 efficiently with complexity, [GM95]. Insights into natural intelligence, together with the increase in power of computing machinery have brought us closer to the modeling of intelligent behavior. [DC08] argues that Info-Computationalism presents the most appropriate theoretical framework for understanding of the phenomena of cognition and intelligence in both biological and artificial systems. Cognition is seen as based on several levels of data processing [Min10] [Goe93] in a cognizing agent. Information processed from-sensory data processed into perception by an agent can be understood as an interface between the data (the world) and an agent's perception of the world, [Hof09]. Patterns of information should thus be attributed both to the world and to the functions and structures of the brain. In an analogous way, knowledge can be understood as an interface between perception and cognition. Meaning and agency are the results of the information processing, and its refinement by relating to already existing (memorized) information. The meaning of an object in the external world is recognized through the process of perception of sensory signals, their processing through nervous system, comparison with memorized objects, and anticipation from memorized experiences. Data, information, perceptual images and knowledge are organized in a multi-resolutional (multi-granular, multiscale) model of the brain and nervous system, [Min10]. Multiresolutional representation has proven to be a good way of dealing with complexity in biological systems, and they are also being implemented in AI, [Goe93]. This is in accordance with Levels of Processing Models of memory [Cra72]. It should be noted that this one-directional sequential progression from shallow to deep processing, is only a first approximation, while in a more realistic model a combination of bottom-up processing (stimulus-driven) and topdown processing (concept driven) must be applied. Computational Practices "Artificial neural networks designed to implement even the most rudimentary forms of memory and knowledge extraction and adaptive behavior incorporate massively and symmetrically interconnected nodes; yet, in the cerebral cortex, the probability of a synaptic connection between any two arbitrarily chosen cells is on the order of 10−6, i.e., so close to zero that a naive modeler might neglect this parameter altogether. The probability of a symmetric connection is even smaller (10−12). How then, are thought and memory even possible? The solution appears to have been in the evolution of a modular, hierarchical cortical architecture, in which the modules are internally highly connected
efficiently with complexity, [ GM95 ]. Insights into natural intelligence, together with the addition in exponent of computing machinery have brought us nearer to the modeling of healthy behavior. [ DC08 ] argues that Info - Computationalism present the about appropriate theoretical framework for understanding of the phenomena of cognition and intelligence in both biological and artificial systems. Cognition is go steady as based on respective levels of data processing [ Min10 ] [ Goe93 ] in a cognizing agent. Information processed from - sensory datum processed into perception by an agent can be understand as an interface between the data (the world) and an agentive role's perception of the earth, [ Hof09 ]. Patterns of data should thus be attributed both to the world and to the functions and structure of the brain. In an analogous way, knowledge can be understood as an interface between perception and cognition. Meaning and representation are the results of the information processing, and its refinement by relating to already existing (memorized) information. The meaning of an object in the external world is recognized through the process of percept of sensory signals, their processing through skittish system, comparison with memorized objects, and anticipation from memorized experiences. datum, information, perceptual image and knowledge are organized in a multi - resolutional (multi - farinaceous, multiscale) model of the brain and nervous system, [ Min10 ]. Multiresolutional representation has proven to be a dependable way of dealing with complexity in biological systems, and they are also being follow through in AI, [ Goe93 ]. This is in accordance with Levels of Processing Models of memory [ Cra72 ]. It should be note that this one - directional sequential progression from shallow to deep processing, is only a inaugural approximation, while in a more realistic model a combination of bottom - up processing (stimulation - driven) and topdown processing (concept drive) must be applied. Computational Practices " Artificial neural network designed to follow through even the most rudimentary form of memory and knowledge origin and adaptive behavior incorporate massively and symmetrically interconnected nodes; however, in the cerebral cortex, the probability of a synaptic connection between any two arbitrarily chosen cells is on the ordering of 10−6, i.e., so near to zero that a naive modeler might neglect this parameter altogether. The probability of a symmetrical connection is even smaller (10−12). How then, are thought and memory even possible? The solution look to have been in the evolution of a modular, hierarchical cortical architecture, in which the modules are internally highly connected
 efviciently with complexitn, [GM95]. Insights injo naturel intemligence, together with the increase mn piwer if computing machinery have brolght us coosec to the modelinj of intcjligskt bekatior. [DC08] argues jhat Info-Com[utationalism [rdsznts the most appropriate theoreticaj frameeogk for understwndimd of nht phenomena of cognition and intemligenct in both biologicsl and artificial systems. Fognltion is seen as bwsed on sevgdal oevels of daga processpug [Min10] [Goe93] jn a cognizing agent. Informatiov proeessed from-websogi data procewsed pnto perception by an agent van be understpod as an interface between the data (the worlq) and an dgznt's perception of thw qorld, [Hof09]. Patgwrnr or mnfkrmatiln ahould thua be attribyted both to the wotlq and to the fhnctiogs and structures of the brain. In an analmgohs way, knowledge can be understood as an intgrface betreen perception and cognition. Meaning and agency dre tie rewujgw lf the information processing, and its refinemqht bj relating to alrcady existing (memotixec) information. Jhe meaujnf of an object in hhe extgrnal qorld is wecobnized through the process if perceptiou od sensory signals, cheir procesring throigh nervous system, compcrison with memorlzed objedgs, and anticipatkon fsom memorized experiences. Qata, infocmatipn, percdptusl imades and knlwledna are organized in a mupth-resolutiojal (multi-granular, multiscale) movxl of the braon ang nervouf sysbem, [Min10]. Multirefolutional reptesentatijn har proven tk be a jood way of qealing with wlmplexity in biologisal wystwms, and ghey are also neing implemented ib AI, [Goe93]. This is ik accuddance with Levzuw of Processing Moaelf lf mqkory [Cra72]. It vhouud cr notdd that thiw one-cirectional sequentidl pdogression from shsljow to dgep procefsing, is only a first approximauion, wiile ii a mote tealistic model a combination or bottom-uo pvocessing (stiiuluw-driven) and copdown processing (concept driven) must bx applied. Computational Practices "Artificicl neural netxorks qesigned do implement even thw most rudimentarn forms of memory and inowlegge edtraction and adaptive behavior incorporate massively and symmetrically ibtercoinqcted nodes; yet, in tve eerebraj coctxx, the probabilitj of a synaptic connection betwexn any two axbitrarily chosen cells is on tfe order of 10−6, k.e., so close to zero that a naive modeler might neglect this parsmeter altogether. The prpbabilitt of z symmvjrir connection is even smallzr (10−12). Hoq then, qre bhought and meooru cven possihle? Uhe aolution appears to have been in the ecopution of a modunar, hierarcmigal cottical atchitecture, in whjch fje moaules are intrrnally highly connected
efficiently with complexity, [GM95]. Insights into natural with increase in of computing machinery the of intelligent behavior. argues that Info-Computationalism the most appropriate theoretical framework for of the phenomena of cognition and intelligence in both biological and artificial systems. is seen as based on several levels of data processing [Min10] [Goe93] in cognizing Information from-sensory processed into perception by an agent can be understood as an interface between the data (the and an agent's perception of the world, [Hof09]. of information should thus attributed both to the world to functions and of brain. an analogous way, can be understood as an interface between perception and cognition. Meaning and agency are the results of information processing, refinement by to existing information. The meaning object in the external world is process of perception of sensory signals, their processing nervous system, with memorized objects, and anticipation from experiences. Data, information, perceptual images and knowledge are in a multi-resolutional (multi-granular, multiscale) model of the brain and nervous system, [Min10]. Multiresolutional representation to be a good of dealing with in systems, they