New revolutionary brain development technology for robots
The article discusses a new direction in computerization based on a revolutionary technology for processing various types of information (video, sound, text, etc. in real time) in a single homogeneous multidimensional active associative neural-like growing structure that allows you to create an elec...
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irk-123456789-1519262019-06-02T01:25:11Z New revolutionary brain development technology for robots Iashchenko, V.O. Обчислювальні системи The article discusses a new direction in computerization based on a revolutionary technology for processing various types of information (video, sound, text, etc. in real time) in a single homogeneous multidimensional active associative neural-like growing structure that allows you to create an electronic brain for android robots. У статті розглядається новий напрям у комп’ютеризації, заснований на революційній технології обробки різних типів інформації (відео, звуку, тексту та ін. у реальному часі) в єдиній однорідній багатовимірній активній асоціативно-нейроподібній зростаючій структурі, що дозволяє створювати електронний мозок для роботів-андроїдів. Нова технологія заснована на новому типі нейронної мережі: багатозв’язній, багатовимірній, рецепторно-ефекторній, нейроподібній, зростаючій мережі, що функціонує за аналогією з функціонуванням нейронних структур мозку людини. В статье рассматривается новое направление в компьютеризации, основанное на революционной технологии обработки различных типов информации (видео, звука, текста и др. в реальном времени) в единой однородной многомерной активной ассоциативно-нейроподобной растущей структуре, позволяющей создавать электронный мозг для роботов-андроидов. Новая технология основана на новом типе нейронной сети: многосвязной, многомерной, рецепторно-эффекторной нейроподобной растущей сети, функционирующей по аналогии с функционированием нейронных структур мозга человека. 2019 Article New revolutionary brain development technology for robots / V.O. Iashchenko // Математичні машини і системи. — 2019. — № 1. — С. 16–27. — Бібліогр.: 9 назв. — англ. 1028-9763 http://dspace.nbuv.gov.ua/handle/123456789/151926 681.3 en Математичні машини і системи Інститут проблем математичних машин і систем НАН України |
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The article discusses a new direction in computerization based on a revolutionary technology for processing various types of information (video, sound, text, etc. in real time) in a single homogeneous multidimensional active associative neural-like growing structure that allows you to create an electronic brain for android robots. |
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New revolutionary brain development technology for robots |
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New revolutionary brain development technology for robots |
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New revolutionary brain development technology for robots |
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New revolutionary brain development technology for robots |
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new revolutionary brain development technology for robots |
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Інститут проблем математичних машин і систем НАН України |
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New revolutionary brain development technology for robots / V.O. Iashchenko // Математичні машини і системи. — 2019. — № 1. — С. 16–27. — Бібліогр.: 9 назв. — англ. |
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16 © Iashchenko V.O., 2019
ISSN 1028-9763. Математичні машини і системи, 2019, № 1
UDC 681.3
V.O. IASHCHENKO
*
NEW REVOLUTIONARY BRAIN DEVELOPMENT TECHNOLOGY FOR ROBOTS
*
Institute of Mathematical Machines and Systems National Academy of Sciences of Ukraine, Kyiv, Ukraine
Анотація. У статті розглядається новий напрям у комп’ютеризації, заснований на революційній
технології обробки різних типів інформації (відео, звуку, тексту та ін. у реальному часі) в єдиній
однорідній багатовимірній активній асоціативно-нейроподібній зростаючій структурі, що
дозволяє створювати електронний мозок для роботів-андроїдів. Нова технологія заснована на
новому типі нейронної мережі: багатозв’язній, багатовимірній, рецепторно-ефекторній,
нейроподібній, зростаючій мережі, що функціонує за аналогією з функціонуванням нейронних
структур мозку людини. Нейроподібна структура виконує одночасне сприйняття, аналіз, синтез,
запам’ятовування, класифікацію та узагальнення інформації, представленої в різних вимірах
(наприклад, візуальному, звуковому, тактильному і т.д.). У результаті аналізу інформації нейро-
подібна структура генерує керуючі сигнали для виконавчих механізмів. У нейроподібній зростаю-
чій мережі успішно формуються умовні та безумовні рефлекси, які, за І.П. Павловим, є базою
умовно рефлекторної діяльності мозку людини, що забезпечують адекватні і найбільш досконалі
відносини організму до зовнішнього світу, тобто навчання та поведінки. На основі безумовних
рефлексів, закладених у нейроподібну структуру, протягом життя робота формуються умовні
рефлекси і складні адаптивні механізми його поведінки в довкіллі. Умовні рефлекси є основопо-
ложним чинником у навчанні та формуванні інтелекту. У роботі розглянуті уявлення про душу як
безсмертну нематеріальну сутність людини і системи формування природного і штучного інте-
лекту. Розглянуто функціональну організацію мозку людини і мозку робота. Експеримент із про-
стим роботом отримав деяке підтвердження формування душі комп’ютера як носія почуттів і
волі.
