Wenzao Ursuline College of Languages Kaohsiung Taiwan On

Wenzao Ursuline College of Languages Kaohsiung, Taiwan On the Neurocognitive Basis of Language Sydney Lamb lamb@rice. edu 2010 November 12

Why is it important to consider the brain? “I gather…that the status of linguistic theories continues to be a difficult problem. … I would wish, cautiously, to make the suggestion, that perhaps a further touchstone may be added: to what esxtent does the throry tie in with other, non-linguistic information, for example, the anatomical aspects of language? In the end such bridges link a theory to the broader body of scientific knowledge. ” Norman Geschwind “The development of the brain and the evolution of language”

Topics • • • A little neuroanatomy Functional webs Nodes and links: Cortical columns Basic operations in the cortex More operations: Learning

Topics • A little neuroanatomy • Functional webs • Nodes and links: Cortical columns • Basic operations in the cortex • Syntax • More operations: Learning

The brain • • Medulla oblongata – Myelencephalon Pons and Cerebellum – Metencephalon Midbrain – Mesencephalon Thalamus and hypothalamus – Diencephalon • Cerebral hemispheres – Telencephalon – Cerebral cortex – Basal ganglia – Basal forebrain nuclei – Amygdaloid nucleus

Two hemispheres Left Interhemispheric fissure (a. k. a. longitudinal fissure) Right

Corpus Callosum Connects Hemispheres Corpus Callosum

Major Left Hemisphere landmarks Central Sulcus Sylvian fissure

Major landmarks and the four lobes Central Sulcus Frontal Lobe Sylvian fissure Parietal Lobe Temporal Lobe Occipital Lobe

Some brain facts – now well established • Locations of various kinds of “information” – Visual, auditory, tactile, motor, … • The brain is a network – Composed, ultimately, of neurons • Neurons are interconnected – Axons (with branches) – Dendrites (with branches) • Activity travels along neural pathways – Cortical neurons are clustered in columns • Columns come in different sizes – The smallest: minicolumn – 70 -110 neurons • Each minicolumn acts as a unit – When it becomes active all its neurons are

Deductions from known facts • Everything represented in the brain has the form of a network – (the “human information system”) • Therefore a person’s linguistic and conceptual system is a network – (part of the information system) • Every lexical entry and every concept is a sub -network – Term: functional web (Pulvermüller 2002)

Primary Areas Central Sulcus Primary Somatosensory Area Primary Motor Area Primary Auditory Area Sylvian fissure Primary Visual Area

Divisions of Primary Motor and Somatic Areas Leg Primary Motor Area Primary Somatosensory Area Trunk Arm Hand Fingers Mouth Primary Auditory Area Primary Visual Area

Higher level motor areas Actions per. Formed by leg Actions performed by hand Leg Primary Somatosensory Area Trunk Arm Hand Fingers Actions performed by mouth Mouth Primary Auditory Area Primary Visual Area

Hierarchy in cortical development

Topics • A little neuroanatomy • Functional webs • Nodes and links: Cortical columns • Basic operations in the cortex • Syntax • More operations: Learning

Hypothesis I: Functional Webs • • A word is represented as a functional web Spread over a wide area of cortex – Meaning includes perceptual information – As well as specifically conceptual information • For nominal concepts, mainly in – Angular gyrus – (? ) For some, middle temporal gyrus – (? ) For some, supramarginal

Example: The concept DOG • We know what a dog looks like – Visual information, in occipital lobe • We know what its bark sounds like – Auditory information, in temporal lobe • We know what its fur feels like – Somatosensory information, in parietal lobe • All of the above. . – constitute perceptual information – are subwebs with many nodes each – have to be interconnected into a larger web – along with further web structure for conceptual information

Building a model of a functional web: first steps Each node in this diagram represents the cardinal node* of a subweb of properties T For example C M V Let’s zoom in on this one *to be defined in a moment!

