Ali Hashemi, Fatemeh Mahdavirad
A cross-cultural, cross-disciplinary, and cross-gender study on Appraisal resources in PhD dissertation abstracts: Martin & White's (2005) Appraisal Theory in focus
Heliyon
attempts formulate computational understanding brain function one fundamental concerns data structure brain represents information many decades conceptual framework dominated thinking brain modelers neurobiologists framework referred classical neural networks well supported experimental data although may incomplete characterization framework offered next section difficulties modeling important functional aspects brain basis classical neural networks alone led recognition another general mechanism must invoked explain brain function mechanism call binding binding neural signal synchrony mentioned several times literature legndy 1970 26bair w koch c temporal precision spike trains extrastriate cortex behaving monkeyneural comput 1996 8 4466crossref google scholar fully formulated general phenomenon 38baylis gc rolls et leonard cm selectivity faces responses population neurons cortex superior temporal sulcus monkeybrain res 1985 342 91102crossref pubmed google scholar although experimental evidence neural synchrony soon found idea largely ignored many years recently become topic animated discussion follows summarize nature roots idea binding especially temporal binding discuss objections raised classical neural networks developed models brain function developing models several questions needed addressed 1 brain states interpreted representations actual situations words neural activity interpreted neural code computer parlance data structure 2 nature mechanisms brain states organized 3 format information laid permanently brain 4 memory laid words mechanisms learning questions remain open shall see plausible answers offered classical neural networks interestingly context field artificial intelligence general answers questions provided conviction specific problems need specific data structures specific algorithms parlance mechanisms organization neuroscience neural modeling hand ambition find general answers commitment generality results binding problem fundamental feature neural code discussing answers questions postulated classical neural networks important introduce important parameter although often made explicit important fix temporal scale refer psychological moment sense short period times shorter one speaks mental state brain state whereas times greater one sees succession states state history whereas state history subject conscious scrutiny potentially gets reflected modalitiesmemory language etc conscious analysis possible state history ignored models conceptual disagreement binding issue focuses exclusively definition state times difficult pin definite value plausible range may 50 200 msec regardless exact value important realize arena discussion binding problem time scale less classical neural networks described deeply engrained set concepts often attributed 14alonso jm martinez lm functional connectivity simple cells complex cells cat striate cortexnat neurosci 1998 1 395403crossref pubmed google scholar 13albright td stoner gr visual motion perceptionproc natl acad sci usa 1995 92 24332440crossref pubmed scopus 76 google scholar reality much older give definite answers questions 14 neural code neurons taken concrete symbols semantic atoms interpreted relation patterns events external organism neurophysiology provided solid experimental basis statement although extrapolation needed extend neurons brain interpretation neurons semantic atoms generally accepted matter much dispute however following addition focus discussion neuron one degree freedom given psychological moment either certain degree thus brain state described listor vectorof neural activities order know brain given psychological moment necessary know vector well course description symbolic meanings neurons interpretation brain activity deliberately ignores fact actually recorded neural signals constant sense tor fixed time scale matter rather maintained observed microstate history inconsequential function brain b mechanism organization brain states based fluxes excitation inhibition neuron collecting incoming signals firing threshold surpassed see instance switching rule 25bair w koch c precision reliability neocortical spike trains behaving monkeyin kluwer bj neurobiology computation academic publishers new york1995google scholar dynamics system regulated stabilizes activity within psychological moment associative memory models instance achieved requiring connections pair neurons symmetric consequence system displays attractor dynamics without restriction mcculloch pitts system would general digital machine without inherent tendency organize kept track force majeure programmer detailed insight switching process c longterm memory stored terms synaptic weights longterm memory laid mechanisms synaptic plasticity based statistics neural signals especially temporal correlations postulates referred framework classical neural networks forms conceptual basis large important part current neurosciences especially genre brain modeling usually referred neural networks connectionism since existing neural models cover small range brains functional repertoire allimportant issue whether framework constitutes adequate basis conquer rest solely constructing appropriate specific wiring diagrams control parameters argued classical code neural networks poor narrow possibilities serve basis expansion functional range current brain models 40beck j perceptual grouping produced changes orientation shapescience 1966 154 538540crossref pubmed google scholar fodor pylyshin 1988 underlying weakness best illustrated classic example due frank 31ballard dh hinton ge sejnowski tj parallel visual computationnature 1983 306 2126crossref pubmed scopus 49 google scholar imagine specific neural network visual recognition internally structured derive four propositions represent output neurons two neurons recognize objects triangle square generalizing position two indicate position objects image upper half lower half generalizing nature object see figure 1 showing single objects network responds adequately eg triangle top square bottom problem arises however two objects present simultaneously output reads triangle square top bottom clear whether triangle square upper position binding problem neural data structure provide means binding proposition top proposition triangle bottom square correct description typographical system could easily done rearranging symbols adding brackets triangle top square bottom problem code classical neural networks provides neither equivalent brackets rearrangement symbols fundamental problem classical neural network code flexible means constructing higherlevel symbols combining elementary symbols difficulty simply coactivating elementary symbols leads binding ambiguity one composite symbol expressed lets assume vital organism trigger action response triangle upper position lower one reaction would tied coincidence activity cells triangle top however would also occur triangle bottom