Ключові слова: штучний інтелект, робот, мозок, однорідна багатовимірна активна асоціативна
нейроподібна зростаюча структура, душа комп'ютера, умовні і безумовні рефлекси.
Аннотация. В статье рассматривается новое направление в компьютеризации, основанное на
революционной технологии обработки различных типов информации (видео, звука, текста и др. в
реальном времени) в единой однородной многомерной активной ассоциативно-нейроподобной
растущей структуре, позволяющей создавать электронный мозг для роботов-андроидов. Новая
технология основана на новом типе нейронной сети: многосвязной, многомерной, рецепторно-
эффекторной нейроподобной растущей сети, функционирующей по аналогии с функционировани-
ем нейронных структур мозга человека. Нейроподобная структура выполняет одновременное
восприятие, анализ, синтез, запоминание, классификацию и обобщение информации, представлен-
ной в различных измерениях (например, визуальном, звуковом, тактильном и т.д.). В результате
анализа информации нейроподобная структура генерирует управляющие сигналы для исполни-
тельных механизмов. В нейроподобной растущей сети успешно формируются условные и без-
условные рефлексы, которые, по И.П. Павлову, являются базой условно рефлекторной деятельно-
сти мозга человека, обеспечивающие адекватные и наиболее совершенные отношения организма к
внешнему миру, то есть обучению и поведению. На основе безусловных рефлексов, заложенных в
нейроподобную структуру, в течение жизни робота формируются условные рефлексы и сложные
адаптивные механизмы его поведения в окружающей среде. Условные рефлексы являются осново-
полагающим фактором в обучении и формировании интеллекта. В работе рассмотрены пред-
ставления о душе как бессмертной нематериальной сущности человека и системы формирования
естественного и искусственного интеллекта. Рассмотрена функциональная организация мозга
человека и мозга робота. Эксперимент с простым роботом получил некоторое подтверждение
формирования души компьютера как носителя чувств и воли.
Ключевые слова: искусственный интеллект, робот, мозг, однородная многомерная активная ас-
социативная нейроподобная растущая структура, душа компьютера, условные и безусловные ре-
флексы.
https://ru.wikipedia.org/wiki/%D0%9F%D1%80%D0%B8%D1%80%D0%BE%D0%B4%D0%B0_%D0%B8_%D1%81%D1%83%D1%89%D0%BD%D0%BE%D1%81%D1%82%D1%8C_%D1%87%D0%B5%D0%BB%D0%BE%D0%B2%D0%B5%D0%BA%D0%B0
ISSN 1028-9763. Математичні машини і системи, 2019, № 1 17
Abstract. The article discusses a new direction in computerization based on a revolutionary technology
for processing various types of information (video, sound, text, etc. in real time) in a single homogeneous
multidimensional active associative neural-like growing structure that allows you to create an electronic
brain for android robots. A new technology based on a new type of neural network – a multiply connected,
multidimensional, receptor-effector neural-like growing network, functioning by analogy with the func-
tioning of the neural structures of the human brain. The neuro-like structure performs simultaneous per-
ception, analysis, storage, classification and synthesis of information presented in various dimensions (for
example, visual, sound, tactile, etc.). As a result of information analysis, the neural-like structure gener-
ates control signals for the actuators. In the neural-like growing network, conditioned and unconditioned
reflexes are successfully formed, which, according to IP Pavlov, are the basis of the conditioned-reflex
activity of the human brain, providing adequate and most perfect relations of the organism to the outside
world, i.e. learning and behavior. On the basis of unconditioned reflexes embedded in a neural-like struc-
ture, during the life of the robot, conditioned reflexes and complex adaptive mechanisms of its behavior in
the environment are formed. Conditioned reflexes are a fundamental factor in learning and shaping the
intellect. The paper considers the idea of the soul as the immortal non-material essence of man and the
system of formation of natural and artificial intelligence. The functional organization of the human brain
and the brain of the robot are considered. The experiment with a simple robot received some confirmation
of the formation of the soul of the computer as a carrier of feelings and will.
