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Ling 411 – 11
Cortical Columns
Perspective – What we know so far: I
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: II
Large-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
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)
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
A1
AAF – Anterior auditory field
A1 – Primary auditory field
PAF – Posterior auditory field
VPAF – Ventral posterior
auditory field
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)
end