Zooming in on the “V” Node. . Cardinal V-node A network of visual features Etc. etc. (many layers)

Add phonological recognition For example, FORK C T M P V These are all cardinal nodes – each is supported by a subweb Labels for Properties: C – Conceptual M – Motor P – Phonological image T – Tactile V – Visual The phonological image of the spoken form [fork] (in Wernicke’s area)

Add node in primary auditory area For example, FORK C T M P PA V Labels for Properties: C – Conceptual M – Motor P – Phonological image PA – Primary Auditory T – Tactile V – Visual Primary Auditory: the cortical structures in the primary auditory cortex that are activated when the ears receive the vibrations of the spoken form [fork]

Add node for phonological production For example, FORK C T M P PP PA V Labels for Properties: C – Conceptual M – Motor P – Phonological image PA – Primary Auditory PP – Phonological Production T – Tactile V – Visual

Part of the functional web for DOG (showing cardinal nodes only) Each node shown here is the cardinal node of a subweb T M PP C P PA V For example, the cardinal node of the visual subweb

An activated functional web (with two subwebs partly shown) T C PP PR PA M C – Cardinal concept node M – Memories PA – Primary auditory PP – Phonological production PR – Phonological recognition T – Tactile V – Visual V Visual features

Ignition of a functional web from visual input T C PR Art PA M V

Ignition of a functional web from visual input T C PR Art PA M V

Ignition of a functional web from visual input T C PR Art PA M V

Ignition of a functional web from visual input T C PR Art PA M V

Ignition of a functional web from visual input T C PR Art PA M V

Ignition of a functional web from visual input T C PR Art PA M V

Ignition of a functional web from visual input T C PR Art PA M V

Ignition of a functional web from visual input T C PR Art PA M V

Ignition of a functional web from visual input T C PR Art PA M V

Ignition of a functional web from visual input T C PR Art PA M V

Ignition of a functional web from visual input T C PR Art PA M V

Ignition of a functional web from visual input T C PR Art PA M V

Ignition of a functional web from visual input T C PR Art PA M V

Ignition of a functional web from visual input T C PR Art PA M V

Speaking as a response to ignition of a web T C PR Art PA M V

Speaking as a response to ignition of a web T C PR Art PA M V

Speaking as a response to ignition of a web T C PR Art PA M From here (via subcortical structures) to the muscles that control the organs of articulation V

An MEG study from Max Planck Institute Levelt, Praamstra, Meyer, Helenius & Salmelin, J. Cog. Neuroscience 1998

Topics • A little neuroanatomy • Functional webs • Nodes and links: Cortical columns • Basic operations in the cortex • More operations: Learning

Hypothesis 2: Nodes as Cortical Columns • Nodes are implemented as cortical columns • The interconnections are represented by intercolumnar neural connections and synapses – Axonal fibers – neural output – Dendritic fibers – neural input

The node as a cortical column • The properties of the cortical column are approximately those described by Vernon Mountcastle – Mountcastle, Perceptual Neuroscience, 1998 • Additional properties of columns and functional webs can be derived from Mountcastle’s treatment together with neurolinguistic findings

Quote from Mountcastle “[T]he effective unit of operation…is not the single neuron and its axon, but bundles or groups of cells and their axons with similar functional properties and anatomical connections. ” Vernon Mountcastle, Perceptual Neuroscience (1998), p. 192

Three views of the gray matter Different stains show different features

Layers of the Cortex From top to bottom, about 3 mm

The Cerebral Cortex § Grey matter • Columns of neurons §White matter • Inter-column connections

Microelectrode penetrations in the paw area of a cat’s cortex

Columns for orientation of lines (visual cortex) Microelectrode penetrations K. Obermayer & G. G. Blasdell, 1993

The (Mini)Column • Width is about (or just larger than) the diameter of a single pyramidal cell – About 30– 50 m in diameter • Extends thru the six cortical layers – Three to six mm in length – The entire thickness of the cortex is accounted for by the columns • Roughly cylindrical in shape • If expanded by a factor of 100, the dimensions would

Simplified model of minicolumn I: Activation of neurons in a column Other cortical locations Cell Types II III Pyramidal Spiny Stellate Thalamus IV Inhibitory Connections to neighboring columns not shown V VI Subcortical locations