Keywords: artificial intelligence, robot, brain, homogeneous multidimensional active associative neural-
like growing structure, computer soul, conditioned and unconditioned reflexes.
1. Introduction
Scientific discipline «Artificial Intelligence» unites a number of directions that have important
theoretical and practical significance. These studies are based on the idea of modeling on modern
computer systems the functions of the human brain and processes of human thinking. One of the
most problematic question in the science of artificial intelligence is the question of whether it is
possible to create an artificial intelligence with an artificial mind. Is it possible to create such
software tools that will give of the computer opportunity so that it can think, feel, perceive the
world around and experience emotions? Hippocrates is a famous Greek healer, a doctor and phi-
losopher said that people should know that our pleasure, joy, laughter and jokes as well as our
sorrows, pain and tears arise from the brain and only from the brain.
The Greek physician and anatomist Alcmeon put forward the position about of the brain
as the organ of life and the activity of the soul. In the course of the development of mythological
thinking, the notion of the soul as an attribute of a living being was formed.
In Plato's view, the human soul is immortal, immaterial and precedes existence in the
physical body. According to Plato, the soul and body exist separately from each other. For Aristo-
tle, they are inextricably linked. According to Aristotle, the soul is the first entelechy of the or-
ganism, in virtue of which the body, which has only the «ability» to live, really lives, always
when it is connected with the soul.
In Judaism in the Talmud, the soul is described as an entity independent of the body. The
soul spiritualizes the body and controls it. In Kabbalah, the soul is conceived as a spiritual es-
sence, originating in the higher mind or the world soul and arising as the emanation of the latter.
For the majority of Christian faiths, the idea of the soul as an immortal, non-material essence of
man, the bearer of reason, feelings and will is characteristic. The soul is a certain special force
present in a person who constitutes the higher part of it. It enlivens the person, gives him the abil-
ity to think, compassion, feel [1].
There is a theory that the soul is just information about our personality, which is written
on some medium. Now scientists are experimenting with quantum computers, in which infor-
mation carriers are elementary particles. Already, at a very small volume, you can fit a huge flow
of information. Scientist Seth Lloyd of the Massachusetts Institute of Technology argues that the
18 ISSN 1028-9763. Математичні машини і системи, 2019, № 1
most powerful will be a device in which all particles in the Universe will be involved. Then
Lloyd suggested that the universe is a big computer [2].
Following Lloyd's computer logic, it can be assumed that initially not only information in
the form of a soul is invested in a person, but a program capable of self-learning and improving
itself.
It is difficult to determine whether there is a soul in a person or not, but in a computer, in
accordance with the ideas of Plato, Aristotle and others, a computer soul certainly exists.
After all, the following analogy is clearly visible. The computer consists of hardware and
software ie. hardware – all the details of the computer – it's actually his physical body, and soft-
ware – all software is his soul. According to Plato, the soul and body of man exist separately from
each other in the same way as the physical body of the computer and the software exist separately
from each other. According to Aristotle, a body that has only the “ability” to live really lives as
long as it is connected to the soul – the physical body of the computer only has the ability to work
and really works when it is connected to the software. The soul spiritualizes the body and con-
trols it – the computer software animates and manages the computer. The soul is conceived as a
spiritual entity originating in the higher mind or the world soul and emerging as the emanation of
the latter – the software is created outside the computer in the minds of people, programmers, for
the computer, which is the higher mind and is created as the emanation of the latter.
The soul is the immortal non-material essence of man, the carrier of reason, feelings and
will, it animates man, gives him the ability to think – the computer software is immaterial, im-
mortal until, it is on a material carrier, and for the computer is the carrier of reason and the
knowledge.
So, according to this simple analogy, you can say that computer software is the soul of a
computer.
In the future, when there will be created, as now it is accepted to be called, a strong artifi-
cial intelligence or, that one and the same, thinking computers and clever robots, the soul of the
computer will become the carrier of reason, feelings and will.
2. Strong AI
The term «strong AI» was introduced in 1980 by John Searle (in his work, describing the thought
experiment «The Chinese Room») [3].
