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Ling 411 – 12
I - Cortical Column Functions
II - Functional Webs
Uniformity of cortical function
 If cortical function is uniform across
mammals and across different cortical
areas, then the findings presented by
Mountcastle can be extended to language
 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
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
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?
Why the usual approach won’t work
 Let us suppose that words are stored in
some kind of symbolic form
 What form?
 If written, there has to be..
• something in there that can read them
• something in there that can write them
• something in there that can move them around,
•
from one place to another
something in there to compare them with forms
entering the brain as it hears someone speaking
– otherwise, how can an incoming word be
recognized?
Why the usual approach won’t work (cont’d)
 If not written, then represented in some
other medium
 Doesn’t solve the problem
 You still need whatever kind of sensory
detectors can sense the symbols in
whatever medium you choose
 Plus means of performing all those other
operations
Compare imagery
 Visual images
• Little pictures?
• If so, what is in there to see them?
 Auditory images
• Little sounds vibrating in the brain?
• If so, what is in there to hear them?
 There has to be another way!
There must be another way
 Visual imagery (e.g. of your grandmother)
• Reactivation of some of the same nodes and
connections that operate when actually seeing her
 Auditory imagery (e.g. of a tune)
• Reactivation of some of the same nodes and
connections that operate in actually hearing it
Another way, for language
 A syllable
• Activation of the nodes and connections needed
to recognize or produce it
 A word
• Activation of the nodes and connections needed
to recognize it
 A syntactic construction
• Activation of the nodes and connections needed
to recognize or produce it
Quotation
The postulation of objects as something
different from the terms of relationships
is a superfluous axiom and consequently a
metaphysical hypothesis from which
linguistic science will have to be freed.
Louis Hjelmslev
Prolegomena to a Theory of Language
(1943: 61)
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
•
•
Exitatory
Inhibitory
 Can be activated to varying degrees
 Can keep activation alive for a period of time
 Broadcasting: An activated column transmits
activation to other columns
 Learning : adjustment of connection strengths
and thresholds
Integration and Broadcasting
 Broadcasting
• To multiple locations
• In parallel
 Integration
Integration and Broadcasting
Broadcasting
Integration
Now I’ll tell my friends!
Wow, I got activated!
What matters is not ‘what’ but ‘where’
 What distinguishes one kind of information
from another is what it is connected to
 Lines and nodes are approximately the
same all over
 Hence, uniformity of cortical structure
• Same kinds of columnar structure
• Same kinds of neurons
• Same kinds of connections
 Different areas have different functions
because of what they are connected to
Operations in relational networks
 Activation moves along lines and
through nodes
• Integration
• Broadcasting
 Connection strengths are variable
• A connection becomes stronger with
•
repeated successful use
A stronger connection can carry greater
activation
What about the rest of language?
 Words and their meanings
 Syntax and morphology
 Conceptual relationships
Sequence
 In language, sequence is very important
• Word order
• Order of phonological elements in syllables
• Etc.
 Also important in many non-linguistic areas
• Dancing
• Eating a meal
 Can cortical columns handle sequences?
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
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
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
Demisyllables in recognizing stops
 Consider stop consonants, e.g. t, d
 At the time of closure
• For voiceless stops there is no sound to hear
• For voiced stops, very little sound
 The stops are identified by transitions
• To following vowel
• From preceding vowel
Demisyllables [di, de, da, du]
F1 and F2
For [a]
It is unlikely that [d] is represented as a unit in perception
Recognizing a syllable and its demisyllables
dim
Cardinal node for dim
Just
labels
Functional subweb for dim
di-
-im
Auditory features of [di-]
Auditory features of [-im]
Another syllable and its demisyllables
bil
Cardinal node for bill
Subweb for bill
bi-
-il
Multiple connections of -il
bil
bi-
hil
-il
kil
Bill
hill
mill
kill
etc.
One and the same
/-il/ in all of them
Multiple connections of -il
bil
bi-
hil
-il
kil
Bill
hill
mill
kill
etc.
Similarly for multiple
connections of bibit, bib, bid, etc.
Multiple connections of -il
bil
bi-
hil
kil
-il
To lower level nodes in the subwebs,
for phonological features
Bill
hill
mill
kill
etc.
Syntactic Recognition – same principle
This link
stays
active
This node
recognizes the
sequence ab
c
a
b
Let node a represent Noun Phrases (Subject) and
let b represent Predicates (Verb Phrases etc.)