Cortical column structure • Minicolumn 30 -50 microns diameter • Recurrent axon collaterals of pyramidal neurons activate other neurons in same column • Inhibitory neurons can inhibit neurons of neighboring columns – Function: contrast • Excitatory connections can activate neighboring columns – In this case we get a bundle of contiguous columns acting as a unit

Cortical minicolumns: Quantities • • • Diameter of minicolumn: 30 microns Neurons per minicolumn: 70 -110 (avg. 75 -80) Minicolumns/mm 2 of cortical surface: 1460 Minicolumns/cm 2 of cortical surface: 146, 000 Neurons under 1 sq mm of cortical surface: 110, 000 • Approximate number of minicolumns in Wernicke’s area: 2, 920, 000 (at 20 sq cm for Wernicke’s area) Adapted from Mountcastle 1998: 96

Large-scale cortical anatomy • The cortex in each hemisphere – Appears to be a three-dimensional structure – But it is actually very thin and very broad • The grooves – sulci – are there because the cortex is “crumpled” so it will fit inside the skull

Topologically, the cortex of each hemisphere (not including white matter) is. . • Like a thick napkin, with – Area of about 1300 square centimeters • 200 sq. in. • 2600 sq cm for whole cortex – Thickness varying from 3 to 5 mm – Subdivided into six layers • Just looks 3 -dimensional because it is “crumpled” so that it will fit inside the skull

Topological essence of cortical structure (known facts from neuroanatomy) • The thickness of the cortex is entirely accounted for by the columns • Hence, the cortex is an array of nodes – A two-dimensional structure of interconnected nodes (columns) • Third dimension for – Internal structure of the nodes (columns) – Cortico-cortical connections (white matter)

Nodal interconnections (known facts from neuroanatomy) • Nodes (columns) are connected to – Nearby nodes – Distant nodes • Connections to nearby nodes are either excitatory or inhibitory – Via horizontal axons (through gray matter) • Connections to distant nodes are excitatory only – Via long (myelinated) axons of pyramidal neurons

Local and distal connections excitatory inhibitory

Simplified model of minicolumn I: Activation of neurons in a column Other cortical locations Cell Types II III Pyramidal Spiny Stellate Thalamus IV Inhibitory Connections to neighboring columns not shown V VI Subcortical locations

Simplified model of minicolumn II: Inhibition of competitors Other cortical locations Cell Types II III Pyramidal Spiny Stellate Thalamus IV Inhibitory V VI Cells in neighboring columns

Local and distal connections excitatory inhibitory

Findings relating to columns (Mountcastle, Perceptual Neuroscience, 1998) • The column is the fundamental module of perceptual systems – probably also of motor systems • This columnar structure is found in all mammals that have been investigated • The theory is confirmed by detailed studies of visual, auditory, and somatosensory perception in living cat and monkey brains

Functional webs and subwebs • A functional web for a word consists of multiple subwebs • Every such subweb – has a specific function – occupies an area that fits the portion of cortex in which it located • For example, – Phonological recognition in Wernicke’s area – Visual subweb in occipital and lower temporal lobe – Tactile subweb in parietal lobe

Hypothesis 3: Nodal Specificity in functional webs • Every node in a functional web has a specific function • Each node of a subweb also has a specific function within that of the subweb

Support for Nodal Specificity: the paw area of a cat’s cortex Column (node) represents specific location on paw

Support for Nodal Specificity: Columns for orientation of lines (visual cortex) Microelectrode penetrations K. Obermayer & G. G. Blasdell, 1993

Hypothesis 3 a: Adjacency • Nodes of related function are in adjacent locations – More closely related function, more closely adjacent • Examples: – Adjacent locations on cat’s paw represented by adjacent cortical locations – Similar line orientations represented by adjacent cortical locations

Support for Nodal adjacency: the paw area of a cat’s cortex Adjacent column in cortex for adjacent location on paw

Hypothesis 4: Extrapolation to Humans • Hypothesis: The findings about cortical structure and function from experiments on cats, monkeys, and rats can be extrapolated to human cortical structure and function • In fact, this hypothesis is simply assumed to be valid by neuroscientists • Why? We know from neuroanatomy that, locally, – Cortical structure is relatively uniform across mammals – Cortical function is relatively uniform across mammals