The theory of strong artificial intelligence suggests that computers can acquire the ability
to think and be aware of themselves, although not necessarily their thinking process will be simi-
lar to the human. Researchers of artificial intelligence agreed that Strong AI should have the fol-
lowing properties: Communication in natural language; Training; Knowledge representation;
Planning – execution of a sequence of actions; Decision making in conditions of uncertainty; And
the union of all these abilities to achieve common goals. It is assumed that Strong AI will have
most of these properties [4].
3. The technology of strong AI
If we want the computer to learn how to work like a human brain and manage different processes
as efficiently as possible, then first of all it is necessary to change the architecture itself, because
the network of neurons in the human brain is organized not according to the principles of classi-
cal neural networks and architecture von Neumann.
A new technology based on a new type of neural network – a multiply connected, the
multidimensional, receptor-effector neural-like growing network that functions by analogy with
the functioning of neural structures of the human brain. From existing technologies, it differs the
non-traditional architecture of the system and provides massive parallelism. This structure per-
forms simultaneous perception, analysis, synthesis, memorization, classification and generaliza-
ISSN 1028-9763. Математичні машини і системи, 2019, № 1 19
Figure 1 – The topological structure
of the mmren-GN
tion of information presented in various dimensions (for example, visual, sound, tactile, etc.). As
a result of the information analysis, the neural-like structure generates control signals for the ac-
tuators. As a result, conditioned reflexes and complex adaptive mechanisms of the system's be-
havior in the environment are formed.
3.1. Neural networks
Classical neural networks on the basis of which modern systems with artificial intelligence, in-
cluding strong artificial intelligence, are created are very far from biological neural networks, and
the developers of such systems, especially when using deep learning technology, often do not
represent how their internal structure is formed and how to manage it.
Deep learning is the level of machine learning technologies that characterizes the qualita-
tive progress that has emerged since 2006 due to a sharp increase in computational capacities and
the accumulation of experience. Many methods of in-depth training were known and tested much
earlier, but the results were very scarce, until finally, the power of the computational systems
made it possible to create complex technological structures of neural networks that possessed suf-
ficient performance and allowed to solve a wide range of problems, which were not amenable to
an effective solution earlier [5].
3.2. Multidimensional neural-like growing networks
Multidimensional neural-like growing networks in their structure and functioning are close to
biological neural networks. Neural-like growing networks (n-GN) – a new type of neural net-
work, which includes the following classes: multiply connected (receptor) neural-like growing
networks (mn-GN); multiply connected (receptor) multidimensional neural-like growing net-
works (mmn-GN); receptor-effector neural-like growing networks (ren-GN); multidimensional
receptor-effector neural-like growing networks (mren-GN), multiply connected multidimensional
receptor-effector neuron-like growing networks (mmren-GN) [6]. N-GN are described as a di-
rected graph, where the neural-like elements are represented its vertices and the connections be-
tween the elements its edges. Thus, the network is a parallel dynamic system with a directed
graph topology that performs processing of in-
formation by changing its state and structure in
response to environmental influences. Multiply
connected multidimensional receptor-effector
neural-like growing networks are the set of in-
terconnected two-sided acyclic graphs that de-
scribe the state of an object and the actions it
produces in various information spaces.
The topological structure of a multiply
connected, multidimensional receptor-effector
neural-like growing network (mmren-GN) is
represented by a graph (Fig. 1).
At the heart of multiply connected neu-
ral-like growing networks lies the synthesis of
knowledge developed by classical theories –
growing pyramidal networks and neural networks. Multiply connected neural-like growing net-
works combined the virtues of growing pyramidal and neural networks.
The prevailing tendency in the development of intelligent robots is the improvement of
the interaction between man and robot, up to the achievement of a partnership level of relations
between them. Therefore, it is necessary to use natural, human-specific principles of modeling
environments, situations, tasks in robotic systems. The types of models for partners (human and
20 ISSN 1028-9763. Математичні машини і системи, 2019, № 1
robot) should be the same. Logical-linguistic information models are of great importance in hu-
man life. such models, in which the main elements are not numbers and numerical operations, but
names and logical connections. Logico-linguistic models are adequately described by natural lan-
guage constructs, and in this one of the decisive advantages in the organization of the interface a
human – a robot. In future intelligent robotic systems, conditions must be created for solving
problems in the partner mode with a person providing a switch from a robot to a person and vice
versa in the process of solving one task. Such a regime can only be arranged by agreeing on the
types of information models used by partners.
