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Schedule of Presentations
DelclosPlanum Temp
BanneyerCategories
Ruby TsoWriting
BosleySynesthesia
Rasmussen2nd language
BrownBilingualism
TsaiTones
Tu Apr 13 Th Apr 15 Tu Apr 20 Th Apr 22
Operations in relational networks
Relational networks are dynamic Activation moves along lines and
through nodes Links have varying strengths
•A stronger link carries more activation, other things being equal
All nodes operate on two principles:• Integration
Of incoming activation•Broadcasting
To other nodes
REVIEW
Operation of the Networkin terms of cortical columns
The linguistic system operates as distributed processing of multiple individual components• “Nodes” in an abstract model
• These nodes are implemented as 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
• Broadcasting: An activated column transmits activation to other columns Exitatory – contribution to higher level Inhibitory – dampens competition at same level
Review
Additional operations: Learning
Links get stronger when they are successfully used (Hebbian learning)•Learning consists of strengthening them
•Hebb 1948
Threshold adjustment•When a node is recruited its threshold
increases
•Otherwise, nodes would be too easily satisfied
Requirements that must be assumed(implied by the Hebbian learning
principle) Links get stronger when they are
successfully used (Hebbian learning)•Learning consists of strengthening them
Prerequisites: • Initially, connection strengths are very weak
Term: Latent Links
•They must be accompanied by nodes Term: Latent Nodes
•Latent nodes and latent connections must be available for learning anything learnable The Abundance Hypothesis
•Abundant latent links
•Abundant latent nodes
Support for the abundance hypothesis
Abundance is a property of biological systems generally•Cf.: Acorns falling from an oak tree
•Cf.: A sea tortoise lays thousands of eggs Only a few will produce viable offspring
•Cf. Edelman: “silent synapses” The great preponderance of cortical
synapses are “silent” (i.e., latent)
•Electrical activity sent from a cell body to its axon travels to thousands of axon branches, even though only one or a few of them may lead to downstream activation
Learning – The Basic Process
These links now get strengthened and the node’s threshold gets raised A
B
This node is therefore recruited
Learning: Deductions from the basic process
Learning is generally bottom-up. The knowledge structure as learned by the
cognitive network is hierarchical — has multiple layers
Hierarchy and proximity:• Logically adjacent levels in a hierarchy can be
expected to be locally adjacent
Excitatory connections are predominantly from one layer of a hierarchy to the next
Higher levels will tend to have larger numbers of nodes than lower levels
Learning in cortical networks:A Darwinian process
A trial-and-error process:•Thousands of possibilities available
The abundance hypothesis•Strengthen those few that succeed
“Neural Darwinism” (Edelman) The abundance hypothesis
•Needed to allow flexibility of learning•Abundant latent nodes
Must be present throughout cortex•Abundant latent connections of a node
Every node must have abundant latent links
Learning – Enhanced understanding
This “basic process” is not the full story The nodes of this depiction:
•Are they minicolumns, maxicolumns, or what?
•Most likely, a bundle of contiguous columns
•Perhaps usually a maxicolumn or hypercolumn
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 100 or more contiguous minicolumns
Hypercolumn – up to 1 mm diameter• Can be long and narrow rather than cylindrical• Bundle of contiguous maxicolumns
Functional column• Intermediate between minicolumn and
maxicolumn• A contiguous group of minicolumns
REVIEW
Hypercolums: Modules of maxicolumns
A homotypical area in the temporal lobe of a macaque monkey
REVIEW
Functional columns vis-à-vis minicolumns and maxicolumns
Maxicolumn•About 100 minicolumns
•About 300-500 microns in diameter
Functional column•A group of one to several contiguous
minicolumns within a maxicolumn
•Established during learning
• Initially it might be an entire maxicolumn
Learning in a system with columns of different sizes
At early learning stage, maybe a whole hypercolumn gets recruited
Later, maxicolumns for further distinctions Still later, functional columns as
subcolumns within maxicolumns New term: Supercolumn – a group of
minicolumns of whatever size, hypercolumn, maxicolumn, functional column
Links between supercolumns will thus consist of multiple fibers
Question on cortical columns
E-mail from Kelly Banneyer:
…. I understand that a minicolumn is the smallest unit and maxicolumns are composed of minicolumns and functional columns are intermediate in size while hypercolumns are composed of several maxicolumns. I wonder if there can exist a minicolumn or functional column in the brain that is not part of a larger type of column. For example, I know that there exists hierarchical structure, but is there maybe some concept so exact and unrelated to anything else that a mini/functional column exists that is not part of a maxicolumn?