Hypothesis 4 a: Linguistic and conceptual structure • The extrapolation can be extended to linguistic and conceptual structures and functions • Why? – Local uniformity of cortical structure and function across all human cortical areas except for primary areas • Primary visual and primary auditory are known to have specialized structures, across mammals • Higher level areas are – locally – highly uniform

Conceptual systems and perceptual systems • Likewise, conceptual systems in humans evidently use the same structures as perceptual systems • Therefore it is not too great a stretch to suppose that experimental findings on the structure of perceptual systems in monkeys can be applied to an understanding of the structure of conceptual systems of human beings • In particular to the structures of conceptual categories

Extrapolation to Language? • Our knowledge of cortical columns comes mostly from studies of perception in cats, monkeys, and rats • Such studies haven’t been done for language – Cats and monkeys don’t have language – That kind of neurosurgical experiment isn’t done on human beings • Are they relevant to language anyway? – Relevant if language uses similar cortical structures – Relevant if linguistic functions are like perceptual functions

Objection • Cats and monkeys don’t have language • Therefore language must have unique properties of its structural representation in the cortex • Answer: Yes, language is different, but – The differences are a consequence not of different (local) structure but differences of connectivity – The network does not have different kinds of structure for different kinds of information • Rather, different connectivities

Summary of the argument • Cortical structure and function, locally, are essentially the same in humans as in cats and monkeys • Moreover, in humans, – The regions that support language have the same structure locally as other cortical regions

Support for the connectionist claim • • • Lines and nodes (i. e. , columns) are approximately the same all over Uniformity of cortical structure – Same kinds of columnar structure – Same kinds of neurons – Same kinds of connections Conclusion: Different areas have different functions because of what they are connected to

Uniformity of cortical function • Claims: – Locally, all cortical processing is the same – The apparent differences of function are consequences of differences in larger-scale connectivity • Conclusion (if the claim is supported): – Understanding language, even at higher levels, is basically a perceptual process

Hypothesis 5: Hierarchy in functional webs • A functional web is hierarchically organized – Bottom levels in primary areas – Lower levels closer to primary areas – Higher (more abstract) levels in • Associative areas – e. g. , angular gyrus • Executive areas – prefrontal • These higher areas are much larger in humans than in other mammals • Corollary: Each subweb is likewise hierarchically organized

Properties of Hierarchy • Each level has fewer nodes than lower levels, more than higher levels – Compare the organization of management of a corporation • Top level has just one node – Compare the “CEO”

Hypothesis 6: Cardinal nodes • Every functional web has a cardinal node – At the top of the entire functional web – Unique to that concept – For example, C/cat/ at “top” of the web for CAT • Corollary: – Each subweb likewise has a cardinal node • At the top level of the subweb • Unique to that subweb • For example, V/cat/ – At the top of the visual subweb

Cardinal nodes of a functional web Some of the cortical structure relating to dog Each node shown here is the cardinal node of a subweb Cardinal node of the whole web T M PP C P PA V Cardinal node of the visual subweb

Support for the cardinal node hypothesis It follows from the hypotheses of nodal specificity and hierarchy – A hierarchy must have a highest level – The node at this level must have a specific function 2. It is needed to account for the arbitrariness of the linguistics sign 3. It is automatically recruited in learning anyway, according to the Hebbian learning hypothesis 1.

Cardinal nodes and the linguistic sign • • Connection of conceptual to phonological representation Consider two possibilities 1. A cardinal node for the concept connected to a cardinal node for the phonological image 2. No cardinal nodes: multiple connections between concept representation and phonological image • supported by Pulvermüller (2002)

Implications of possibility 2 • • No cardinal nodes: multiple connections between concept representation and phonological image I. e. , different parts of meaning connected to different parts of phonological image Consider fork – Maybe /f-/ connects to the shape? – Maybe /-or-/ connects to the feeling of holding a fork in the hand? – Maybe /-k/ connects to the knowledge that fork is related to knife? Conclusion: Possibility 2 must be rejected

Topics • • • A little neuroanatomy Functional webs Nodes and links: Cortical columns Basic operations in the cortex More operations: Learning

Cortical columns do not store symbols • They only – Receive activation – Maintain activation – Inhibit competitors – Transmit activation • Important consequence: – We have linguistic information represented in the cortex without the use of symbols – It’s all in the connectivity • Challenge: – How?

