International Journal of Machine Consciousness World Scientific Publishing Company

Episodic Memory – Key to ThinkingWIESLAW L. GALUS

Eutherm Ltd, Poznanska 129/133,Ozarow Mazowiecki, 05-850 Poland

w.galus@eutherm.eu

Received Day Month YearRevised Day Month Year

Intelligent machines should be equipped with motivation to broadentheir knowledge via their own experiences and to infer on basis ofprevious experiences. Thus, they should possess self-consciousnessthat would allow them to express higher mental functions such asreasoning, curiosity, understanding, stubbornness, bravery, etc.This paper presents a flowchart schematic for an artificial brainthat would aspire to realize the higher mental functions that arepresent in the human brain. Also presented is the significance ofepisodic and association memory plus their interrelations with theinclusion of short-term memory, working memory, associative memoryand long-term memory. A structure for the episodic memory ispresented alongside its place in the architecture of emergentconnectionist network. The paper also shows the inaccessibility anduniqueness of the internal language of neural correlates of mentalevents and difficulties in creating hybrid architectures.

Keywords: self-consciousness; cognitive architectures; curiosity;machine reasoning; understanding; ; episodic memory, schematicof artificial brain, mind

1. Introduction

An artificial cognitive neural network should realizemental functions similar to those of a natural mind. Inspecificity, the cognitive networks should be characterizedby a rich repertoire of behaviours and thought processestypical for animals or humans. Cognitive networks thataspire to achieve self-consciousness are characterized bymotivation - understood as the necessity to understand thereality - as well as by curiosity, imitated by propermechanisms of environment and memory exploration, asdescribed in Machine Mind [Galus 2011][2]. This should,however, not be taken as a statement that any artificialnetwork can possess a psyche that is comparable, or beyond,that of a human being.

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We need to start the analysis by focusing on the ways inwhich the mind registers stimuli from the senses. In thequoted work was presented a recognition and categorizationmethod for visual stimuli. The proposed schematic wasdescribed and acknowledged by many researchers and appearssufficient for analysis of the issues of motivation and themechanism that activate the memory in order for the mind tocarry out cognitive functions. However, when faced with theactivity of a individual specimen we come across theproblem of recognizing and registering an actively changingenvironment. Applying that to visual observation ofenvironment, we see static images that change in a dynamicfashion, episodes composed of series of images thatresemble film clips. The higher fields of the network,responsible for creation of qualia, are therefore notexcited by a stable configuration of neurons but rather byrapid changes of these configurations occurring within theinput nodes, where the dynamic of the changes themselvesdepends on the rate of the input sensations.

We know, that both, human beings and animals, possessability to remember the observed episodes in a storage areacalled the episodic memory. This is an important locationon most flowcharts presenting the mind’s operations.However all models for episodic memory, employed untiltoday, have been far removed from the actual operatingprinciples of the episodic memory in the animal brain.Although the importance of episodic memory function in thecognitive processes is well recognized, the actualmechanism of storing the episodes has not been researchedsufficiently. This paper presents a hypothesis of how suchprocesses may be realized; how to complement thearchitecture of artificial neural networks, allowing themto register and recall the required episodes; and whatwould be the consequences of such model for the psyche ofneural networks organized accordingly.