square top animal therefore would respond socalled false conjunction perhaps grave consequences analogous situation occurs brain correspondence object type position explicit retinal level loss way output circuit due generalization taking place within circuit instance brains pathways temporal parietal pathways primate cortex problem general one implications far beyond vision imagine mental object represented set p neurons refer figure 2 another mental object represented set q possibly overlapping p point becomes important activate objects mental operation instance comparing would natural coactivate sets brain state coactivation however leads call superposition catastrophe two sets merge one neural code express information needed subdivide composite state components see figure 2 rosenblatts problem simple solution terms combinationcoding cells would suffice existed neuron reacted triangle top position set p q neurons could realized help connections lower elemental levels generalization yet taken place however problem arises appropriate combinationcoding cells exist cannot exist due impracticality large numbers required previous history system connection patterns could cause confusion assumption combination coding cells available required problematic system modeled general purpose device symbol systems means combining elementary symbols complex ones handled units without danger ambiguity explicit structure basis compared recognized decomposed combined build even higher structures classical code neural networks doesnt means root binding problem curious proposition brain ultimate handler symbol systems shouldnt general mechanism combining subsymbols symbol systems mathematics natural languages suggest complex binding patterns may required grouping number elements one block internal structure visual image extended object may need represented array local features bound together way expresses topological neighborhood relationships within figure even hierarchy object parts 6adelson eh perceptual organization judgment brightnessscience 1993 262 20422044crossref pubmed google scholar similarly representation natural language structures requires binding arrangements hierarchical structure purely hierarchical system combinationcoding cells may good reasons certain combinationcoding cells exist complex combination cell represents special context refers experience gained particular context recorded terms changed synaptic connections affixed general description situation exploited contexts instance something associated appearance triangle happened upper position would inefficient affix association uppertriangle cell experience would repeated relearned positions triangle similar vein absolute location speck ink paper little relevance counts relative location pattern environment complex combinatorial complex objects situations constructed combining simpler elements try represent complexity noncombinatorial way letting single cells stand external objects complexity appears terribly inefficient strategy combinatorial symbol system however needs mechanism bind elements groups evidence exists suggests brain simply use combinationcoding cells process stimuli psychophysical studies show certain conditions brains binding mechanism may fail people report illusory conjunctions see wolfe cave 1999 issue neuron given enough viewing time subjects commit false conjunction errors related vein conjunction search experiments 36barlow hb twelfth bartlett memorial lecture role single neurons psychology perceptionquart j exp psychol 1985 37 121145crossref google scholar subjects asked find object specific combination features array distractors one features show reaction times scale linearly total number elements display types experiments one conclude relevant combinations features represented combinationcoding cells brain requires time form ascertain correct bindings widespread opinion classical neural networks universal medium limits abilities consequently subject binding problem address claim two steps first discuss whether universality suffices solution brains problems raise doubts whether classical neural networks indeed universal medium idea universality crystallized alan turings formulation turing machine demonstration effective procedure conceived cannot realized program turing machine thus completely specified function realized program algorithm run computer limits storage space time extrapolated mental processes made concrete terms rules could realizable machines view brain digital machine universality computer turing machine sufficiently many neurons available 25bair w koch c precision reliability neocortical spike trains behaving monkeyin kluwer bj neurobiology computation academic publishers new york1995google scholar applied idea modeling nervous system proving logical function realized network threshold elements appropriately connected universality buy compared universality pen sufficiently many sheets white paper universal medium formulating novels still write time field artificial intelligence discovered practical task write program emulates capabilities brain becoming increasingly clear goal hope establish system constitutes basis selforganization learning equivalent newborn better still genetic program development brain let learn experience communication others brain theorists realized late 1950s modified mcculloch pitts networks accommodate selforganization learning resulting framework classical neural networks tendency fall stable patterns learns synaptic plasticity however changes may come price clear whether neural networks universal sense although community seems inherited implicit belief brain function modeled basis abstractions real nervous system went formulation neural networks even clear formulate new universality theorem classical universality states give procedure ill tell implement neural network version universality would give brain problem able implement classical neural networks characterize brain problems general satisfactory way would foolish argue particular problem solved basis classical neural networks proves solved way context present discussion particular version universality critics binding problem uphold state concrete problem solve classical neural network without ever running binding ambiguity common argument support posits binding ambiguity implicit stated problem dealt concrete combinationcoding cells model network solves example see riesenhuber poggio 1999a issue neuron concrete example approach mel fisers model 1999 recognizing words text network codes text terms triplets contiguous letters based statistical observation english two words agree letter triplets thus special case model completely avoids binding problem ensuing compositional ambiguity would beriddle model based representation singleletter cells although examples meant support general universality claim doubtful claim ever established easy state problems far beyond abilities present neural network models think task emulate human ability segment visual scenes necessary cue integration although problem old exists classical neural network solution perhaps good reason could