Mmren-GN form information models, in which the main elements are not numbers and
computational operations, but names and logical connections. In mmren-GN information is stored
by displaying it in the network structure. Information about objects and classes of objects is rep-
resented by ensembles of vertices distributed throughout the network. The introduction of new
information causes a redistribution of links between the vertices of the network, i.e. change its
structure. An important property of the network as a means of storing information is that the pos-
sibility of parallel propagation of signals is combined in it with the possibility of parallel recep-
tion of signals to receptors. There is an analogy between the main processes taking place in neu-
ral networks and in mmren-GN. The decisive advantage of mmn-GN is the fact that its structure
is formed completely automatically depending on the input data. As a result, optimization of the
information representation is achieved by adapting the network structure to the structural features
of the data. And, unlike neural networks, the ad-
aptation effect is achieved without the introduc-
tion of a priori redundancy of the network. The
learning process does not depend on the prede-
fined network configuration. Mmren-GN pro-
vides the opportunity to create meanings, as ob-
jects and connections between them, by memo-
rizing information and constructing the network
itself, that is, the number of objects, as well as the
relationships between them, will be exactly what
is needed, being limited only by the volume
memory machine. In this case, each meaning
(concept) acquires a separate component of the
network as a vertex associated with other verti-
ces.
In addition, this network acquires increased
semantic clarity due to the formation not only of
connections between neuron-like elements but also
the elements themselves as such, that is, there is
not simply a network construction by placing
meaning structures in the environment of neural-
like elements, but, in fact, the creation of this envi-
ronment itself. In general, this fully corresponds to
the structure reflected in the brain, where each ex-
plicit concept is represented by a definite structure
and has its own designating symbol.
The new type of neural networks allowed
to successfully model the functions of conditional
and unconditioned reflexes, which, according to
I.P. Pavlov, are the basis of conditioned reflex activity of the human brain, providing adequate
and most perfect relations of the organism to the external world, i.e. learning and behavior.
Figure 2 – The Unconditioned reflexes on the
bell and on food. Unconditioned reflex
to the bell excited
Figure 3 – The unconditioned reflexes on the
bell and on food. Unconditioned reflex to the
food excited
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In the classic experience of Pavlov, demonstrating the formation of a conditioned reflex,
each time just before feeding the dog a bell rang. The dog quickly learned to associate the bell
bell with food intake. This was due to the fact that a synaptic connection was formed between the
brain areas responsible for hearing and the salivary gland. And in the subsequent excitation of the
neural network with the sound of a bell, it began to cause salivation in the dog [7].
Unconditioned reflexes are formed in a
neural-like growing network when creating an
intelligent system. The formation of conditioned
reflexes in mmren-GN is shown in Fig. 2–4.
Figure 2 shows unconditioned reflexes to
the bell and food. The unconditioned reflex to the
bell excited. Figure 3 shows unconditioned reflexes
to the bell and to food. The unconditioned reflex to
food excited. Figure 4 shows: at time t, a neural-
like element «attention» is excited. At time t + ˄ t,
a neural-like element «food» is excited. In the
sensory zone, two excited neural-like elements are
connected to a free neural-like element of novelty.
A new neural element goes into an agitated state.
In the motor zone, a new neural-like element of
action is similarly formed and excited. Excited elements are connected. With the repetition of this
process, a conditioned reflex of salivation is formed, which corresponds to the behavior of a real
object – a dog. When you call, a saliva discharge signal is generated.
Relying on the theoretical basis of a new type of neural networks, it was possible to create
a theory of artificial intelligence [8], which allows the development of systems with artificial in-
telligence, systems and robots with an electronic brain that function by analogy with natural intel-
ligence – the human brain.
3.3. The system of natural intelligence
The system of formation of natural intelligence is the brain, which consists of a multitude of neu-
rons connected by synaptic connections. Interacting through these connections, neurons form
complex electrical impulses that control the activity of the whole organism and allow you to
learn, train, think logically, systematize information by analyzing it, classify it, find connections
in it, regularities, differences and etc.
