Ling 411 11 SmallScale Representation Cortical Columns Perspective

Ling 411 – 11 Small-Scale Representation: Cortical Columns

Perspective – What we know so far Sources of information about the brain § Aphasiology • Research findings during a century-and-a-half § Brain imaging § Neuroanatomy § Other research in neuroscience • E. g. , Mountcastle, Perceptual Neuroscience (1998)

Perspective – What we know so far § Large-scale representation • Subsystems and their • § Locations § Interconnections The Wernicke Principle § Cortical information processing

LARGE-SCALE REPRESENTATION What we know so far – Subsystems I § Phonology is separate from grammar and meaning § Phonology has three components • • • Recognition (Wernicke’s area) Production (Broca’s area) Monitoring (Somatosensory mouth area) • • Alternative pathways (cf. ‘phonics’ vs. ‘whole words’) Angular gyrus • • Different areas for different kinds of words Different areas for the network of a single concept • In or near Broca’s area § Writing likewise has three components § Phonological-graphic correspondences § Meaning is all over the cortex § Grammar depends heavily on frontal lobe

LARGE-SCALE REPRESENTATION What we know so far – Subsystems II § Nouns and verbs are different • In some ways (what ways? ) • How to explain? § Written forms are connected to conceptual information independently of phonological forms § Writing can be accessed from meaning even if speech is impaired § Conceptual information for nouns of different categories may be in different locations

LARGE-SCALE REPRESENTATION What we know so far – Subsystems III: “Lexicon” § The information pertaining to a single lexical item is widely distributed § That is, every lexical item is represented by a large distributed functional web • This web has subwebs for different kinds of information § Phonological (three subwebs) § Multiple subwebs for different facets of the meaning

LARGE-SCALE REPRESENTATION What we know so far – Subsystems IV: The Proximity Principle § Neighboring areas for closely related functions • The closer the function the closer the proximity § Intermediate areas for intermediate functions § Consequences • Members of same category will be in same area • Competitors will be neighbors in the same area

LARGE-SCALE REPRESENTATION What we know so far – Subsystems V: Locations of certain areas § Locations of various kinds of “information” • Primary • • § Visual § Auditory § Tactile § Motor Phonological § Recognition § Production § Monitoring Etc.

PROCESSING What we know so far – Processing § Processing in the cortex is • Distributed • Parallel and serial • Bidirectional

Next on the agenda: I. Small-scale representation Cortical Columns II. Processing at the small scale Operation of cortical columns

SMALL-SCALE REPRESENTATION Findings that are now well established § The brain is a network § Composed, ultimately, of neurons • Neurons are interconnected • § Axons (with branches) § Dendrites (with branches) Activity travels along neural pathways

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 lexeme and every concept is a subnetwork • Term: functional web (Pulvermüller 2002)

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

SMALL-SCALE REPRESENTATION Findings not yet well established § Cortical neurons are clustered in columns • The column rather than the individual neuron as • the main operative unit Each minicolumn acts as a unit § Columns come in different sizes • The smallest: minicolumn – 70 -110 neurons § When column becomes active all its neurons are active § Cortical columns as basic units of • Information representation • Processing

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

Layers of the cortex § I – dendritic tufts of pyramidal neurons • No cell bodies in this layer § II, III – pyramidal neurons of these layers project to other cortical areas § IV – spiny stellate cells, receive activation from thalamus and transmit it to other neurons of same column § V, VI – pyramidal neurons of these layers project to subcortical areas § Various kinds of inhibitory neurons are distributed among the layers

Evidence for columns § Microelectrode penetrations § If perpendicular to cortical surface • Neurons all of same response properties § If not perpendicular • Neurons of different response properties

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 § 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 § About 30– 50 m in diameter § If expanded by a factor of 100, the dimensions would correspond to a tube with diameter of 1/8 inch and length of one foot

Cortical Columns (impressionistic sketch) A graphic model, not an anatomical diagram (There aren’t actually any boundaries between columns. ) From M. van. Landingham, unpublished

Cortical column structure § Minicolumn 30 -50 microns diameter § Recurrent axon collaterals of pyramidal neurons activate other neurons in same column § Inhibitory neurons inhibit neurons of neighboring columns

Columns and neurons § At the small scale. . • Each column is a little network § At a larger scale. . • Each column is a node of the cortical network § The cerebral cortex: • Grey matter — columns of neurons • White matter — inter-column connections

Minicolumns and Maxicolumns § Minicolumn 30 -50 microns diameter § Maxicolumn – a contiguous bundle of minicolums (typically around 100) • 300 -500 microns diameter • Dimensions vary from one part of cortex to • another In some areas at least, they are roughly hexagonal

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) Cf. 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

The cortical column as node § Hypothesis: The nodes of a functional web are cortical columns § 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 • Method: “connecting the dots”

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 § 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)

The cortex as a network of columns § Each column represents a node § The network is thus a large twodimensional array of nodes § Nodes are connected to other nodes both nearby and distant • Connections to nearby nodes are either • excitatory or inhibitory Connections to distant nodes are excitatory § Via long (myelinated) axons of pyramidal neurons