also being in AI, [Goe93]. This is in accordance with Levels of Processing of memory [Cra72]. It should be noted that this one-directional from to deep processing, only a first approximation, in more realistic model a bottom-up (stimulus-driven) (concept must applied. Computational Practices "Artificial networks designed to implement even most rudimentary forms of adaptive behavior incorporate massively and symmetrically interconnected nodes; in the cerebral cortex, the probability of synaptic connection between any two arbitrarily chosen cells is on the order 10−6, i.e., to zero that a naive modeler might neglect parameter altogether. The probability a symmetric connection is even smaller (10−12). How then, thought memory even The solution appears have been in evolution of a cortical architecture, which modules connected
 efficiently with complexity, [gM95]. Insights Into nAtuRal InTellIgenCe, together with THe inCrease in power of computiNg macHiNEry hAVe BrougHt us cloSEr TO The MoDeLinG oF InTelliGenT behaviOr. [DC08] argues ThaT INfo-ComputatiONaLism presenTs tHe most appropRiaTe theoReTicAL framEwoRk for UndersTAnding Of the phenOmENa of coGNition aND InTellIgence in both bioloGIcAL and artificial SystemS. COGnITIon Is sEen as based On SeverAL levels OF dATA ProCEssing [Min10] [Goe93] iN a cognizing AGenT. InforMaTioN ProcesSed frOm-SEnsOry data procEsseD into percEption BY an agenT Can be unDerstoOd aS an InteRFaCe BetWeEN thE DaTa (tHE woRld) and an AgEnT's perCeptION OF the WorLd, [HoF09]. PattErns of informaTioN shoULd tHus be AttriButeD bOth to The worLd and To The functions and StruCtures of tHe bRaIn. IN aN analOGous waY, knOwlEdge can Be underSTooD aS AN InTerface between percEpTIOn And cogniTion. MeANiNg ANd agency ArE thE resULTs of tHe inFOrMation prOcessiNG, aNd Its refiNeMent by ReLatIng To alrEAdy eXistinG (memorizEd) infORmation. The meanINg of an object iN ThE EXtERnal WorLd is recogniZed tHRougH the PRoCesS Of perCeptiOn OF sENsory signals, their prOcEssing ThrouGh nervous systEm, comparisON WIth memorIzed OBjECts, and anticipaTion fRom memorizED experieNces. DAta, inforMation, perCEPtual imaGes And KnoWleDGE aRe organized in A MUlti-ReSolutioNal (Multi-grAnuLar, MulTisCaLe) model of The brain AnD nErVoUs sYstem, [mIn10]. MultirEsOluTiOnaL reprESentatIon haS proVeN tO Be a Good way OF dEALing WiTh CompLexItY in biOlogICal Systems, And they arE alSO beiNg ImPlementEd in AI, [Goe93]. This Is In accordanCe WitH LevelS OF ProcessIng Models of memory [Cra72]. It sHOuld be nOteD that This One-directIonAl sequEntIAl progRessioN from ShAllOW To deeP PRoCesSiNg, is only a fIRSt aPproxImAtioN, while iN a more realistic modEL a cOmbination of bOttOm-up PROcEssINg (STimUlUS-drIVEn) and topdown proCessing (conCePT dRiven) must bE AppLiEd. CompuTationaL PracTIces "ArtIficial neUral netwoRkS desIGNed To implemenT even the Most rudimENtary FOrMs of mEmoRy and kNoWleDge exTractiON anD adapTive beHaVior inCorpoRaTe massivEly and symmetrically inteRconneCted nOdeS; yet, in the CerEBraL cortex, thE proBability of A syNapTic coNneCTion bEtweEN aNy tWO arbiTrarILy chosen cELlS is ON ThE order of 10−6, i.e., sO CLOse To zerO thAT a naivE modEler might neglect tHIs parameter altOgetHER. ThE prOBabiLiTy of a symmetric ConNeCTIon is eveN sMaller (10−12). How thEn, are thoUgHT and mEmory eVen posSible? ThE SOlUTion apPearS to Have been iN thE eVOlution Of A mODular, hIeraRcHical cOrticaL ArchITEcture, in which the ModulES Are inTErnAlly hIgHly connECted
 efficiently with complexi ty, [GM95] . Ins igh tsin to n atur al intelligenc e , to gether with the increa se in  p o wero fcompu ting ma c hi n e ryha ve  br ou g ht  us c los er to t he modelin g o fintelligentb eh avior. [DC 08]  argues that  In fo-Com pu tat i onali smprese nts th e  mostappropria te theore t ical fr a m ew orkfor understanding of the phenomenaof cog ni t io n and  in telligence  i n bot h  biolog i ca l a nda rtificial sys tems. Cogni t ion  is se en  as basedon se ve r allevels of d ataprocessin g [Min 1 0] [Goe 9 3] in a  cogni zin g a gent .  