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Ассоциативная память — среда для формирования пространства знаний. От биологии к приложениям

Дюссельдорф: Palmarium Academic Publishing, 2017.

 

The monograph deals with the analysis of the structure and functionality of the
human brain, the results of which are presented on the example of processing quasilinguistic information of various types using operating software models. It also
describes the implementation of an element of corticomorphic associative memory
developed on the basis of these results, in the form of a microelectronic model ±
associative memory VLSI with fuzzy search.
Three basic structures of the brain: the cortex of the large hemispheres, the
hippocampus and the thalamus are responsible for processing information in the brain
obtained from the sensory organs of a human. Columns of the cortex form
dictionaries of images for events of varying complexity and various modalities. The
cortex together with the hippocampus forms representations of whole situations in the
lamellae of the hippocampus on the basis of event images formed and stored in the
cortical columns. The hippocampus, along with the cortex, reorders the presentations
in the cortical columns involved in representing the situations in the hippocampus
lamellae, while simultaneously ranking the weight characteristics of the event images
in the cortex. In other words, in the cortex and the hippocampus, a structural
representation of the external and internal world of a person is formed, which has the
property of associative access to information. The thalamus, as a control structure, by
controlling all information flows layer by layer at the inlet, outlet and inside the
cortex, allows control of the processes in the cortex strengthening some of them and
weakening others.
104
The cerebral cortex column, as an association of pyramidal neurons of the third
layer, performs structural processing of specific information modeling a fragment of
a multidimensional signal space. The model is implemented by an artificial neural
network based on neuron-like elements with time summation of signals. Input
sensory information is a matrix of information flows, where each information flow
can be considered as a quasi-text consisting of repeating elements of various
complexity and with various frequency of occurrence. The brain is a parallel
computer that implements layerwise parallel processing of information coming from
the sensory organs through the subcortical nuclei, in the cortex, and again through the
subcortical nuclei to the effectors.
The column of the cortex modeling a fragment of a multidimensional space
performs the transformation of the incoming information flow into a trajectory in a
multidimensional space. In a multidimensional space, in the process of structural
processing of input quasi-texts of various modalities, dictionaries of images are
formed for events of the internal and external world of various complexity, which
make up a multi-level hierarchical structure. Information relations between the levels
allow filtering out the words of the current level from the input quasi-text, sending
further the new information characterizing the relations of these words in the input
information, which at the next level can also be used to form a dictionary of the next
level. As a result of this processing, dictionaries of event images of various levels are
formed, from the simplest to the most complex elements in this modality. If we
consider the text of a natural language as an example, then these dictionaries form
images of events of morphological, lexical, syntactic, and, finally, semantic levels.
The semantic level in this case means the level of representation of the semantics of a
separate sentence, where the lexical component of the semantics of the sentence is
represented by the pairwise compatibility of concepts (words or word combinations).
The pairwise compatibility of concepts allows formation of a semantic network
of the whole text. In the brain the so-called cognitive semantic networks are formed,
networks that are built on a physical substrate - a natural neural network, the brain.
105
These networks are built on the principle of accounting for the joint occurrence of
individual images of events in situations, that is, the semantic relations between the
concepts in these networks ± of only one type, associative ones. Therefore, cognitive
semantic networks formed are homogeneous (that is, associative). The primary
semantic network is characterized by frequency weight characteristics.
The information constantly added during the learning process requires its
correlation with the previously processed information already stored, for which
constant corrective weighting of the parameters is necessary as the vertices of the
network and their connections. The role of the novelty filter is played by the
hippocampus. In its lamellae, information is formed and stored on relations of the
images of events stored in the cortical columns, as they appear to be connected in
various situations. The re-ranking of network concepts is performed by an iterative
procedure similar to the Hopfield network algorithm. The structure of representations
of situations in the hippocampus lamellae resembles the structure of the semantic
graph of a separate sentence of the text, the extended predicate structure of the
sentence.