2. Requirements for the cognitive network

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It seems obvious that in order to achieve higher mentalfunctions similar to those occurring in the human brain,the network should be connectionist with an emergentarchitecture [Duch, 2010][1]. It should meet therequirements set forth in the Machine Mind [Galus, 2011][2].A special characteristic of such a network is itsmultilevel, hierarchical structure, high capacity memoryand feedback loops that allow excitation of the descendinghierarchy of the neural cell fields. Apart the fields andlayers that constitute the hierarchic structure, specialistareas should also be designed - responsible for thefunctions of: speech, emotions, reasoning, logic, geometricand mathematical transformations, timekeeping and planningas well as motor fields, controlling the body, speechapparatus and sensors. A neural network modified bysynaptic reinforcement seems quite inadequate when facedwith the need to record a quick, massed, great inflow ofdata that needs to be stored in the episodic memory. Theneuron cells should be accompanied by memory cells capableof permanent change of state under the influence of asingular electro-chemical stimulus provided by the network.Such role could perhaps be realized by astrocytes that arebelieved to serve an important function in the informationprocessing taking place in the brains of humans andanimals. In any case, the nodes of an artificial networkshould consist of cells capable of introducing permanentchanges in the transmission parameters of the network.Sampled temporal segments of each episode, much like movieframes, must be registered in subsequent memory cells. Ifwe remember that each percept and scene is accompanied by asignificant tree of cell group excitation that ascends tothe higher layers of the processing hierarchy, we come toconclusion that registering subsequent configurations ofexcitations of these cells will result in the memory beingflooded by a giant torrent of unnecessary information. Theway in which images are constructed in a hierarchic,connectionist network results in a remarkable ability tocompress the information recorded. As the processingpathway passes through fields and groups of cells that arespecialized in recognizing singular aspects of the analyzed

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image, in the subsequent configurations of excitationsresponding for the sequence of time segments, the onlyelements to be registered will only be these cells thatrecognize the changing aspects. The remaining configurationsets, associated with the observed scene will remainunchanged, and when the episode would be recalled, theywould create a complete and dynamically changing rerun ofthe episode in cooperation with cells responsible forregistering changes.

All information reach the Central Executive (CE) - thehighest field in the semi-hierarchical structure ofinformation processing. In many of the available works theCE is tasked with the role of situation analysis i.e.thinking, associating abstract notions, planning,motivation and goal setting, concentration and control overthe effects of undertaken actions [3,5,8]. Such wide rangeof tasks that CE is forced to fulfil results that nospecific structure was singled out that would effectivelyrealise all these functions. In the works referenced aboveas well as in other attempts to propose working cognitivearchitectures, the consciousness functions have been movedto this central field without providing information on howexactly are they to be realized or stating that itimpossible to achieve such functions at the current stateof the art of technology.

A paper by Starzyk from the University of Ohio [5],analyzes the mechanisms for concentration of attention anddescribes systems for effectors and location control. Anattention switching mechanism located in the CE, based oncompetition of excitation configurations by stimulireaching the association memory, being a part of the CE,would select the winning mental representations on thewinner-takes-all (WTA) basis, creating in effect, a streamof consciousness. The episodic memory serving as a basisfor the learning processes, would be filled with such pre-selected data provided by the semantic memory. The“Planning and thinking” block would be responsible forplanning and thinking, triggering conscious attention

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focusing, recalling of episodes, controlling effectors andwould cooperate with the block responsible for goaldefinition and motivation. While we can imagine a stream ofconscious registration of stimuli that reach the level ofcomparison and selection, still unknown is the actual aimand way of recalling required episodes by the consciousnessthat has to be already emerged before it may serve itsselective and regulatory functions.

3. Schematic of a brain-type neural network

Armed with the hypothesis regarding the operatingprinciples of the episodic memory and taking intoconsideration the need to understand the mechanisms ofcuriosity as described in Machine Mind [Galus, 2011] we canattempt to propose a certain network architecture thatwould realize functions of the human brain, demonstratingself-consciousness and other complex mental states. Aschematic for such a network is presented on Illustration1.

The most important part of the presented schematic is thenetwork of connections between the separate areas of the“brain.” An especially important and, sadly, neglectedelement is the network of backward, descending connectionswhich are responsible for the curiosity mechanism. Itshould be remembered, however, that the connectionspresented, show only a chosen set of excitationtransmission and that they represent clusters of neuralconnectors that sometimes consist of millions of neuralstrands that connect individual processing fields, whichallow groups of cells – network nodes - to transmit theexcitation states.

