Small-Scale Representation: Cortical Columns

1. Ling 411 – 11 Small-Scale Representation:Cortical Columns

2. Perspective – What we know so farSources 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)

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

4. 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) • Writing likewise has three components • Phonological-graphic correspondences • Alternative pathways (cf. ‘phonics’ vs. ‘whole words’) • Angular gyrus • Meaning is all over the cortex • Different areas for different kinds of words • Different areas for the network of a single concept • Grammar depends heavily on frontal lobe • In or near Broca’s area

5. 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

6. 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

7. 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

8. 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.

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

10. Next on the agenda: I. Small-scale representationCortical ColumnsII. Processing at the small scaleOperation of cortical columns

11. 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

12. 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 sub-network • Term: functional web(Pulvermüller 2002)

13. 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

14. 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

15. 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

16. Three views of the gray matter Different stains show different features

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

18. 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

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

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

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

22. 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

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 From M. vanLandingham, unpublished

24. 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

25. 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

26. 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

27. Cortical minicolumns: Quantities • Diameter of minicolumn: 30 microns • Neurons per minicolumn: 70-110 (avg. 75-80) • Minicolumns/mm2 of cortical surface: 1460 • Minicolumns/cm2 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

28. 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

29. 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”

30. 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

31. 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)

32. The cortex as a network of columns • Each column represents a node • The network is thus a large two-dimensional 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

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

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

35. 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)

36. Map of auditory areas in a cat’s cortex A1 AAF – Anterior auditory field A1 – Primary auditory field PAF – Posterior auditory field VPAF – Ventral posterior auditory field

37. More quantities • Number of neurons in cortex: 27.4 billion • Number of minicolumns: 368 million • Neurons per minicolumn: average 75-80 • Neurons beneath 1 mm2 of surface: 113,000 Mountcastle 96

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

39. 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

40. Local and distal connections excitatory inhibitory

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

42. 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)

43. 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

44. 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

45. 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

46. 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

47. 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

48. 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

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

50. 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