Then c represents Clauses: the sequence ab
Syntactic Recognition: higher-level perception
This link
stays
active
This node
recognizes the
sequence ab
c
a
b
The whole process is one of recognition, just as at
lower levels (e.g., phonological recognition)
Same structures, different connections
Conclusion: All of linguistic
structure is relational
 The whole of linguistic structure is a
connectionist system
 Good thing, since that is exactly the
kind of system that the cortex is
built to represent and to operate with
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
Review
Operation of the Network
 The linguistic system operates as distributed
processing of multiple individual components –
cortical columns
 Columnar Functions
•
•
Integration: A column is activated if it receives
enough activation from other columns
 Can be activated to varying degrees
 Can keep activation alive for a period of time
An activated column transmits activation to other
columns
 Exitatory – contribution to higher level
 Inhibitory – dampens competition at same level
 Columns do not store symbols!
Neuronal Structure and Function
(Pulverműller 2002, Chapter 2)
Neuronal Structure and Function:
The Cortex as a Network
 Pulvermüller (2002):
• The brain is not like a computer
“…any hardware computer configuration can
realize almost any computer program or piece
of software.”
“… it may be that the neuronal structures
themselves teach us about aspects of the
computational processes that are laid down in
these structures.”
 Connectivity as key property
The cortex operates
by means of connections
 Grey matter
• Cortical columns
• Horizontal connections among
neighboring columns
 White matter
• Connections between distant columns
Computers and Brains:
Different Structures, Different Skills
 Computers
• Exact, literal
• Rapid calculation
• Rapid sorting
• Rapid searching
• Faultless memory
• Do what they are told
• Predictable
 Brains
• Flexible, fault tolerant
• Slow processing
• Association
• Intuition
• Adaptability, plasticity
• Self-driven activity
• Unpredictable
• Self-driven learning
What brains but not computers can do
 Acquire information to varying degrees
•
•
“Entrenchment”
How does it work?
 Variable connection strength
 Connections get stronger with repeated use
 Perform at varying skill levels
•
•
•
Degrees of alertness, attentiveness
Variation in reaction time
Mechanisms:
 Global neurotransmitters
 Variation in blood flow
 Variation in available nutrients
 Presence or absence of fatigue
 Presence or absence of intoxication
Neuronal Structure and Function:
Connectivity
 White matter: it’s all connections
• Far more voluminous than gray matter
• Cortico-cortical connections
•
 The fibers are axons of pyramidal neurons
 They are all excitatory
White since the fibers are coated with myelin
 Myelin: glial cells
 There are also grey matter connections
• Unmyelinated
• Local
• Horizontal, through gray matter
• Excitatory and inhibitory
Pyramidal neurons and their connections
 Connecting fibers
• Dendrites (input): length 2mm or less
• Axons (output): length up to 10 cm
 Synapses
• Afferent synapses: up to 50,000
•
 From distant and nearby sources
• Distant – to apical dendrite
• Local – to basal dendrites or cell body
Efferent synapses: up to 50,000
 On distant and nearby destinations
• Distant – main axon, through white matter
• Local – collateral axons, through gray matter
Proportion of pyramidal cells in the cortex
 Abeles (1991: 52) says 70%
 Mountcastle says 70% - 80% (1998: 54)
• Based on information from Feldman (1984)
 Pulvermüller (2002: 13) says 85%
• Based on information from Braitenburg & Schüz
(1998)
 Some difference comes from how spiny
stellate cells are counted
• Pyramidal or not?