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Fig. 1. The ascending signals are shown as solid lines, descending signalsas dashed lines (the schematic shows only the primary excitation pathwaysbetween the operative blocks). The Center lines show repetitions of blocksin vertical direction (to determine the level of processing), and inhorizontal direction (for denotation of sensory modalities). The separatesensory blocks that constitute levels of processing A1, … A4, … Ak

correspond to fields V1, … V4, … Vk in the human brain. The brain interactswith its environment via sensors and effectors. The Body, InternalParameters block is a collection of sensors that analyze the physicalstate of the network or agent body in the case of embodied intelligence.The internal structure of blocks is presented on Illustration 2.

Individual areas constitute a semi-hierarchic structureonly in functional terms. They could be incorporated into auniform network structure with similar characteristics. Inspecificity, the CE area does not have to constitute auniform whole. It could be divided between other areas ofhigher mental functions - uniting their actions. The actuallocalization may be dependent on the requirement tominimize the length of connections and their optimalcooperation.

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The division of areas into layers is in itself symbolic.The sensory and perceptive fields have a classic neuralnetwork form as they serve cognitive functions. Theirmultilayer structure is required in order to allow cellgroups to realize functions that in the human brain arerealized by a singular neuron or a group of a few neurons.The long-term memory cells are not differentiated as aseparate layer in artificial neural networks but ratheraccompany the association and short-term episodic memorycells. Perhaps they should serve a function similar to thefunction of astrocytes in the animal brains. Theassociation memory cells realize a comparative function,i.e. they calculate the correlations of excitationconfigurations that arrive to these cells from the lowerlevels.

A fragment of a cognitive neural network corresponding to aspecialized block is presented on Illustration 2.

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Fif. 2. Creation of episodic memory in typical memory; time sequencesare changed into spatial arrangement.Excitation at moments t1, t2 ....tn are registered in cells c1, c2,... cn

located in the vicinity. They are registered in a sequential order, when acell of a lower number has changed its state by registering the previousexcitation. Strong or recurring stimuli are recorded by the long-termmemory cells lm1,... lmn that correspond to association memory cells.Excitations are filtered through the association memory and transmittedout of the block to connected structures in a way presented onIllustration1.

Separate layers on Illustration 2, designated with numbers1,2,3,4 represent specialized layers that connect “neuron”cells – nodes that serve specific functions. Looking at thefunctions served by the cell groups in their respectivelayers, we can differentiate them into:

1 - Input Layers (IL)2 - Short-term Memory (SM) and Short-term EpisodicMemory (SEM)

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3 - Association Memory (AM)4 - Long-term Memory (LM) and Long-term Episodic Memory(LEM)

Short-term episodic memory cells, presented on Illustration2 as “n”, Long-term Episodic Memory, presented as ln , andAssociation Memory cells Cn are separate neuron cellslocalized in artificial cerebral cortex in accordance withthe localness hypothesis. They are connected in such a waythat an excitation can be forwarded to subsequent,neighbouring cells. The excitation propagates automaticallyto further long-term and short-term episodic memory cellswhich results in “displaying” of the recorded sequence.However, for this to occur, the episodes need to beproperly registered beforehand in these memories. Thistakes place thanks to specific operating principles of theassociation memory cells. Therefore, a hypothesis isproposed that in order to achieve the required effect, theoperation of the cell groups must be as follows:i. Stimuli received by the receptors form objectpatterns that are recognized in the input layer (layer no1) that creates a cognitive neural network.ii. At the highest level of the network, that we shallcall operational short-term memory, the recognizedpatterns responding to subsequent time segments for agiven tn moment are recorded. The set of neural cellsbeing a neuron correlate of these changing patterns willundergo changes so it will be different in any timemoment tn. As in a short time period most elements of a scene do notchange significantly, the majority of neural cells willremain in their previous state. The changes will affectonly the part of cells responsible for recording of thechanging elements of the scene. Illustration 2 showsthese cell groups as squares denoted tn in the SEM layer(layer 2), their designation matches their correspondingtime moments.iii.All stimuli configurations from the SEM layer shouldbe transferred to the next cell layer characterized byits further specialization. Their operating principle is