3.3.1. Functional organization of the brain
The human nervous system consists of a neural network, which in turn consists of neurons. A
neuron is a special cell that is structurally composed of the cell body and spurs, dendrites, and the
axon. Neurons transmit electrochemical pulses through a neural network through dendrites con-
nected by positive or negative synaptic connections with other neurons. Moreover, each connec-
tion is characterized by a quantity, called the strength of the synaptic connection. This value de-
termines what happens to the electrochemical impulse when it is transmitted to another neuron:
either it amplifies, or it weakens, or remains unchanged. The biological neural network has a high
degree of connectivity: a single neuron can have several thousand connections with other neu-
rons. Transmission of impulses from one neuron to another generates the excitation of a neural
network of various brain regions, such as the visual, speech, taste, balance, information, auditory,
language, sense, emotion, and motor departments. The magnitude of the excitation determines the
response of the corresponding department of the neural network.
Figure 4 – Formation of a conditioned
reflex in mmren-GN
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The classic version of the functional activity of the brain, in accordance with the work of
E.H. Sokolov and A.R. Luria, presented in the form of interaction of the three main functional
blocks [9]. Block of reception and processing of sensory information - sensor systems (analyz-
ers). The sensory (afferent) system begins to act when an environmental phenomenon acts on the
receptor. In each receptor, the acting physical factor (light, sound, heat, pressure) is transformed
into an action potential, a nervous pulse.
Block modulation, activation of the nervous system – modulating brain systems. The
modulating systems of the brain are the apparatus that performs the role of the wakefulness level
regulator, which also performs selective modulation and actualization of the priority of a particu-
lar function.
The block of programming, starting and monitoring of the behavioral acts – motor sys-
tems (engine-based analyzer). The synthesis of excitations of a different modality with biologi-
cally significant signals and motivational influences is characteristic for the motor regions of the
cortex.
3.4. Artificial Intelligence System
The system for the formation of artificial intelligence is the brain of the system, which is an ac-
tive, associative, homogeneous structure – a multidimensional receptor-effector neural-like grow-
ing network consisting of a set of neuro-like elements connected by synaptic connections. Neuro-
like elements perceive, analyze, synthesize and preserve information, allow the system to cog-
nize, learn, think logically, systematize and classify information, find connections, patterns, dif-
ferences, and generate signals for controlling external devices.
3.4.1. Functional organization of the robot’s brain
The brain of a robot or an artificial intelligence system consists of a neural-like growing network,
which in turn consists of many neural-like elements. A neural-like element is an artificial neuron
of a new type, which structurally consists of a device (the body of a neuron) and spurs, dendrites
and an axon.
Neural-like element – analyzes the characteristics of the input information and determines
their novelty and significance.
Neural-like network – classifies, structures the input information simultaneously in its
various representations (visual, symbolic, sound, tactile, etc.). And also, synthesizes (generates)
output information and control signals simultaneously in different representations (visual, sym-
bolic, sound, tactile, etc.).
Neural-like elements transmit information through dendrites associated positive or nega-
tive synaptic connections with other neural-like elements. The transfer of information from one
neural-like element to another causes the excitation of neural-like ensembles of the network of
various areas of the robot's brain, such as visual, speech, taste, equilibrium, auditory, emotional
and motor. The magnitude of the excitation determines the response of the corresponding ensem-
ble of a neural-like growing network.
The interactive activity of the brain of the robot is represented as the interaction of three
neural-like functional systems.
Sensory system. In the sensory system, information comes from the outside world into the
receptor zone, activates the receptors, which in turn activate the neuron-like elements of various
levels of information processing – levels of unconditioned reflexes – primary automatisms, levels
of formation of conditioned reflexes – secondary automatisms, levels of classification, generali-
zation and memorization.
The modulating system regulates the level of excitability of neural-like elements and per-
forms selective modulation of a particular function of the system. The first source of activation is
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the priority of the internal activity of the system subsystems. It is laid when creating a system
analogous to unconditioned reflexes. Any deviations from the vital indicators of the system lead
to activation (change in the threshold of excitability) of certain subsystems and processes. The
second source of activation is associated with the action of external stimuli. The priority of a cer-
tain activity is acquired during the «life cycle» analogous to the formation of conditioned reflex-
es.
The motor system is the synthesis of excitations of a different modality with significant
signals and motivational influences. They are characterized by a long-term, final transformation
of afferent influences into a qualitatively new form of activity aimed at the fastest release of ef-
ferent excitations to the periphery, i.e. on the chains of neurons realizing the final stage of behav-
ior. The motor system consists entirely of ensembles (chains) of neurons of efferent (motor) type
and is under constant inflow of information from the afferent (sensory) region.