Functional columns in phonological recognition:A hypothesis
Demisyllable (e.g. /de-/) activates a maxicolumn
Different functional columns within the maxicolumn for syllables with this demisyllable• /ded/, /deb/, /det/, /dek/, /den/, /del/
REVIEW
Functional columns in phonological recognition
A hypothesis
[de-]
A maxicolumn (ca. 100 minicolumns)
Divided into functional columns
(Note that all respond to /de-/)
deb
ded den de- det del
dek
REVIEW
Phonological hypercolumns (a hypothesis)
Maybe we have •Hypercolumn of contiguous maxicolumns for
/e/
•With maxicolumns for /de-/, /be-/, etc.
•Each such maxicolumn subdivided into functional columns for different finals /det/, /ded/, /den/, /deb/, /dem/. /dek/
(N.B.: This is just a hypothesis)•Maybe someday soon we’ll be able to test
with sensitive brain imaging
REVIEW
Adjacent maxicolumns in phonological cortex?
ge- ke-
be- pe-
te- de-A module of contiguous
maxicolumns
Each of these maxicolumns is
divided into functional columns
Note that the entire module responds to [-e-]
Hypercolum
REVIEW
Adjacent maxicolumns in phonological cortex?
ge- ke-
be- pe-
te- de-A module of
six contiguous maxicolumns
The entire module responds to [-e-]
deb
ded den de- det del
dek
The entire maxicolumn responds to [de-]
REVIEW
Latent super-columns
Bundles of latent links
Dedicated super-columns and links
Revisit the diagram: Each node of the diagram represents a group of minicolumns – a supercolumn
Learning:Refined view
Next time it gets activated it will send activation on these links to next level
AB
AB
A further enhancement
Minicolumns within a supercolumn have mutual horizontal excitatory connections
Therefore, some minicolumns can get activated from their neighbors even if they don’t receive activation from outside
Learning: Refined view
Hypercolumn composed of 3 maxicolumns Can get subdivided for finer distinctions
AB
AB
Learning: refined viewIf, later, C is activated along with A and B, then maxicolumn ABC is recruited for ABC
AB
AB
C
ABC
Learning: refined view
And the connection from C to ABC is strengthened –it is no longer latent
AB
AB
C
ABC
Learning phonological distinctions:A hypothesis
ge- ke-
be- pe-
te- de-1. In learning,
this hypercolumn
gets established
first, responding to
[-e-]2. It gets subdivided into maxicolumns for demisyllables
deb
ded den de- det del
dek
3. The maxicolumn gets divided into functional columns
Remaining problems – lateral inhibition
When a hypercolumn is first recruited, no lateral inhibition among its internal subdivisions
Later, when finer distinctions are learned, they get reinforced by lateral inhibition
Problem: How does this work?
Hypothesis applied to conceptual categories
A whole maxicolumn gets activated for the category•Example: DRINKING-VESSEL
Different functional columns within the maxicolumn for subcategories• CUP, GLASS, etc.
Adjacent maxicolumns for categories related to DRINKING VESSEL
• BOWL, JAR, etc.
REVIEW
Locating Functions:The Proximity Principle
Related functions tend to be in close proximity• If very closely related, they tend to be
adjacent
Areas which integrate properties of different subsystems (e.g., different sensory modalities) tend to be in locations intermediate between those subsystems
Consequences of the Proximity Principle
Nodes in close competition will tend to be neighbors•And their mutual competition is preordained
even though the properties they are destined to integrate will only be established through the learning process
Therefore, inhibitory connections should exist predominantly among nodes of the same hierarchical level•The presence of their mutual inhibitory
connections could be genetically specified
Learning and the Proximity Principle
Start with the observation:• Related areas tend to be adjacent to each
other Primary auditory and Wernicke’s area V1 and V2, etc. Wernicke’s area and lexical-conceptual
information – angular gyrus, SMG, MTG
Thus we have the ‘proximity principle’ Question: Why – How to explain?
Two aspects of the proximity principle
1. A node that integrates a combination of properties of different subsystems can be expected to lie in a location intermediate between those subsystems
2. A node that integrates a combination of properties of the same subsystem should be within the same subsystem, and maximally close to the properties it integrates
How to Explain the Proximity Principle?
Factors responsible for observations of proximity in cortical structure1. Economic necessity2. Genetic factors3. Experience – provides details of localization
within the limits imposed by genetic factors
Proximity: Economic necessity
Question: Could a given column be connected to any other column anywhere in the cortex?
