that cannot
be grasped by one individual’s cognitive ability. By having BRAs designed by
people with expertise in neuroscience, even developers who do not have a deep
understanding of the brain can develop brain-inspired software based on BRAs.
First, we discussed that the BRA is a description consisting of a BIF
supported by a mesoscopic neural circuit and an HCD consistent with BIF. Next,
to compensate for the lack of neuroscientific findings, we introduced the SCID
method, which formulates the creation of an HCD consistent with the brain’s
anatomical structure. Furthermore, even if a BRA is used for development,
individual development results tend to diverge depending on the diversity of
target tasks. To address this problem, it is planed to introduce merge
development, which integrates and brings AGI closer to how the brain works.
Moreover, we discussed the evaluation of biological plausibility using BRA to
prevent the developed software from veering away from the brain.

The main contribution of this paper on BRA-driven development, having the
following features, is the establishment of a methodology for accumulating
data on brain constraints in a form that can be used for software development.

  1. 1.

Separation of design information: BRA data can be used in a variety of
development projects because they are described in a standard format for
software development that does not depend on any particular development
environment.

  2. 2.

Standardization of description granularity: As a rule, describing BRA data at
a coarser granularity than the mesoscopic level reduces the possibility that
development will get caught up in details that are unnecessary for the
realization of the target cognitive behavioral level.

  3. 3.

BRA design: The method of designing computational functions according to
anatomy (SCID method) allows BRAs to be created while compensating for the
lack of neuroscientific knowledge in a wide range of brain areas.

  4. 4.

Tolerance of diversity: Even BRAs that contain mutually contradictory HCDs can
be registered if they have a certain level of validity, thereby reducing the
risk of overly narrowing the design space to be considered.

The above features of the BRA will provide a scaffold for large-scale whole-
brain software development as the comprehensiveness of its data increases.
This will allow the brain architecture to exert a centripetal force as an
anchor that can efficiently bring about the convergence and eventual
completion of the development of human-like AGI, when currently the
development results in this field have a tendency to diverge.

## Declaration of competing interest

The author declares no known competing financial interests or personal
relationship that could appear to influence the work reported in this paper.

## Acknowledgements

I would like to thank Naoya Arakawa, Koichi Takahashi, Naoyuki Sakai, Masahiko
Osawa, Takashi Omori, Shinichi Asakawa, Kotaro Mizuta, Mei Sasaki, Hirokazu
Kiyomaru, Hitomi Sano, and Michihiko Ueno for cooperation in developing the
methodology for BRA-driven development. I would like to thank Haruo Mizutani,
Hiroshi Okamoto, Yudai Suzuki, Naoyuki Sato, Taku Hayami, and Hiroto Tamura
for constructing the basic data for the BIF. I would like to thank Kosuke
Miyoshi, Kotone Itaya, Masayoshi Nakamura, Tatsuji Takahashi, Masanori Yamada,
Taro Sunagawa, Shion Honda, Yutaka Matsuo, Yuji Ichisugi, Satoshi Kurihara,
and Ryutaro Ichise for implementing related software. I would like to thank
Ayako Fukawa, Takahiro Aizawa, Yoshimasa Tawatsuji, Akira Taniguchi, and Ikuko
Eguchi Yairi for advancing the SCID method. I would like to thank Haruhiko
Bito, Kenji Doya, Tadashi Yamazaki, Michita Imai, Hiroyuki Okada, and Nobuo
Kawakami for discussions on research and development. This work was supported
by the Japan Ministry of Education, Culture, Sports, Science and Technology
(KAKENHI Grant Number 17H06315, Grant-in-Aid for Scientific Research on
Innovative Areas, Brain information dynamics underlying multi-area
interconnectivity and parallel processing) and DWANGO Co., Ltd.

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