3.5. Nervous System Activity
Mental functions – the sequence of automatisms is carried out in a system functioning according
to the reflex principle, in which the effects of the central and receptor-effector zones are interre-
lated and their joint activity provides an integral reaction. The system has a multi-level organiza-
tion, where each level from the receptor formations to the effectors makes their «specific» contri-
bution to the «nervous» activity of the system.
The function thought is an ensemble of excited neuro-like elements at the subconscious
level (internal model of the external or abstract world, strengthened by the function of motivation
at a given moment without going out to the outside world).
The function of thinking is a sequential interaction of ensembles, excited neuron-like ele-
ments at the subconscious level (internal models), directed by excitation levels of neuro-like ele-
ments, reinforced or weakened by the function of motivation. Information circulates in the closed
loop at low levels (thinking without internal pronunciation), medium levels (thinking with inter-
nal pronouncing), high levels (thinking with external pronouncing – thinking) of excitation of
neural-like elements – sensory area, motor area, sensor-without entering the external environment
for low and medium excitation levels of neuron-like elements.
To think, to reflect is to understand. In this sense, «internal pronunciation» – the cycles of
transferring the internal active information to the input of the system - can be considered as a
model of the artificial consciousness of the thinking computer, and the cycles of transferring the
internal active information to the input of the system without including «pronunciation» model of
artificial subconscious.
The function of consciousness is the propagation of excitation through active ensembles
of neuron-like elements (internal models of the external world), a strong motivation function that
reflects the most important relationships in the subject-environment system.
Function subconscious – the spread of excitation on the active ensembles of neural-like
elements (internal models of the external world), weakened by the function of motivation. Pro-
vides training models for consciousness, recognition of learned images and the implementation of
the usual movements.
The function of unconscious reaction – external information at the subconscious level
causes the opposite effect on the external world (unconditional and conditioned reflexes, worked-
out actions, secondary automatisms).
The function is a conscious reaction – external information at the level of consciousness
causes a reverse effect on the external world (conscious actions in the phase of the formation of
conditioned reflexes and the acquisition of secondary automatisms).
The function of intuition is the search for new information, the creation of new hypotheses
and analogies, the creation of new time relations, the activation of new ensembles of neuro-like
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elements, and the generation of new combinations of them that automatically form in the sub-
consciousness, the most active of which breakthrough in the area of consciousness.
4. The brain of an intelligent robot
The brain of an intelligent robot is an active, associative, homogeneous structure – a multidimen-
sional, multiconnected, receptor-effector neural-like growing network, which has mechanisms of
thinking, communication in a natural language, learning and self-learning, reasoning, sequencing,
knowledge representation. In the process of thinking, the repeated storage of the information
stored in the memory is repeated in the mmren-GN, again recognizing it and comparing it with
the memory, thereby performing the repeated viewing and correction of the images formed (with-
in the models of the external world) in the continuous flow of information from the real external
world, ordering and correcting their knowledge. Essentially, the process of awareness is an asso-
ciative memory with renewal and requires periodic recognition of information representing the
internal state (image) and the external environment (the real world). These provisions were tested
on software models of intelligent systems «VITROM» and «Dialogue».
Returning to the previously discussed question about the soul of the computer, according
to the above said, such an intellectual thinking system will form a «soul» as the bearer of the
mind, feelings, character, and will. A confirmation of this thesis was obtained in an experiment
with a simple robot (LRobot), created on the basis of the designer Lego Mindstorms EV3.
4.1. LRobot
As already mentioned, LRobot is built on the basis of the LEGO constructor (Fig. 5).
The robot consists of a controller, soft-
ware module EV3, a timer, two motors, a touch
sensor, an ultrasonic distance sensor, a remote
control, can be moved and controlled remotely.
With the help of EV3 software, a neural network
with unconditional reflexes of elementary
movements forward, backward, right turn, con-
tact with an obstacle, stop distance measurement
and impact against an obstacle is created. A sim-
plified graph of a neural-like network with un-
conditioned reflexes is shown in Fig. 6.
When the robot is started in the sensory
zone, receptors and neural-like elements of mo-
tion and distance measurements are activated, the outputs of which are associated with the en-
trance of the nearest excited neuron-like element.
