Intelligence and Patterns

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Dec 11, 2013 (3 years and 11 months ago)

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1


The Brain is a Pattern matching Machine

Intelligence and Patterns

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2


His book is free on line for download
http://hubel.med.harvard.edu/index.html




PREFACE
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1

INTRODUCTION

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2

IMPULSES, SYNAPSES, AND CIRCUITS

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3

THE EYE

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4

THE PRIMARY VISUAL CORTEX
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5

THE ARCHITECTURE OF THE VISUAL CORTEX

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6

MAGNIFICATION AND MODULES

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7

THE CORPUS CALLOSUM AND STEREOPSIS

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8

COLOR VISION
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9

DEPRIVATION AND DEVELOPMENT

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10
PRESENT AND FUTURE

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FURTHER READING

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SOURCES OF ILLUSTRATIONS



INDEX


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3


Reverse
-
Engineering the Brain

At MIT, neuroscience and artificial intelligence are beginning to intersect.

www.technologyreview.com/read_article.aspx?id=17111


While AI's progress has been slower than expected,
neuro
-
science has
gotten much more sophisticated in its understanding of how the brain
works. Nowhere is this more obvious than in the 37 labs of MIT's
BCS

Complex. Groups here are charting the neural pathways of most of the
higher cognitive functions (and their disorders), including learning,
memory, the organization of complex sequential behaviors, the formation
and storage of habits, mental imagery, number management and control,
goal definition and planning, the processing of concepts and beliefs, and
the ability to understand what others are thinking.

One breakthrough example: Biological vision solves problems in several different ways.
One, according to
Poggio's

group, is to organize parallel processing around two simple
operations and then alternate these operations in an ordered way through layers of neurons.
Layer A might filter the basic inputs from the optic nerve; layer B would integrate the results
from many cells in layer A; C would filter the inputs from B; D would integrate the results
from C; and so on, perhaps a dozen times. As a signal rises through the layers, the outputs
of the parallelized processors gradually combine, identity emerges, and noise falls away.

Some of their assumptions turned out to predict real features, such as the presence of cells
(call them OR cells) that pick the strongest or most consistent signal out of a group of
inputs and copy it to their own output fibers. (Imagine a group of three neurons, A, B, and C,
all sending signals to OR neuron X. If those signals were at strength levels 1, 2, and 3
respectively, X would suppress A and B and copy C's signal to its output. If the strengths
had been 3, 2, and 1, it would have copied A's signal and suppressed those of B and C.)

When human subjects and
Serre

and
Poggio's

immediate
-
recognition

program took the
animal presence/absence test, the computer did as well as the humans
--

and better than the
best machine vision programs available. (Indeed, it got the right answer 82 percent of the
time, while the humans averaged just 80 percent.)


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4


Processing without attention and
consciousness

Rapid visual categorization

Visual input can be classified very
rapidly. As famously demonstrated by
Thorpe and colleagues (Thorpe et al.,
1996; Kirchner and Thorpe, 2006)
around 120 msec following image
onset, some brain processes begin to
respond differentially to images
containing one of more animals from
pictures than contain none. At this
speed, it is no surprise that subjects
often respond without having
consciously seen the image;
consciousness for the image may
come later or not at all.

Dual
-
task and dual
-
presentation
paradigms support the idea that such
discriminations can occur in the near
-
absence of focal, spatial attention
implying that purely feed
-
forward
networks can support complex visual
decision
-
making in the absence of
both attention and consciousness.
Indeed, this has now been formally
shown in the context of a purely feed
-
forward computational model of the
primate’s ventral visual system (Serre
et al., 2007).


www.technologyreview.com/printer_friendly_article.aspx?id=17111

Reverse

Engineering

the Brain

www.scholarpedia.org/article/Attention_and_consciousness/
processing_without_attention_and_consciousness

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5


One Example of Chunking

Explaining
Rapid Categorization
.

Thomas Serre, Aude Oliva, Tomaso Poggio.

http://cbcl.mit.edu/seminars
-
workshops/workshops/serre
-
slides.pdf

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6


The organization of visual cortex based on a core of knowledge that has been accumulated
over the past 30 years. The figure is modified from[Oramand Perrett, 1994]mostly to include
the likely involvement of prefrontal cortex during recognition tasks by setting task
-
specific
circuits to read
-
out shape information from IT [Scalaidhe et al., 1999; Freedman et al., 2002,
2003; Hung et al., 2005].

Thomas Serre (2006), Learning a Dictionary of Shape
-
Components in Visual Cortex: Comparison with
Neurons, Humans and Machines, Ph.D. dissertation, Department of Brain and Cognitive Sciences,
Massachusetts Institute of Technology, April, http://cbcl.mit.edu/publications/ps/MIT
-
CSAIL
-
TR
-
2006
-
028.pdf

Chunking Hierarchy


The results by
Logothetis
et al. are in
agreement with a
general computational
theory [
Poggio, 2000]
suggesting that a
variety of visual object

recognition tasks
(involving the
categorization of
objects and faces at
different levels) can

be performed based on
a linear combination of
a few units tuned to
specific task
-
related

training examples.

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96

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13


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17


Cortical column

Retrieved from "
http://en.wikipedia.org/wiki/Cortical_column
"

A
cortical column, also called
hypercolumn

or sometimes cortical module,
[1]

is a group of neurons in the
brain

cortex

which can be
successively penetrated by a probe inserted perpendicularly to the cortical surface, and which have nearly identical
receptive fields
.
Neurons within a
minicolumn

encode similar features, whereas a
hypercolumn

"denotes a unit containing a full set of values for any
given set of receptive field parameters"
[2]
. A cortical module is defined as either synonymous with a
hypercolumn

(
Mountcastle
) or as a
tissue block of multiple overlapping
hypercolumns

(
Hubel&Wiesel
).