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that if subsequent signals do not change for a longertime, their corresponding excitation pattern is recordedin the long-term memory in the LM layer. Regardless fromthis process the signal is forwarded from these cells toother areas (blocks) that are placed higher in theprocessing hierarchy. However, if there is no correlationbetween the excitation patterns (which means there arechanges in the excitation configurations), the newconfiguration is recorded in new memory cells designatedas Cn. Due to their ability to recognize the level ofpattern correlation and the ability to forward them onthe basis of existence or lack of existence of suchcorrelation, these cells are called Association Memory(AM, layer 3). If the state of neural excitation in AM ismaintained for a longer period, no matter the reason, itmust be recorded in LM. If a group of neighbouring cellslm records a sequence of subsequent configurationscorresponding to a changing scene in a series of timemoments, such memory will be called the Long-termEpisodic Memory (LEM, layer 4).

A group of cells created on the basis of the aboveoperating principles may realize a pattern-recognition andrecording function. Based on the method of correlationrecognition, the patterns may be recorded in long-termmemory or be transferred to higher fields in the processinghierarchy. It should be noted that in the proposed modelthe episodic memory cells tn do not stand out from othershort-term memory cells and in a similar manner theepisodic memory cells do not stand out from other long-termmemory cells ln. In this model the episodic memory iscreated using long-term memory cells typical for a givenstructure. Moreover, it should be noted that such a networkchanges the temporal sequences into spatial sequences,recording data from a series of subsequent moments inneighbouring memory cells. The recalling of a sequencetransforms the spatial distribution into a sequence ofscenes that are played out. Similar method may be utilizedfor recording mental processes dealing with logical,abstract or mathematical thinking that would constitute

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specific types of mental episodes. Such recordedinformation could be accessed by the system, at will, muchlike episodes from its? individual history.

Processing areas consist of similar blocks – arrangementsof cells that in their entirety resemble a cerebral cortex.Just as the CE block, localized among other specializedblocks of an artificial mind, described above, theremaining blocks can, hypothetically, constitute a singlelayer of cerebral cortex divided into specialized areas.The semi-hierarchical structure presented on Illustration 1consists of only a system of connections plus the order oftransmissions between different blocks. Apart the actualorder of transmitting the ascending signals, these areasare differentiated by their joining to sensory organs thatdeal with both the environment and the state of thebody/network itself. This way, the fields that are joinedto sensors create sensory fields with their topographic andretinotopic maps. Depending on the type of sensory inputdefined by the sensors connected there is a range ofmodalities of sensory inputs (Sensory Input block). Part ofthe artificial cerebral cortex that is connected to theeffectors should, therefore, serve as a basic motor cortex(Motors, Effectors, Actuators block). A specialized partresponsible for effectors would be in charge of the speechapparatus and the orientation and tuning of sensoryreceptors. If the artificial brain is provided with a fullbody, it should be conscious of the state of its body. Thiswould provide possibility to use well establishedmotivation systems such like the drive to minimize “pain”.If embodied intelligence would be able to take intoconsideration many parameters defining state of its “body”it should be possible to create a hierarchy of pain,establishing an excitation scale related to the severity ofdanger signalized by the pain. However, the dominatingmotivator would not be the physical pain. Pain signalsshould be transferred to the Central Executive, where theywould be compared with other signals and, after being“understood”, a decision would be made on the strategy ofavoiding that pain.

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Feedbacks covering the fields of emotion (Mental Pain &Emotions), body (Body & Pain) and motor (Motors, Effectors& Actuators) functions would be responsible only for quickreactions to sudden dangers, as a form of a survivalinstinct, without actually engaging the consciousness. Thecognitive functions would be realized mostly at individuallayers of sensory data processing areas.