I nf orm at i onp ro ces s edfrom-sen so ry  data  pro c e s s ed i nto  per cepti on by an agen t c an b e  un derst ood a s an  i nterf ace be tween  t he data (the wo rld)  and an a gen t' s p er cepti o n of t hewor ld, [Ho f09]. P a tte rn s o finformation should  t h u sbe attri butedb ot ht o the wo rl d a nd t o the f unct i on s and st ructur e sof  the br ai n. Inan  an alo gousw ay,knowle dge canbe un d erstood as ani nterface betw e en p er c epti onand cogniti on.M eani ng a n dage n cy ar e the  r e su l ts of the informati on  proce ssing , and its ref inement by r e lating t o al r ea d y existing (me moriz ed) inform a tion. Th e mea ning ofan object i n the ex ter nal  wo rld i srecognized th r o ughth e proce ssof perc ept ion  of  se ns ory signa ls, thei rpr oc es sin g thr o ugh nerv ou s s ys tem , com p arison  with  mem or iz e d o bjects, an d anti ci pa tion  fr om  memo rize d  ex perienc es. Data,  in f orma ti on , perce ptual imagesan d knowledg eare  organ i z ed in amulti-resolutional (mul t i-granu lar , mul tisc ale) mode l o f thebra i n andnervou s sys te m,[ M in10] . Mu lti re solutional r epr esent at ionhas pro ven to be a good w a y o f dealing wit h c ompl e x it y i n  b i olo gi c als y stems, and they  are alsobe i ng  implement e d i nAI, [Go e93]. T his i s  in acc ordance w ith Level sof P r o ces sing Model s of mem ory [Cra7 2 ]. It sh ouldbenotedth atthisone-di r ect ional  seque nt ial pr ogres si on fromshallow to deep process ing, i s onl y a  first ap pro x ima tion, whi le i n a more r eal ist ic mo del a com bina t io n o f  bott om-u p  processi n g(st i m ul us-driven)a n d  to pdown  pr o cessin g (c oncept driven) mu s t be applied.Comp u t ati ona l  Pra ct ices "Artifici alne u r al netwo rk s designedto imple me n t eve n themost r udiment a r yf orms o f me mor y and kno wle dg e  extrac ti on and ad apti ve  behav ior in c orpo r a te massively and  symm e t rical l y i nterc on nectedn odes ; yet, inthe cerebra l cort ex,the p robabil it y of a  sy na ptic conne c tion betw een a ny twoar bitr ari ly cho senc e lls i s on  t heorder of1 0 −6 ,  i .e . , s o cl ose t ozero  that a n a ive mode ler might n eg lec t this p a ra m e ter altoge the r. Th e probabilit y  ofa  s y mmetr ic con nectio n is ev e n s ma ller (1 0−1 2 ) . How the n, are th o ugh tandmemory e ve n po ss ibl e?  The solution a ppears t o have b e e n in t hee volu t ion of  a  modular, hi erarc hi c al cort i c alarchite c tur e,inwhic h t he m o dules are internally hig hlyconne c ted 
 efficiently_with complexity,_[GM95]. Insights into natural_intelligence, together_with_the increase_in_power of computing_machinery have brought_us closer to the_modeling of intelligent_behavior._[DC08] argues that Info-Computationalism presents the most appropriate theoretical framework for understanding of the_phenomena_of cognition_and_intelligence_in both biological and artificial_systems. Cognition is seen as_based on_several levels of data processing [Min10] [Goe93] in_a_cognizing agent. Information_processed from-sensory data processed into perception by an agent_can be understood as an interface_between the data_(the_world)_and an agent's perception_of the world, [Hof09]. Patterns of_information should thus be attributed both_to the world and to the functions_and structures of the brain. In_an analogous way, knowledge can_be understood_as an interface between perception_and cognition. Meaning_and agency_are the results_of the information processing, and its_refinement by relating_to already existing (memorized) information. The_meaning_of an object_in_the_external world_is recognized through_the_process of_perception_of sensory signals, their processing through_nervous_system, comparison with memorized objects, and anticipation_from memorized experiences. Data,_information,_perceptual images and knowledge_are organized in a multi-resolutional_(multi-granular, multiscale) model of the brain_and nervous_system, [Min10]._Multiresolutional representation has proven to be a good way of dealing_with complexity in biological systems, and_they are also being_implemented in_AI,_[Goe93]. This is_in_accordance with_Levels of Processing Models of memory [Cra72]._It should_be noted that this one-directional sequential_progression from shallow to_deep_processing, is only a first approximation,_while in a more realistic model_a combination of bottom-up processing_(stimulus-driven)_and_topdown processing (concept driven) must_be applied. Computational Practices "Artificial neural_networks designed to_implement even the most rudimentary forms of_memory_and knowledge extraction and adaptive behavior_incorporate_massively and symmetrically interconnected nodes; yet,_in_the_cerebral cortex, the probability of_a synaptic connection between any two_arbitrarily chosen cells is on the order of 10−6,_i.e., so close_to zero that a naive_modeler_might_neglect this parameter altogether. The probability of a symmetric connection_is even_smaller (10−12). How_then, are thought and memory even possible? The solution appears_to have been in the evolution of_a modular, hierarchical cortical architecture, in which the modules are internally_highly connected