 No discrete boundary between these
categories
Connecting
fibers of
pyramidal
neurons
Apical dendrite
Basal dendrites
Axon
Interconnections of pyramidal neurons
Input from
distant cells
Input from
neighboring
columns
Output to
distant cells
Neuronal Structure and Function:
Connectivity
 Synapses of a typical pyramidal neuron:
• Incoming (afferent) – 50,000 (5 x 104)
• Outgoing (efferent) – 50,000
 Number of synapses in cortex:
• 28 billion neurons (Mountcastle’s estimate)
 i.e., 28 x 109
 Synapses in the cortex (do the math)
• 5 x 104 x 28 x 109 = 140 x 1013 = 1.4 x 1015
• Approximately 1,400,000,000,000,000
• i.e., over 1 quadrillion
Cortical connectivity properties
 Probability of adjacent areas being
connected: >70% (Pulvermüller p. 17)
• But if we count by minicolumns instead of
cells the figure is probably higher, maybe
close to 100%
 Probability of distant areas being
connected: 15-30% (p. 17)
• Distant areas: at least one intervening area
• In Macaque monkey, most areas have links
to 10 or more other areas within same
hemisphere
More cortical connectivity properties
 Most areas are connected to
homotopic area of opposite hemisphere
 Most connections between areas are
reciprocal
 Primary areas not directly connected
to one another, except for motorsomatosensory
• Connections under central sulcus
Degrees of separation
between cortical neurons or columns
 For neurons of neighboring columns: 1
 For distant neurons in same hemisphere
• Range: 1 to about 5 or 6 (estimate)
• Mostly 1, 2, or 3, especially if functionally
•
closely related
Average about 3 (estimate)
 For opposite hemisphere
• Add 1 to figures for same hemisphere
 Probably, for any two columns anywhere in
the cortex, whether functionally related or
not, fewer than 6 degrees of separation
Neural processes for learning
 Basic principle: when a connection is
successfully used, it becomes stronger
• Successfully used if another connection to
same node is simultaneously active
 Mechanisms of strengthening
• Biochemical changes at synapses
• Growth of dendritic spines
• Formation of new synapses
 Weakening: when neurons fire
independently of each other their
mutual connections (if any) weaken
Neural processes for learning
C
Synapses here
get strengthened
A
B
If connections AC and BC are active at the same
time, and if their joint activation is strong enough
to activate C, they both get strengthened
(adapted from Hebb)
Pulvermüller’s functional webs
 For example, a web for the concept CAT
 Pulvermüller:
• A significant portion of the web’s neurons
are active whenever the cat concept is being
processed
• The function of the web depends on the
intactness of its member neurons
• If neurons in the functional web are strongly
linked, they should show similar response
properties in neurophysiological experiments
(2002:26)
The neural basis of cognition
 Earlier proposals (p. 23)
•
•
Individual neurons (Barlow 1972)
 Individual neurons too noisy and unreliable
 Would require more information processing
capacity than one neuron has
Mass activity and interference patterns in the
entire cortex (Lashley 1950)
 Better alternative:
•
Functional webs of neurons (Pulvermüller)
•
•
Functional webs of cortical columns
(not mentioned by Pulvermüller)
 Even better
Pulvermüller’s functional webs
 A large set of neurons that
• Are strongly connected to each other
• Are distributed over a set of cortical areas
• Work together as a functional unit
• Are functionally interdependent so that each
is necessary for the optimal functioning of
the web (p.24)
Hypothesis I: Functional Webs


A word is represented as a functional web
Spread over a wide area of cortex
•
•
•
Includes perceptual information
 Relating to the meaning
As well as specifically conceptual information
• For nominal concepts, mainly in
• Angular gyrus
• (?) For some, middle temporal gyrus
• (?) For some, supramarginal gyrus
As well as phonological information
 Temporal, parietal, frontal
Example: The meaning of 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
The Wernicke-Lichtheim concept node
(1885)
Where?
The “C” Node
 Not just in one place
• Conceptual information for a single word is
•
widely distributed
Conceptual information is in different areas for
different kinds of concepts
 The second of these points and probably
also the first were already recognized by
Wernicke
 But..
• There may be a single “C” node anyway as
cardinal node of a distributed network
“C” node as cardinal node of a web
For example, FORK
T
C
M
V
Labels for Properties:
C – Conceptual
M – Motor
T – Tactile
V - Visual
Each node in this diagram
represents the cardinal node
of a subweb of properties
Some connections of the “C” node for FORK
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
Zooming in on the “V” Node..
V
FORK
A network of
visual features
Etc. etc.
(many layers)
Add phonological recognition node
For example, FORK
C
T
M
P
V
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
Arcuate fasciculus
Articulatory structures (in Broca’s
area) that control articulation of
the spoken form [fork]
Labels for Properties:
C – Conceptual
M – Motor
P – Phonological image
PA – Primary Auditory
PP – Phonological Production
T – Tactile
V – Visual
Some of the cortical structure relating to fork
T
M
PP
C
P
PA
V
Functional web of a simple lexeme: fork
Meaning
Phonological
form
T
M
PP
C
P
PA
V
Link
betw
form and
meaning
Part of the functional web for FORK
(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
Pulvermüller’s line of reasoning
1.
2.
3.
“If neurons in the functional web are strongly
linked, they should show similar response properties
in neurophysiological experiments.
“If the neurons of the functional web are necessary
for the optimal processing of the represented
entity, lesion of a significant portion of the network
neurons must impair the processing of this entity.
This should be largely independent of where in the
network the lesion occurs.
“Therefore, if the functional web is distributed
over distant cortical areas, for instance, certain
frontal and temporal areas, neurons in both areas
should (i) share specific response features and (ii)
show these response features only if the respective
other area is intact.”
(2002: 26, see also 27)
end