The long-term memory of the system is dispersed andlocalized both in the CE block, as far as past episodes areconcerned, in the Speech & Language block, as far assemantics and syntax is concerned, plus in otherspecialized blocks in regards to logic, reasoning,geometric and mathematical transformations. The episodicmemory of the lower processing fields corresponding tobrain fields V1... V4 and higher ones, consists ofcomponents of episodic sensations that are utilized whenepisodes from higher fields are recalled. The long-termmemory ensures the permanency of recognized data transferpathways in layer 2 and permanency of selected datatransfer when in accordance with the expected pattern bythe association memory in layer 3 to higher layers ofprocessing. The pattern becomes permanent when:

1. the stimuli and excitation are very strong. Theexcitation may result from sensory signals from thesensor fields as well as from internal sources –signals from other processing fields.

2. the stimuli are frequent. The repetitions may resultfrom frequent sensory signals or from return signalsfrom higher processing layers – from cells that lay onthe pathway for excitation pattern transfer to CE.

3. There is a synchronisation of stimuli with signalstransmitted intentionally or non-intentionally fromthe higher layers, which conditions recording in long-term memory which in turn provides additionalreinforcement for a given stimuli while limiting otherstimuli that fight for the system’s attention i.e.being forwarded to higher processing layers.

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There should be a possibility for the ascending pathrecording process should be complemented by formation of aparallel descending pathway. Long-term memory cells can befitted with such a function, although there is no researchindicating that such a correlation is present in case ofanimal brains. Discovering relation between the realizationof association functions and the long-term memory is ofvital importance for neurological research. Much promise isassociated with the role of astrocytes in these processes.

Consciousness is emergent within the Central Executiveblock. The Self & Understanding block is responsible onlyfor registering the state of understanding and self-consciousness that are, in fact, located in CE block. In asimilar manner, all the higher mental functions may berecognized in the association memory of the CE block andrecorded as “experienced” in the cerebral cortex blocknamed “Self.” The abovementioned intentional signals thatcoordinate long-term memory activation are governed by aconscious mechanism of “attention.” When analyzed from theviewpoint of selection of different configurations ofmental states, this means that the correlated CE cells sendstrong return, descending, signals to lower fields,generating a strong excitation that is chosen by theselection and transfer by WTA mechanisms back to the higherlevels. The correlation between proper CE cells and groupsresponsible for configuration recording in the lower fieldsare created in the process of learning and experiencecollecting which in turn results in connections (pathways)recorded in the long-term memory. The same signals whensend spontaneously due to excitation that is not associatedwith conscious states, would mean a mechanism forunconscious “attention switching.”

4. Episodic memory – operation

The episodic memory allows for recording sequences ofevents (scenes) or sequences of states. For that purpose,the brain has to divide a given event into shorter timesegments and record them in subsequent cells corresponding

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to the temporal sequence based on the timeframes of thesesegments. Some of research seems to indicate that thesubsequent cells are in fact neighbouring cells, whichwould be in accordance with the localness hypothesis,presented in the Machine Mind paper [Galus 2011]. In mostcases, scenes are differentiated only by sparse changes intheir constituting images. The scenes are recorded inneighbouring, subsequent cells. Therefore, these cells haveto be joined by a serial connection so that excitation ofone of them results in activating of a whole series ofsubsequent cells. This results in capability to accesswhole episodes at will, and replaying them in theirentirety. There is also possibility of initiating therecall from a chosen scene in the recorded episode – ascene that is most crucial for the aspect that is beinganalyzed at the conscious level. After such excitation, thesubsequent, serial cells would be activated in aconfiguration corresponding to recorded episodes, forcingintentional or coincidental “remembering” of pastsensations. The exciting signals from other blocks wouldnot have to reach the working memory but could exciterecall of configuration episodes directly from the long-term memory.

With such assumptions question arises concerning thefaithfulness of recording and recalling of a storedepisode. This concern both the temporal resolution,connected to sampling frequency, as well as spatialresolution, or to be more general - sensory resolution. Toregister visual sensation the sampling time for individualepisodes must be short enough to create a feeling ofcontinuity of the image. On the other hand its speed cannotbe shorter than the time needed to process the image in itsdynamically changing aspects. Most research data on thisissue is based on research of the human brain. It seemsthat in case of humans the sampling time is equal toregistration from few to a few dozen frames per second.Shorter sampling time is required for the sense of hearingand is connected with the ability to differentiatefrequently recurring sounds. These times equal the

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frequency of a few hundred Hertz. Other senses do notrequire such great sampling frequencies.