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

Another Quotation “Every cellular study of the auditory cortex in cat and monkey has provided direct evidence for its columnar organization. ” Vernon Mountcastle (1998: 181)

Map of auditory areas in a cat’s cortex A 1 AAF – Anterior auditory field A 1 – Primary auditory field PAF – Posterior auditory field VPAF – Ventral posterior auditory field

More quantities § § Number of neurons in cortex: 27. 4 billion Number of minicolumns: 368 million Neurons per minicolumn: average 75 -80 Neurons beneath 1 mm 2 of surface: 113, 000 Mountcastle 96

Findings relating to columns (Mountcastle, Perceptual Neuroscience, 1998) § The column is the fundamental module of perceptual systems • probably also of motor systems • Each column has a very specific local function § Perceptual functions are very highly localized § 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

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

Lateral inhibition § Inhibitory connections § Extend horizontally to other columns in the vicinity • These columns are natural competitors § Enhances contrast

Inhibitory connections Based on Mountcastle (1998) § Columnar specificity is maintained by pericolumnar inhibition (190) • Activity in one column can suppress that in its immediate neighbors (191) § Inhibitory cells can also inhibit other inhibitory cells (193) § Inhibitory cells can connect to axons of other cells (“axoaxonal connections”) § Large basket cells send myelinated projections as far as 1 -2 mm horizontally (193)

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

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

Justifying extrapolation § Hypothesis: Extrapolation of findings about cortical columns can be extended to • • humans inguistic and conceptual structures § Why? Cortical structure, viewed locally, is • Uniform across mammalian species • Uniform across different cortical regions § Exceptions in primary visual and primary auditory areas § Different cortical regions have different functions • because of differences in connectivity • not because of differences in structure

In particular. . § Cortical structure and function, locally, are essentially the same in humans as in cats and monkeys and rats § Moreover, in humans, • The regions that support language have the same structure locally as other cortical regions

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

Argument for local uniformity of representation § § § Different types of cortical information • • How are they different? Two possibilities 2. They differ in their structural form They differ based on their connections • The “connectionist claim” 1. § Perceptual Conceptual Grammatical Phonological Claim: Possibility #2 is the correct one

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

Perception and Language § § Our knowledge of cortical columns comes mostly from studies of perception in cats, monkeys, and rats Why haven’t such studies been done for language? 1. That kind of neurosurgical experiment isn’t done on human beings 2. Cats and monkeys don’t have language § Are they relevant to language anyway? 1. Relevant if language uses similar cortical structures 2. Relevant if linguistic functions are like perceptual functions

Relevance to Language § These studies of perception are relevant if • Perceptual structure and functions are • basically the same across modalities § Including associative areas (higherlevel) Linguistic comprehension is basically a perceptual process

Linguistic Information in the Cortex § Problem: Linguistic information is usually described symbolically § In the symbolic mode of description, different kinds of linguistic information appear to have different kinds of structure • • Phonology Morphology § Regular and irregular inflections Syntax Semantics § Claim: If the information is viewed as connectional instead of symbolic, it turns out to have a high degree of uniformity

Uniformity of cortical structure § § § Six layers throughout, with similar structure Columns throughout Same neuron types everywhere – pyramidal most frequent, spiny stellate in layer IV, etc. Inhibitory and excitatory connections throughout Same neurotransmitters everywhere • • Excitatory: glutamate Inhibitory: GABA 1. 2. The differences are relatively minor They may be based on experience But: What about the different Brodmann areas?

Structural Uniformity? A closer look § Differences are found at lower levels • Primary sensory areas have specialized • • structures These are called heterotypical areas Properties of columns depend on afferent inflow § More uniformity in higher-level areas • “Homotypical” areas (i. e. , same type) • Relatively uniform structure • Makes them flexible, adaptable • Properties depend on intracortical processing • Different homotypical areas differ not because of their structures but because of their connections

A heterotypical area: Visual motion perception An area in the posterior bank of the superior temporal sulcus of a macaque monkey (“V-5”) 40 0 - 50 0μ Albright et al. 1984

Auditory areas in a cat’s cortex (Heterotypical) A 1 AAF – Anterior auditory field A 1 – Primary auditory field PAF – Posterior auditory field VPAF – Ventral posterior auditory field

Exceptions: Diversity in cortical function § Perception vs. production • Back brain vs. front brain § Sharpness of contrast • Phonology and morphology require sharp contrasts • Conceptual categories have fuzzy definitions § Left vs. right hemisphere • RH may have. . § Larger minicolumns § Less lateral inhibition

Uniformity in LH Associative Areas § The associative areas are homotypical § The structure that subserves language understanding is the same as perceptual structure • Columns of similar structure • With similar kinds of connections § Claim: Understanding language is the same process as perception • To support this claim, must look more closely • at cortical function Subclaim: Locally, all columns function alike

The uniformity of information? § § § Different types of cortical information • • How are they different? Two possibilities 2. They differ in their form of representation They differ based on their connections • The “connectivity claim” 1. § Perceptual Conceptual Grammatical Phonological Claim: Possibility #2 is the correct one

The 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

Testing the claim § Claim: • The apparent differences of function are consequences of differences in larger-scale connectivity § To test, we need to understand cortical function § That means we have to understand the function of the cortical column

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?

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