Columnar Functions: Integration and Broadcasting • Integration: A column is activated if it receives enough activation from – Other columns – Thalamus • Can be activated to varying degrees • Can keep activation alive for a period of time • Broadcasting: An activated column transmits activation to other columns – Exitatory – Inhibitory • Learning : adjustment of connection strengths and thresholds

Integration and Broadcasting § Broadcasting • To multiple locations • In parallel § Integration

Integration and Broadcasting Integration Now I’ll tell my friends! Wow, I got activated!

Processing in the cortex • • Parallel (distributed) and serial Hierarchical Bidirectional Variable – Varying strengths of connections – Varying degrees of activation – Variation over time • Adaptability • Learning • Plasticity

Uniformity of structure and function • Locally, – All cognitive and perceptual information, of any kind, is represented as nodes and their interconnections – All cognitive processing, of any kind, consists of broadcasting and integration

Complexity from simplicity • Complexity: what the brain can do • Simplicity: every node is a simple processor – Integration – Broadcasting – Changes in connection strengths and thresholds • Problem: how can such simplicity produce such complexity? • Answer: – Huge quantity of nodes and connections – Parallel distributed processing – Hierarchical organization

Topics • • • A little neuroanatomy Functional webs Nodes and links: Cortical columns Basic operations in the cortex More operations: Learning

Additional operations: Learning • Links get stronger when they are successfully used (Hebbian learning) – Learning consists of strengthening them – Hebb 1948 • Threshold adjustment – When a node is recruited its threshold increases – Otherwise, nodes would be too easily satisfied

Requirements that must be assumed (implied by the Hebbian learning principle) • Links get stronger when they are successfully used (Hebbian learning) – Learning consists of strengthening them • Prerequisites: – Initially, connection strengths are very weak • Term: Latent Links – They must be accompanied by nodes • Term: Latent Nodes – Latent nodes and latent connections must be available for learning anything learnable • The Abundance Hypothesis – Abundant latent links – Abundant latent nodes

Support for the abundance hypothesis • Abundance is a property of biological systems generally – Cf. : Acorns falling from an oak tree – Cf. : A sea tortoise lays thousands of eggs • Only a few will produce viable offspring – Cf. Edelman: “silent synapses” • The great preponderance of cortical synapses are “silent” (i. e. , latent) – Electrical activity sent from a cell body to its axon travels to thousands of axon branches, even though only one or a few of them may lead to downstream activation

Locations of available latent connections • Local – Surrounding area – Horizontal connections (not white matter) • Intermediate – Short-distance fibers in white matter – For example from one gyrus to neighboring gyrus • Long-distance – Long-distance fiber bundles – At ends, considerable branching

Learning – The Basic Process Latent nodes Latent links Dedicated nodes and links

Learning – The Basic Process Latent nodes Let these links get activated

Learning – The Basic Process Latent nodes Then these nodes will get activated

Learning – The Basic Process That will activate these links

Learning – The Basic Process This node gets enough activation to satisfy its threshold

Learning – The Basic Process This node is therefore recruited B A These links now get strengthened and the node’s threshold gets raised

Learning – The Basic Process This node is now dedicated to function AB AB B A

Learning Next time it gets activated it will send activation on these links to next level AB B A

Learning: Deductions from the basic process • Learning is generally bottom-up. • The knowledge structure as learned by the cognitive network is hierarchical — has multiple layers • Hierarchy and proximity: – Logically adjacent levels in a hierarchy can be expected to be locally adjacent • Excitatory connections are predominantly from one layer of a hierarchy to the next • Higher levels will tend to have larger numbers of nodes than lower levels