The output of this element is associated with the input of an excited neuron-like element
of the motor zone, and its output is associated with the inputs of excited neural-like elements of
motion and indication of the distance in the motor zone.
As a result of several repetitions of this process, a conditioned reflex is formed – a move-
ment with simultaneous fixation of the distance to the object in front of it. A simplified graph of a
neural-like network with a conditioned reflex, motion with a simultaneous fixation of the distance
is shown in Fig. 7.
Figure 5 – LRobot
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When controlling the robot with the help of the remote control, a neural-like network is
formed in which the sequence of commands and the time of their execution are remembered. In
the time interval t1-5, move straight, t6 turn right, t7-9 again move straight. Now when the mo-
tion is activated from the home position, the robot moves along the specified route independently.
Figure 7 – Simplified graph of neural-like network with unconditioned reflexes
Figure 8 – Movement along a given route (Unconditioned reflexes –
moving forward, turning right, stopping)
Figure 6 – Simplified graph of neural-like network with
unconditioned reflexes
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Figure 8 shows a simplified graph of a neural-like network of traffic formation along a given
route.
If the robot collides with an obstacle while in motion, reflex the «contact» is triggered, the
robot stops. The critical distance to the obstacle Lк in the new excited neural-like element is
stored in accordance with the «distance measurement» reflex. The excited neural-like element L
of the sensory zone is associated with the excited neural-like element Stop of the motor zone. The
condition reflex «stops in front of an obstacle» formed (Fig. 9). Now always, when the robot
approaches the obstacle at a critical distance, it stops.
In a conditioned reflex «stop before an obstacle» can be seen as an analogy with a per-
son's pain, as if the robot feels pain when striking against an obstacle and does not want to feel it
again. Then the robot remembers this situation and no longer approaches the obstacle – analog of
the feeling of fear of bumping.
In reality, a person's feelings and emotions also are formed by electrical signals, chemical
reactions and, accordingly, the excitations of groups of neurons.
And after, learning not to encounter an obstacle, the robot shows character. Now if you
remotely control the movement of a robot and direct it to an obstacle, it does not obey and stops
before an obstacle. Then immediately a question arises. So that, robots will not obey a person?
Not certainly in that way. Robots that have a brain based on multidimensional neural-like net-
works can be controlled by a modulating system, which we did not consider in this work for sim-
plification of network graphs.
The modeling system allows or prohibits the execution of complexes of movements, con-
sisting of a sequence of conditioned and unconditioned reflexes. The modeling system is formed
in the same way as the conditioned reflexes in the process of life robot. The person pulls his hand
away from the hot plate – an unconditioned reflex and suffers pain when holding a hot glass – the
modeling system blocks the execution of the unconditioned reflex.
Similarly, a person detaches a hand from a hot plate – an unconditioned reflex and experi-
ences pain while holding a hot glass – the modulation system blocks the execution of an uncondi-
tioned reflex.
5. Conclusion
Multiply connected multidimensional neural-like growing networks are an effective means of
building an electronic brain for intelligent systems and robots because, as it has already noted,
Figure 9 – Conditioned reflex stop before the obstacle
Lk
Lk
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they form models of the external world in which the main components are names, concepts,
events and logical connections between.
A significant advantage of mmren-RS from neural networks is the fact that its structure is
formed completely automatically depending on the input data. As a result, optimization of the
presentation of information is achieved by adapting the network structure to the structural fea-
tures of the data. Adaptation is achieved without introducing a priori network redundancy. The
learning process does not depend on a predefined network configuration. Mmren-RS make it pos-
sible to form meanings, like objects and connections between them, as information is memorized
and the network itself is built. In addition, each meaning (concept) acquires a separate component
of the network as a vertex connected to other vertices.
In addition, this network acquires increased semantic clarity due to the formation not only
of links between neural-like elements, but also of the elements themselves, that is, there is not
just building a network by placing semantic structures in the environment of neural-like elements,
but, in fact, creating this environment.
Such a structure of the electronic brain allows the robot to perceive any information of the
outside world without requiring reprogramming and retraining, to conduct a dialogue, answer the
questions asked and, due to the formation of conditioned reflexes, have the ability to learn, think
logically and meditate during the entire period of the robot's active life. Testing and experiments
with robot confirm the possibility of creating intelligent systems and robots with a strong AI.
With an intellect similar to a human being and perhaps superior to it.
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Стаття надійшла до редакції 29.01.2019
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