The spatial resolution of the recalled images is remarkablyvariable. We know that when reminiscing of a landscape weat first see a general shapes, colours and dominatingobjects. We also direct our attention to dynamicallychanging elements. This means that CE is reached by signalsfrom the association memory. Taking a well known landscapeas an example, we can assume that CE will consciouslyrecognize the beach, water and sun over the waves. Plus aflying seagull. These sensations will be generated in asequence, as a result of two variables that force changingpatterns of stimuli to be transferred to CE. The firstvariable is discharge the sequence of short-term episodicmemory cells. The waves of changing excitations will berecognized as the movement of the seagull over the horizon.The second variable is the switching mechanism ofattention, that focuses on the main elements of thelandscape, allowing us to see, in turn, sand, water,horizon and sun. This mechanism is described in a paper [3]is in accordance to the postulated competition ofexcitations [2]. The order in which the elements areperceived could be regulated by the mechanism of mentalsaccades that was also analyzed in the previously mentionedwork [3] and that resembles the saccadic eye movements.

If we focus our attention on the details (which means thatthe competition mechanisms select new objects of attention,focusing on them), we will be able to perceive smallerdetails, like: the breaking of waves, footprints in thesand or the movement of the seagull’s wings. What mechanismallows for such a change in resolution of the recalledepisode? This is possible due to excitation of the deepermemory structures in fields responsible for recognizingscene details. Therefore, we can expect, in relation to thenatural brain, a system of return, descending connections,from the higher processing levels to the lower, closer toperception fields. A similar system should be included indesigns of artificial systems. This type of backward,

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descending connections was described earlier [2] and isconnected to creation of patterns for comparison within the“curiosity” mechanism. This name was chosen because thismechanism is responsible for penetrating and exploring ofthe memory, i.e. realizing cognitive functions that allowintrospection, reflection and, through association,understanding of new phenomena arriving from the sensoryfields. This excitation of episodic memory may beconscious, as the excitation signals that trigger recallingof episodes originate in the highest layer of CE inassociation with other notions, possibly originating inother areas. This could be an area responsible forprocessing other modal functions, e.g. the hearing andspeech recognition block, which is responsible for the factthat when we hear the words “beautiful summer vacation”certain semantic memory structures are excited and theexcitation signal is forwarded through internal verticaland later descending connections to the episodic memory.Similar mechanism can invoke even more detailed elements inimages from the episodes recorded in our memory, which wecan call sub-episodes. However, we cannot be certainwhether given sub-episodes are in fact elements of theepisode that evoked them. This is because there is apossibility of exciting sub-episodes from a completelydifferent ”fable” as long as they seem to match ourmemories and as long as our CE accepts and “recognizes” thefinal, artificial episode. If our memory has a sub-episodeconsisting of the sound of seagulls’ cries, it can be usedas a sub-episode in different episodic recollections.

Sub-episodes can be excited by descending connections,visible on Illustration 1 and denoted by letters Ak , Bk , Ck

where ABC stands for sensory modality and the k index is thelevel of pattern analysis. This pattern can be correlatedwith the level of detail of a given scene. This is when aninteresting question arises: how to choose the k level? Twohypothetical possibilities need to be addressed. The firstassumes that we excite all the hierarchy levelssimultaneously. In this case the consciousness is providedwith a level of detail chosen by the pattern selection of

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the competition mechanism (equal to attention switchingmechanism presented in [Starzyk, 2011]). The secondpossibility is choosing the k level by binding the kdescendent pathway to its corresponding ascending pathway,as it was described above. Such connection must be recordedin the long-term memory and can be created via learningprocesses, i.e. recording of events. It is possible thatboth possibilities are at work in the animal brain. Thecurrent understanding of neural processes and the role ofastrocytes in these processes does not allow us toinfallibly determine the ways in which association andlong-term memory cooperate and what is their exact relationto the episodic memory.