Learning in cortical networks: A Darwinian process • It works by trial-and-error – Thousands of possibilities available • The abundance hypothesis – Strengthen those few that succeed • “Neural Darwinism” (Edelman) • The abundance hypothesis – Needed to allow flexibility of learning – Abundant latent nodes • Must be present throughout cortex – Abundant latent connections of a node • Every node must have abundant latent links

Learning – Enhanced understanding • This “basic process” is not the full story • The nodes of this depiction: – Are they minicolumns, maxicolumns, or what? – Nodes of the model may be represented by • Minicolumns or • Contiguous bundles of minicolumns – Of different sizes » “maxicolumns”, “hypercolumns”

Findings of Mountcastle: Columns of different sizes for categories and subcategories • Minicolumn – The smallest unit – 70 -110 neurons • Functional column – Variable size – depends on experience – Intermediate between minicolumn and maxicolumn • Maxicolumn (a. k. a. column) – 100 to a few hundred minicolumns • Hypercolumn – Several contiguous maxicolumns

Hypercolums: Modules of maxicolumns A visual area in temporal lobe of a macaque monkey

Perceptual subcategories and columnar subdivisions of larger columns • Nodal specificity applies for maxicolumns as well as for minicolumns • The adjacency hypothesis likewise applies to larger categories and columns – Adjacency applies for adjacent maxicolumns • Subcategories of a category have similar function – Therefore their cardinal nodes should be in adjacent locations

Functional columns • The minicolumns within a maxicolumn respond to a common set of features • Functional columns are intermediate in size between minicolumns and maxicolumns • Different functional columns within a maxicolumn are distinct because of non-shared additional features – Shared within the functional column – Not shared with the rest of the maxicolumn Mountcastle: “The neurons of a [maxi]column have certain sets of static and dynamic properties in common, upon which others that may differ are superimposed. ”

Similarly. . • Neurons of a hypercolumn may have similar response features, upon which others that differ may be superimposed • Result is maxicolumns in the hypercolumn sharing certain basic features while differing with respect to others • Such maxicolumns may be further subdivided into functional columns on the basis of additional features • That is, columnar structure directly maps categories and subcategories (!)

Hypercolumns: Modules of maxicolumns A visual area in the temporal lobe of a macaque monkey Category (hypercolumn) Subcategory (can be further subdivided)

Learning in a system with columns of different sizes • At early learning stage, maybe a whole hypercolumn gets recruited • Later, subdivided into maxicolumns for further distinctions • Still later, functional columns as subcolumns within maxicolumns • New term: Supercolumn – a group of minicolumns of whatever size, hypercolumn, maxicolumn, functional column • Links between supercolumns will thus consist of multiple fibers

Revisit the diagram: Each node of the diagram represents a group of minicolumns – a supercolumn Latent supercolumns Bundles of latent links Dedicated supercolumns and links

Learning – The Basic Process Let these links get activated

Learning – The Basic Process: Refined view Then these supercolumns get activated

Learning – The Basic Process: Refined view That will activate these links

Learning – Refined view This supercolumn gets enough activation to satisfy its threshold

Learning – Refined view This supercolumn is recruited for function AB AB B A

Learning: Refined view Next time it gets activated it will send activation on these links to next level AB B A

Learning Refined view Can get subdivided for finer distinctions AB B A

A further enhancement • Minicolumns within a supercolumn have mutual horizontal excitatory connections • Therefore, some minicolumns can get activated from their neighbors even if they don’t receive activation from outside

Learning: Refined view AB Hypercolumn composed of 3 maxicolumns Can get subdivided for finer distinctions B A

Learning: refined view If, later, C is activated along with A and B, then maxicolumn ABC is recruited for ABC AB B A C

Learning: And the connection from C to ABC is strengthened –it is no longer latent refined view ABC AB B A C

Topics • • • A little neuroanatomy Functional webs Nodes and links: Cortical columns Basic operations in the cortex More operations: Learning

Thank you for your attent. Ion!

