cortical columns ling 411 – 11. perspective – what we know so far: i sources of information...
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Cortical Columns
Ling 411 – 11
Perspective – What we know so far: ISources 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: IILarge-Scale Representation of Information
Subsystems and their locations• Known for some important ones:
Primary areas Phonological recognition Phonological production Etc.
Interconnections among subsystems E.g., arcuate fasciculus
The Wernicke Principle
What we know so far III: Connectionism
“Wernicke’s Principle” • Each local area does a small job• Large jobs are done by multiple small areas
working together, by means of interconnecting fiber bundles
The basic principle of connectionism• Connectivity Rules• Consequence: Distributed processing
Anatomical support for connectionism
The brain is a network Composed, ultimately, of neurons
• Neurons are interconnected Axons (with branches) Dendrites (with branches)
• Activity travels along neural pathways
Consequences of basic principle of connectionism
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)
It would appear to follow that every lexeme and every concept is a sub-network (next slide)
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
Connectionism and lexicon
The information pertaining to a single lexical item must be widely distributed
That is, every lexical item is represented by a large distributed network• With subnetworks for different kinds
of information Phonological (three subwebs) Multiple subwebs for different
facets of the meaning
What we know so far IV: Determinants of location of subsystems
Genetically determined primary areas• Motor – frontal lobe• Perceptual – posterior cortex
Somatic – parietal Visual – occipital Auditory – temporal
Hierarchy Plasticity
• Consequence: Higher-level areas not in genetically determined areas
Locations of 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
Locations of 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
Locations of Subsystems III:
Locations of various kinds of “information”• Primary
Visual Auditory Tactile Motor
• Phonological Recognition Production Monitoring
• Etc.
What we know so far V: 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
Vernon W. Mountcastle
From Wikipedia: He discovered and characterized the columnar organization of the cerebral cortex in the 1950s. This discovery was a turning point in investigations of the cerebral cortex, as nearly all cortical studies of sensory function after Mountcastle's 1957 paper on the somatosensory cortex used columnar organization as their basis. Indeed, David Hubel in his Nobel Prize acceptance speech said Mountcastle's "discovery of columns in the somatosensory cortex was surely the single most important contribution to the understanding of cerebral cortex since Cajal".
Vernon Mountcastle
More from Wikipedia: Mountcastle's devotion to studies of single unit neural coding evolved through his leadership in the Bard Labs of Neurophysiology at the Johns Hopkins UniversitySchool of Medicine, which was for many years the only institute in the world devoted to this sub-field, and its work is continued today in the Krieger Mind/Brain Institute. He is University Professor Emeritus of Neuroscience Johns Hopkins University.
Professor Mountcastle was elected to the National Academy of Sciences in 1966.In 1978, he was awarded the Louisa Gross Horwitz Prize from Columbia University together with David Hubel and Torsten Wiesel, who both received the Nobel Prize in Physiology or Medicine in 1981. In 1983, he was awarded the Albert Lasker Award for Basic Medical Research. He also received the United States National Medal of Science in 1986.