We can see that the described operation of recallingepisodes with varying level of detail creates differentneeds for working memory in which it happens. The brain canbe said to protect itself from overload. Episodes that weseldom recall are eventually forgotten (except theirelements that are frequently used as sub-episodes) andusually, after some time, we remember only static scenesthat are animated only by imagination or verbal narration.If these memories are not used, they also eventuallydisappear.

The mechanism for forgetting episodes is as important asthe mechanism for their recalling. The short-term episodicmemory of the sensor areas is bombarded by gargantuanamounts of input data. The competition and inhibitionmechanisms select only a fraction of these inputs to beprocessed and recorded in long-term episodic memory. Theworking memory must be continually emptied to allow for newsensations to be taken into consideration. After theinformation is recorded in long-term memory, theassociation memory can be made available for futureoperations. As one of the determinants of recordinginformation in the long-term memory is repetition of thestimuli in given time periods, the association memory hasto record these information for at least the duration ofthe said periods. The timeframe for recording data in the

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long-term memory is dependent on the life strategy of theembodied intelligence. If the memory resources are limited,the forgetting mechanism, activated in relation to unuseddata may be beneficial. The strategy for forgetting, thatwas an evolutionary achievement for animals, we have todetermine on the basis of economical analysis.

5. Communication with the cognitive network

Recognizing these remarkable characteristics is howeverimpossible without first establishing a language forcommunication. Similar problems are present in approachestrying to discover and assess the level of consciousness ofliving organisms. We are witnessing an endless discussionon self-consciousness of apes, dolphins, elephants, dogs,cats and other species, although they possess similar tohumans: social behaviors, body language, somatic reactionsand even facial expressions. This should, theoretically,make conducting appropriate experiments easier. We can butimagine, what would the communication problems be inrelation with an entity that would resemble nothing that wehave ever seen, if such an entity is not provided with aproper language beforehand.

It seems that the contemporary level of speech analysis andsimulation offers a possibility of equipping a neuralnetwork with capability to understand speech and to use itconsciously in contact with other entities. This capabilitymust be developed as a learning process. The network’sability to recognize and categorize objects must be used inparallel in two modalities. There must be presentation ofobjects and simultaneous naming of the said objects. Themental representations of objects and their names,recognized at the level of cognitive functions, will besimultaneously transferred to CE, where created will be auniversal pattern for a recurring new excitationconfigurations related to these objects. A similarsituation of repeating sequences of words that coincideswith certain operations on the presented objects, may

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result in creation of patterns corresponding to syntaxrelations between words. If these sets of symbols expressedas sequences of phonemes will be fed to the speechgenerator, the network will gain ability to express its ownthoughts. This way a new symbolic language, expressing themental processes within the network, will be created.

Using a complex symbolic system will allow the network tocreate abstract ideas, with a higher level of generality.This requires a conscious operation on sentences of thatlanguage, sentences that also are episodes recorded in theepisodic memory of the speech centre. Activating theselingual episodes may be achieved by exciting the inputlayers of the speech centre by signals originating in CE onthe basis of associations with ideas being processed in CEstructures. A different possibility is a conscious focusingof attention on uttered sentences or following a verbalizedlogical reasoning. In such a case, lingual episodes will befed to CE and possibly connected to logical symbols oradditional emotional motivators (like ambition,stubbornness, fear, etc.). This will force the network tosearch for complex configurations (via “curiosity”)connected with ideas defined by the speech centre. This waya conscious fulfillment of curiosity will be demonstrated,which will result in conscious cognitive actions.

6. Internal communication – language of mental states.

External, symbolic knowledge must be translated into thelanguage of neural excitations. The language of excitationconfigurations is individual for each and every network, asit stems from that network’s experiences: the set ofexperiences or even the order in which they were gained.Moreover, natural neural networks are characterized byunique configuration of neural connections. Due to this,every network deconstructs and processes an image orepisode in a different way on the intermediary levels. Inconsequence, there is no universal system for recognizingand categorizing of notions or ideas by the network. Every

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network creates its own individual language of mentalconfigurations, which, due to high complexity of networksand individual history of gaining experiences, self-teaching and teaching, makes learning such internallanguage impossible.