References Lamb, Sydney, 1999. Pathways of the Brain: The Neurocognitive Basis of Language. John Benjamins. Mountcastle, Vernon, 1998. Perceptual Neuroscience: The Cerebral Cortex. Harvard University Press. Pulvermüller, Friedemann, 2002. The Neuroscience of Language. Cambridge University Press Internet Sources www. rice. edu/langbrain www. owlnet. rice. edu/ling 411/Class. Notes

For further information. . www. rice. edu/langbrain lamb@rice. edu

The two big problems of neurosyntax How does the brain handle. . 1. Sequencing – ordering of words in a sentence – 2. And ordering of phonemes in a word Categories – Noun, Verb, Preposition, etc. • Subtypes of nouns, verbs, etc. – – What categories are actually used in syntax? How are syntactic categories defined? How represented in the brain? How does a child build up knowledge of such categories based on just his/her ordinary language experience?

First step: accounting for sequence • Important not just for language – Dancing – Eating a meal – Events of the day, of the year, etc. – Etc. , etc. • In language, not just syntax (lexotactics) – Ordering of morphemes in a word • Morphotactics – Order of phonological elements in syllables • Phonotactics

Neurological Structures for Sequence • How is sequencing implemented in neural structure? • For an answer, consider the structure of the cortical column

Lasting activation in minicolumn Cell Types Recurrent axon branches keep activation alive in the column – Until is is turned off by inhibitory cell Pyramidal Spiny Stellate Inhibitory Connections to neighboring columns not shown Subcortical locations

The ‘Wait’ Element 1 W 2 www. ruf. rice. edu/~lngbrain/neel

Lasting activation in minicolumn Cell Types Recurrent axon branches keep activation alive in the column – Until is is turned off by inhibitory cell Pyramidal Spiny Stellate Inhibitory Connections to neighboring columns not shown Subcortical locations

Simple notation for lasting activation Thick border for a node that stays active for a relatively long time Thin border for a node that stays active for a relatively short time N. B. : Nodes are implemented as cortical columns

Recognizing items in sequence This link stays active This node recognizes the sequence ab c a b Node c is satisfied by activation from both a and b If satisfied it sends activation to output connections Node a keeps itself active for a while Suppose that node b is activated after node a Then c will recognize the sequence ab

Example: eat apple (structure for recognition) (Just labels, not part of the structure) eat apple

Example: eat apple, eat banana (structure for recognition) eat apple eat banana

Producing items in sequence Wait element ab a b First a, then b

How does the delay element work? • Remember: each node is implemented as a cortical column – Within the column are 75 -110 neurons • Enough for considerable internal structure • When node ab receives activation, it – Sends activation on down to node a – And to the delay element, which • Waits for activation from clock timer or feedback – Will come in on line labeled ‘f’ in diagram • Upon receiving this signal, sends activation on to node b

Producing items in sequence Delay element ab a f b Carries feedback or clock signal

Producing items in sequence May be within one cortical column ab a f b

Producing items in sequence a different means a b f This would apply for items ‘a’ and ‘b’ in sequence where there is no ‘ab’ to be recognized as a unit. Example: Adjectives of size precede adjectives of color, which precede adjectives of material in the English noun phrase, as in big brown wooden box

Two different network notations Narrow notation • • Nodes represent cortical columns Links represent neural fibers Uni-directional Close to neurological structure eat apple Abstract notation • • Nodes show type of relationship (OR, AND) Easier for representing linguistic relationships Bidirectional Not as close to neurological structure eat apple

Two different network notations Narrow notation b a b f a ab Abstract notation § Downward ab Upward Bidirectional a b b

Constructions have meanings and functions • They are also signs Meaning/Function Form/Expression The sign relationship: a (neural) connection The difference is that for a construction the expression is variable rather than fixed

The transitive verb phrase construction Semantic function Syntactic function CLAUSE DO-TO-SMTHG Transitive verb phrase Variable expression Vt NP

Linked constructions The clause construction CL DO-TO-SMTHG NP Transitive verb phrase Vt NP

Add a few more connections ACTOR-DO CL DO-TO-SMTHG Transitive verb phrase Vt NP

Add other types of predicate THING-DESCR CL BE-SMTHG DO-TO-SMTHG Vi (A rough first approximation) Vt be Adj NP Loc

The other big problem for syntax • Categories • Problems of categories are considered in a separate presentation