binding implemented brain basic idea temporal binding signals neurons grouped together correlated time neural signals thus evaluated two ways one classical concept neural firing rate relevant parameter running average number spikes arriving within period second concerns temporal correlations signal fluctuations happening time scales faster correlations express binding subdivision time scales final account arbitrary unless one sticks distinction changes slower accessible introspection faster changes pick scale evaluate signal fluctuations terms correlations calling fluctuations rate changes course lower bound depends fastest temporal scale processed neural tissue brain related psychological moment illdefined may take order 50 200 msec although scales minutes beyond also potential relevance throughout discussion discuss signal correlations evaluated without taking account relative delays however may necessary also consider delayed coincidences argued 8adelson eh movshon ja phenomenal coherence moving visual patternsnature 1982 300 523525crossref pubmed google scholar understand general idea temporal binding exact nature signal fluctuations relevant much discussion experimental evidence concerning whether oscillatory signals important respect discussion side issue shouldnt cloud fundamental question whether binding problem whether neural signal correlations solution problem although much discussion signal correlations focuses binary case correlation two neurons emphasized much relevant important type event concerns correlations higher order simultaneous firing larger groups neurons reason correlations play important role brain must possible evaluated quickly reliably since given set impulses may accidental coincidences vitally important discern true correlations noisy background binary correlations possible long observation times correlations sufficiently high order even individual coincident events become highly significant regard plausibility temporal binding several fundamental questions need discussed 1 correlations temporal signal structure arise ultimately purpose temporal binding express significant relations data items eg causal spatial nature physical interactions establishing relations must represented signal correlations since many interactions already present external world temporal correlations imposed external stimuli triggering neural signals happens causally related external events evident less often realized due bodily eye movements create stream sensory impressions whose temporal structure expresses spatial structure environment thus signal correlations relevant binding already implicit perceptual input see also singer 1999b issue neuron short aside argument often raised use temporal patterns expressing binding may clash use temporal patterns purposes representation temporal structure given external world especially relevant auditory language motor modalities clash may avoided nervous system recoding temporal signals format involve rapid signal changes single neurons responding representing syllables would example correlations probably overwhelming majority created within nervous system synaptic connections neuron fired neuron b signals two would correlated disregarding small delay correlations induced synaptic connections also signify causality addition activity neurons without connections connections common input correlated rosenblatt example binding problem could solved neurons v1 activated triangle given position pass temporal signature signals neurons expressing shape identity one hand position signals came express common origin 2 signal correlations evaluated brain two action potentials arrive common target neuron relative timing exerts strong influence signals arrive simultaneously cooperate raise neuronal membrane potential firing threshold however miss time effect first impulse decayed second arrives might fail fire target neuron thus neurons act coincidence detectors evaluate signal correlations 1abeles role cortical neuron integrator coincidence detectorisrael j med sci 1982 18 8392pubmed google scholar 23assad j maunsell j neuronal correlates inferred motion primate posterior parietal cortexnature 1995 373 518521crossref pubmed google scholar exact details interaction depend upon many complex factors including membrane time constants nonlinear effects dendritic geometry consequence current neurophysiology cannot solidly predict temporal resolution spike coincidences evaluated however likely range 110 msec correlations evaluated globally single neurons combinationcoding cell would required binding pattern defeating purpose binding however complex correlation patterns created circuit interconnected neurons evaluated circuits appropriately interconnected neurons individual neuron checking small subpattern thus pairs circuits may may resonate terms correlation patterns produce point probably easily understood reference concrete models invariant object recognition discussed proposed 38baylis gc rolls et leonard cm selectivity faces responses population neurons cortex superior temporal sulcus monkeybrain res 1985 342 91102crossref pubmed google scholar correlation patterns also evaluated rapid reversible synaptic plasticity addition slow plasticity connection physically present would cause confusion given situation could temporarily inactivated activity presynaptic postsynaptic side sensed uncorrelated confusion could thus suppressed given situation even signals involved develop stray coincidences switchedoff connection returned near previous value time scale seconds minutes 3 correlation patterns effective physiological timescales play role brains function mandatory evaluated within short time intervals finding pair correlated neurons set others firing stochastically may take unrealistically long integration times situation improved two ways previously mentioned one way relies coincidences high order even superimposed stochastic signals single event n simultaneous spikes n large enough say 50 100 high statistical significance way relies suppression accidental correlations appropriate inhibitory circuits thus large set neurons needs subdivided several bound subsets inhibition subsets make sure coincident spikes neurons different subsets occur integral part many models eg 43bergen jr adelson eh early vision texture perceptionnature 1988 333 363364crossref pubmed google scholar 49bienenstock e model neocortexnetwork 1995 6 179224crossref scopus 101 google scholar 4 network patterns created required production evaluation significant firing patterns random connection patterns neither able create significant firing patterns able distinguish many arguments raised validity idea temporal binding eg 34barlow hb single units cognition neurone doctrine perceptual psychologyperception 1972 1 371394crossref pubmed google scholar 33barbas h pandya dn architecture intrinsic connections prefrontal cortex rhesus monkeyj comp neurol 1989 286 353375crossref pubmed google scholar issue neuron implicitly explicitly based assumption random connectivity however nervous system endowed capacity selforganize using synaptic plasticity slow 46biederman