This is why an automatic translating system for external,encyclopaedic knowledge remains an impossibility. Knowledgemay only be translated via a process of presenting the saidknowledge via the senses. It must be “lived through” by thesystem and connected to previous experiences. The processof such translation will therefore be similar to theprocesses of learning exhibited by the human brain. Despitethe artificiality of the network and seemingly easy accessto its mental states, the learning process will be just asslow.

This will be true especially in relation to new, complexideas that will require individual approach and patience inorder to find a form that would be accessible for everyindividual network. Lack of universal internal languagemakes it impossible for the entities of the external worldto communicate using this language. However, it is possiblefor the networks to create a universal symbolic languagethat will allow them to express their mental states, justas it happens with humans. This language will allowcommunication, coordination, learning or social action.

Individual language of mental state configuration serves asa barrier against intruding into a human personality. Everyconscious brain will be forced to create its own model ofthe world and its own viewpoint, stemming from the personalexperiences, learning and self-learning, pondering andinternal, verbalized discussion.

Even if we copied the exact same neural networks, filledwith preconceived knowledge, such approach does notguarantee creating uniform personalities. Intelligententities, just as their independent “life” starts, willbegin to gather and categorize differentiated experiences

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which will determine their form and function. On the otherhand the infallibility and processing speed of machinememory, no limitations as to its capacity and developmentof knowledge generalization levels may result in emergingof an intelligence and self-consciousness far suppressingthe human ones.

7. Conclusions

The proposed architecture of the cognitive neural networkwill be capable of categorizing the recognized objects,recording and recalling episodes from its ownexistence/past, logical thinking and understanding itsenvironment. What is most important, the network willpossess motivation to act, to actively care for the comfortof its operation and to gather and record knowledge due toits own curiosity. The ability to act intelligently willresult in self-consciousness and many other higher mentalfunctions. These spectacular effects result from theability of the connectionist network to recognize andcategorize, from its ability to record its own experiencesand sensations in the episodic memory and from the abilityto recall these memories at will. It seems that thesesingular abilities may be well aided by complementing themby symbolic knowledge that humankind has gathered inavailable databases.

However, the issue is not as simple as it may appear.Networks with a symbolic architecture are able to expresswords and ideas recorded in their memory. The operationhowever resembles re-playing of stored data. Humans arealso capable or reading a given text. The ability tomechanically speak the words with or without understandingis not a great feat. Human brain, equipped withconnectionist architecture is able to, at any given moment,activate the mechanism for recognizing and understanding ofthe words being read. This ability is unknown to a networkwith a symbolic architecture in which word-symbols are notprocessed into an individual, internal language of neural

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configuration excitation. Association memory has in such acase no basis for correlation. The “Self” block receives nosignals of understanding. There is no self-consciousness.

The example of the human brain proves that there is apossibility of a hybrid architecture. In such a system, thesymbolic layer is analyzed by the cognitive network andfurther operations are carried out on the mentalrepresentations of the input signals. This can bereplicated in artificial brains without difficulties. Thedownside of this characteristic is that we cannot simplycopy, into an artificial brain, a piece of softwarecontaining symbolic knowledge as in case of e.g.algorythimc databases. This is because we cannot foreseethe method in which a given connectionist network willcategorize information. With a large number of elements,this can be a chaotic process and its effects can beunique. What is more, even if the networks are identical,the categorization depends on previous experiences. This iswhy the process of “translating” the symbolic knowledgeinto the language of internal mental states must berealized via a strenuous process of individual learning.The effects of this learning could be different and dependon “capabilities” of a given network, its motivation andhierarchy of “values.” This way we know that each suchnetwork will possess unique personality. In every case wewill have to deal with an individual Person.

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teoretyczna, Ch. 14, pp. 329-361, editor R. Tadeusiewicz.,

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24     Wieslaw L. Galus

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