Cortical Columns Ling 411 – 11

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". David Hubel

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 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.National Academy of SciencesLouisa Gross Horwitz PrizeDavid HubelTorsten WieselNobel Prize in Physiology or MedicineAlbert Lasker Award for Basic Medical ResearchNational Medal of Science

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 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 microns diameter  Maxicolumn – a contiguous bundle of minicolumns (typically around 100) 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: (avg )  Minicolumns/mm 2 of cortical surface: 1460  Minicolumns/cm 2 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 cortical locations Subcortical locations II III IV V VI Connections to neighboring columns not shown Cell Types Pyramidal Spiny Stellate Inhibitory

Simplified model of minicolumn II: Inhibition of competitors Thalamus Other cortical locations II III 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 field A1 – Primary auditory field PAF – Posterior auditory field VPAF – Ventral posterior auditory field A1

More quantities  Neurons per minicolumn: average  Number of neurons in cortex: 27.4 billion  Number of minicolumns: ca. 350 million (27,400,000,000 / (75-80))  Neurons beneath 1 mm 2 of surface: 113,000  Columns beneath 1 mm 2 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 neurons (avg 75-80) Diameter barely more than that of pyramidal cell body (30-50 μ)  Maxicolumn (term used by Mountcastle) Diameter 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 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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