In the cerebral cortex, hierarchies of modal dictionaries are formed; at higher
levels of representation a hierarchy of multimodal dictionaries is formed. The
collection of all the words from all dictionaries of all levels of all modalities, as well
as all the words from all dictionaries of all levels of the multimodal representation,
represent a static model of the human world in which information about the world is
stored in terms of images of events of various complexity, and which interact with
each other according to the principle of associative reference to information: similar
information is stored in close dictionaries. That is why the whole model of the world
is a semantic network in which three subnets are prominently distinguished:
linguistic; multimodal multilevel schematic, and multimodal multi-level image
bearing (individual) ones. The formation of dictionaries of the multimodal multi-level
representation is performed bottom-up with gradual filling of levels of more and
more complex representations. The world model is clearly structured into three main
106
components: two in the dominant hemisphere, and one in the subdominant one. In the
dominant hemisphere, the linguistic part of the world model is formed through the
language under the influence of society, as well as a multi-level multimodal
schematic part of the world model. While in the subdominant hemisphere a
multimodal two-level individual part of the world model is formed, which is less
dependent on the society and mostly takes into account the individual history of the
development of a particular person. Due to the level-by-level links of the linguistic
and multimodal parts of the world model, and also due to the synchronism of their
formation, the language model of the world is isomorphic to the multimodal multilevel schematic part of the world model in the dominant hemisphere.
If we consider a particular quasi-text projecting it onto the semantic network of
the world model, then we can see that it is projected into a chain of words of
dictionaries, which corresponds to a specific description of a certain subject domain.
This chain can be transformed into a chain of extended predicate structures of the
sentence corresponding to the fragment of a quasi-text representation of a particular
modality described by this sentence (for example, a video sequence). A lot of such
chains can be structured, on the one hand, into two classes: descriptions and
algorithms, and on the other hand (already within the framework of a particular
subject domain) into classes of structurally close chains that can be replaced by one
representative of the class. Such (on the basis of many classes of chains) a
representation is naturally called pragmatic.
On the example of the formation of the sensory information flow of the visual
modality in the precise channel of the human visual analyzer, it can be shown that all
sensory flows are quasi-texts that resemble natural language texts in structure.
Therefore quasi-texts, like natural language texts, can be processed as natural
language texts using the structural approach. Since the linguistic methods of text
processing based on formal signs of the text form allow processing information up to
the syntactic-semantic level of a single sentence, the formation of a semantic
representation of the text is possible only through statistical methods [Kharlamov,
107
2006]. Algorithms of the structural statistical analysis of the semantics of the whole
text are presented on the example of the work of the program neural network
technology TextAnalyst. The TextAnalyst technology is implemented on the basis of
the formation of dictionaries of event images in the columns of the cortex, which are
a homogeneous semantic network, as well as the re-ranking of the weight
characteristics of the vertices ± concepts of this network, as it is done in the joint
work of cortical columns and the hippocampus lamellae. The main functions
implemented within the TextAnalyst technology are the formation of a semantic
network, re-ranking of weight characteristics of concepts, formation of a summary,
comparison of texts in meaning (by structure) and classification of texts. The
TextAnalyst technology is the base for creating e-books that allow non-linear text
reading.
Since the corticomorphic associative memory is an effective tool for analyzing
any quasi-text information, its microintegral implementation can be useful for
creating information processing hardware. Modern computer technology is at the
limit of applicability of well-known Moore¶s law, since the design standards of
microelectronic technology have come close to the size of the atom, when absolutely
different laws of physics begin to work. Objectively, the time has come for the
transition from the traditional sequential von Neumann paradigm of building
computing devices to other approaches. One of such new ways of development of
computer technology can be the use of the corticomorphic associative memory for the
construction of massively parallel computing structures that use the ³CorrelationExtremation-Uncertainty´ computational basis, rather than WKH ³AND-OR-NO7´
traditional basis.
 






































































































































Ассоциативная память — среда для формирования пространства знаний. От биологии к приложениям