That would require a huge number of available latent connections
Way more than are present Hence there are strict limits on
intercolumn connectivity Therefore, proximity is necessary just for
economy of representation
Limits on intercolumn connectivity
Number of cortical minicolumns: • If 27 billion neurons in entire cortex
• If avg. 77 neurons per minicolumn
•Then 350 million minicolumns in the cortex
Extent of available latent connections to other columns•Perhaps 35,000 to 350,000
•Do the math.. A given column has available latent
connections to between 1/1000 and 1/10000 of the other columns in the cortex
Locations of available latent connections
Local •Surrounding area•Horizontal connections (grey matter)
Intermediate•Short-distance fibers in white matter•For example from one gyrus to neighboring
gyrus Long-distance
•Long-distance fiber bundles•At ends, considerable branching
The role of long-distance fibers
Arcuate fasciculus•Genetically determined
•Limits location of phonological recognition area
Interhemispheric fibers•Also genetically determined
•Wernicke’s area – RH homolog of W’s area
•Broca’s area – RH homolog of B’s area
•Etc.
Two Factors in Localization
Genetic factors determine general area for a particular type of knowledge
Within this general area the learning-based proximity factors select a more narrowly defined location
Thus the exact localization depends on experience of the individual
When part of the system is damaged, learning-based factors can take over and result in an abnormal location for a function – plasticity
Genetically determined proximity
Genetically-determined proximity would have developed over a long period of evolution• Many features are shared with other mammals
This process could be called ‘evolutionary learning’
According to standard evolutionary theory..• A process of trial-and-error:
Trial • Produce varieties
Error: • Most varieties will not survive/reproduce
• The others – the best among them – are selected
Other genetic factors supplement proximity• Long-distance fiber bundles
Innate factors relating to primary areas
Location •Genetically determined locations
But there are exceptions •Malformation
•Damage
Structure •Genetically determined structures adapted to
sensory modality (they have to be where they are) Heterotypical structures
•Found in primary areas» Primary visual» Primary auditory
A Heterotypical (i.e., genetically built-in) structure
Visual motion perception
An area in the posterior bank of the superior temporal sulcus of a macaque monkey (“V-5”)
A heterotpical area
Albright et al. 1984
400-500 μ
REVIEW
A Heterotypical structure:Auditory areas in a cat’s cortex
AAF – Anterior auditory fieldA1 – Primary auditory field PAF – Posterior auditory fieldVPAF – Ventral posterior auditory field
A1
REVIEW
Innate factors relating to localization
The primary areas Long-distance fiber bundles
• Interhemispheric – via corpus callosum
•Longitudinal – from front to back Arcuate fasciculus is part of the superior
longitudinal fasciculus
They allow for exceptions to proximity•Areas closely related yet not neighboring
Applying the proximity principle
For both types (genetic and experience-based) we can make predictions of where various functions are most likely to be located, based on the proximity principle•Broca’s area near the inferior precentral gyrus
•Wernicke’s area near the primary auditory area
Such predictions are possible even in cases where we don’t know whether genetics or learning is responsible• maybe both
Implications of the proximity principle
System level•Functionally related subsystems will tend to
be close to one another
•Neighboring subsystems will probably have related functions
Cortical column level•Nodes for similar functions should be
physically close to one another
•Nodes that are physically close to one another probably have similar functions Therefore..
•Neighboring nodes are likely to be competitors
•They need to have mutually inhibitory connections
Deriving location from proximity hypothesis
The cortex has to provide for “decoding” speech input
Speech input enters the cortex in the primary auditory area
Results of the “decoding” (recognition of syllables etc.) are represented in Wernicke’s area
Why is Wernicke’s area where it is?
Speech Recognition in the Left Hemisphere
Primary AuditoryArea
PhonologicalRecognition
PhonologicalProduction
Wernicke’s Area
Exercise: Location of Wernicke’s area
Why is phonological recognition in the posterior superior temporal gyrus?•Alternatives to consider:
Anterior to primary auditory cortex•Advantage: would be close to phonological
production
Inferior to primary auditory cortex
(There are two reasons)
Answer: Location of Wernicke’s area
Wernicke’s area pretty much has to be where it is to take advantage of the arcuate fasciculus
The location of W.’s area makes it close to angular gyrus, likely area for noun lemmas (morphemes and complex morphemes)
Also, close to SMG, presumed area for phonological monitoring• (Why?
Because it is adjacent to primary somatosensory area)
More exercises
Explaining likely locations of morphemes•verb morphemes in the frontal lobe
•noun morphemes in the angular gyrus and/or middle temporal gyrus
The dorsal (where) pathway of visual perception
Experience-based proximity
Can be expected to be operative •more at higher (more abstract) levels, less
at lower levels
• for areas of knowledge that have developed too recently for evolution to have played a role Reading Writing Higher mathematics Physics, computer technology, etc.
Innate features that support language
Columnar structure Coding of frequencies in Heschl’s gyrus Arcuate fasciculus Interhemispheric connections (via corpus
callosum) – e.g., connect Wernicke’s area with RH homolog
Spread of myelination from primary areas to successively higher levels
Left-hemisphere dominance for grammar etc.