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
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• Hence 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
Three views of the gray matter
Different stains show different features
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
Layers of the Cortex
From top to bottom, about 3 mm
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 minicolumns
(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/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
Simplified model of minicolumn I:Activation of neurons in a column
Thalamus
Other corticallocations
Subcorticallocations
IIIII
IV
VVI
Connections to neighboring columns not shown
Cell Types
Pyramidal
Spiny Stellate
Inhibitory
Simplified model of minicolumn II:Inhibition of competitors
Thalamus
Other corticallocations
IIIII
IV
V
VI
Cells in neighboring columns
Cell Types
Pyramidal
Spiny Stellate
Inhibitory
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)
Deductions from findings about cortical columns
Property I: Cortical topography Property II: Intra-column uniformity of function Property III: Nodal specificity Property IV: Adjacency Property V: Extension of II-IV to larger columns
Deductions from findings about cortical columns
Property I: Cortical topography Property II: Intra-column uniformity of function Property III: Nodal specificity Property IV: Adjacency Property V: Extension of II-IV to larger columns
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
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
Deductions from findings about cortical columns
Property I: Cortical topography Property II: Intra-column uniformity of function Property III: Nodal specificity Property IV: Adjacency Property V: Extension of II-IV to larger columns
Uniformity of function within the cortical column
All neurons of a column have the same response properties
It follows that: The nodes of the cortical information network are cortical columns
The properties of the cortical column are approximately those described by Vernon Mountcastle • Mountcastle, Perceptual Neuroscience, 1998
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)
Deductions from findings about cortical columns
Property I: Cortical topography Property II: Intra-column uniformity of function Property III: Nodal specificity Property IV: Adjacency Property V: Extension of II-IV to larger columns
Nodal specificity
Property III: Every column (hence every node) has a specific function
Known from the experiments for all areas that have been tested
Hypothesis: nodal specificity applies throughout the cortex• Including higher-level areas• The cortex is relatively uniform in structure• Therefore, specificity should apply
generally to cortical columns• This claim needs to be investigated (soon)
Microelectrode penetrations in the paw area of a cat’s cortex
Map of auditory areas in a cat’s cortex
AAF – Anterior auditory fieldA1 – Primary auditory field PAF – Posterior auditory fieldVPAF – Ventral posterior auditory field
A1
More quantities
Neurons per minicolumn: average 75-80 Number of neurons in cortex: 27.4 billion Number of minicolumns: ca. 350 million
(27,400,000,000 / (75-80)) Neurons beneath 1 mm2 of surface: 113,000 Columns beneath 1 mm2 of surface:
14,000 – 15,000 (113,000 / (75-80))
Mountcastle 96
Features of the cortical (mini)column
75 to 110 neurons 70% of the neurons are pyramidal The rest include
• Other excitatory neurons• Several different kinds of inhibitory neurons
Findings summarized by Vernon Mountcastle, Perceptual Neuroscience (1998)
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
Deductions from findings about cortical columns
Property I: Cortical topography Property II: Intra-column uniformity of function Property III: Nodal specificity Property IV: Adjacency Property V: Extension of II-IV to larger columns
Nodal Adjacency
Property IV: Nodes that are anatomically adjacent have closely related functions
This property extends beyond immediate neighbors• Adjacent nodes are functionally very similar• Nodes that are nearby but not adjacent have similar
function• Degrees of topographic closeness correspond to
degrees of functional similarity Consequence: A cortical area forms a functional map
Deductions from findings about cortical columns
Property I: Cortical topography Property II: Intra-column uniformity of function Property III: Nodal specificity Property IV: Adjacency Property V: Extension of properties II-IV to larger columns
Columns of different sizes
Minicolumn• Basic anatomically described unit• 70-110 neurons (avg 75-80)• Diameter barely more than that of pyramidal cell body (30-50 μ)
Maxicolumn (term used by Mountcastle)• Diameter 300-500 μ• Bundle of about 100 continuous minicolumns
Hypercolumn – up to 1 mm diameter• Can be long and narrow rather than cylindrical
Functional column• Intermediate between minicolumn and maxicolumn• A contiguous group of minicolumns
Functional Columns
Intermediate in size between minicolumn and maxicolumn
Hypothesized functional unit whose size is determined by experience/learning
A maxicolumn consists of multiple functional columns A functional column consists of multiple minicolumns Functional column may be further subdivided with
learning of finer distinctions
Columns of different sizes In order according to size
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
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
Hypercolums: Modules of maxicolumns
A visual area in the temporal lobe of a macaque monkey
Category (hypercolumn)
Subcategory(can be further subdivided)
The Proximity Principle
Closely related cortical functions tend to be in adjacent areas• Broca’s area and primary motor cortex• Wernicke’s area and primary auditory area• Angular gyrus and Wernicke’s area• Brodmann area 37 and Wernicke’s area
A function that is intermediate between two other functions tends to be in an intermediate location• Wernicke’s area – between primary auditory
area and Angular gyrus
Deductions from findings about cortical columns
Property I: Cortical topography Property II: Intra-column uniformity of function Property III: Nodal specificity Property IV: Adjacency Property V: Extension of II-IV to larger columns Property VI: Competition
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)
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