Human cerebral cortex

The human
cerebral cortex

is composed of 6
somewhat distinct layers
; each layer identified by the nerve cell type and the destination
of these nerve cell's
axons

(within the brain). The human cortex is a roughly 2.4 mm thick sheet of neuronal cell bodies that forms the
external surface of the
telencephalon
. The
dolphin

cortical column is composed of only 5 layers. The
reptilian

cortex has only three
layers.

The columnar functional organization, as originally framed by
Vernon
Mountcastle
, suggests that
neurons that are horizontally more
than 0.5 mm (500 µm) from each other do not have overlapping sensory receptive fields
, and other experiments give similar results:
200

800 µm (
Buxhoeveden

2002, Hubel 1977,
Leise

1990, etc.). Various
estimates suggest there are 50 to 100
cortical
minicolumns

in a
hypercolumn
, each comprising around 80 neurons
.

An important distinction is that the columnar organization is functional by definition, and reflects the local connectivity o
f t
he cerebral
cortex. Connections "up" and "down" within the thickness of the cortex are much denser than connections that spread from side

to

side.

Hubel and Wiesel studies

Hubel

and
Wiesel

followed up on
Mountcastle
's

discoveries in the somatic sensory cortex with their own studies in vision. A part of the
discoveries that resulted in them winning the 1981
Nobel Prize
[3]

was that there were cortical columns in vision as well, and that the
neighboring columns were also related in function in terms of the orientation of lines that evoked the maximal discharge.
Hubel

and
Wiesel

followed up on their own studies with work demonstrating the impact of environmental changes on cortical organization, and
the sum total of these works resulted in their Nobel Prize.

Size of cortex

From the size of the cortex and the typical size of a column, it can be estimated that there are
about two million function columns

in
humans
[4]
. There may be more if the columns can overlap, as suggested by
Tsunoda

et al

[5]
.

References

1.
Kolb, Bryan;
Whishaw
, Ian Q. (2003).
Fundamentals of human neuropsychology
. New York: Worth.
ISBN 0
-
7167
-
5300
-
6
.



2.
Horton
JC
, Adams DL (2005). "The cortical column: a structure without a function".
Philos. Trans. R. Soc.
Lond
., B, Biol. Sci.

360
(1456): 837

62.
doi
:
10.1098/rstb.2005.1623
.
PMID

15937015
.



3.
"The Nobel Prize in Medicine 1981"
.
http://nobelprize.org/medicine/laureates/1981/
. Retrieved on 2008
-
04
-
13.



4.
Christopher Johansson and Anders
Lansner

(January 2007). "Towards cortex sized artificial neural systems".
Neural
Netw

20 (1):
48

61.
doi
:
10.1016/j.neunet.2006.05.029
.
PMID

16860539
.



5.
Kazushige
Tsunoda
,
Yukako

Yamane, Makoto
Nishizaki
, and Manabu
Tanifuji

(August 2001). "Complex objects are represented in
macaque
inferotemporal

cortex by the combination of feature columns".
Nat.
Neurosci
.

4 (8): 832

8.
doi
:
10.1038/90547
.
PMID

11477430
.






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18


Cortical minicolumn

Retrieved from "
http://en.wikipedia.org/wiki/Cortical_minicolumn
"


A cortical
minicolumn

is a
vertical column

through the
cortical

layers of the brain, comprising perhaps 80

120
neurons
,
except in the
primate

primary visual cortex

(V1), where there are typically more than twice the number. There are
about
2
×
10
8

minicolumns

in humans
.
[1]

From calculations, the diameter of a
minicolumn

is about 28

40 µm.

Many sources support the existence of
minicolumns
, especially
Mountcastle
,
[2]

with strong evidence reviewed by
Buxhoeveden

and Casanova
[3]

who conclude "... the
minicolumn

must be considered a strong model for cortical
organization" and "[the
minicolumn

is] the most basic and consistent template by which the
neocortex

organizes its
neurones
, pathways, and intrinsic circuits". See also
Calvin's Handbook

on cortical columns.

Size



The
minicolumn

measures of the order of 40

50 µm in transverse diameter (
Mountcastle

1997,
Buxhoeveden

2000,
2001); 35

60 µm (
Schlaug
, 1995,
Buxhoeveden

1996, 2000, 2001); 50 µm with 80 µm spacing (
Buldyrev
, 2000), or 30 µm with
50 µm (
Buxhoeveden
, 2000). Larger sizes may not be of human
minicolumns
, for example Macaque monkey V1
minicolumns

are 31µm diameter, with 142 pyramidal cells (Peters, 1994)


1270 columns per mm
2
. Similarly, the cat V1 has
much bigger
minicolumns
, ~56 µm (Peters 1991, 1993) .

The size can also be calculated from area considerations: if cortex (both hemispheres) is 1.27
×
1011 µm
2

then if there are
2
×
10
8

minicolumns

in the cortex then each is 635 µm
2
, giving a diameter of 28 µm (if the cortex area were doubled to the
commonly quoted value, this would rise to 40 µm). Johansson and
Lansner
[4]

do a similar calculation and arrive at 36 µm
(p51, last
para
).

Facts


Cells in 50µm
minicolumn

all have the same receptive field; adjacent
minicolumns

may have very different fields (Jones,
2000).


Downwards projecting axons in
minicolumns

are ≈10µm in diameter, periodicity and density similar to those within the
cortex, but not necessarily coincident (
DePhilipe
, 1990).


Thalamic input (1 axon) reaches 100

300
minicolumns
.


The number of
fibres

in the corpus
callosum

is 2

5
×
10
8

(Cook 1984,
Houzel

1999)


perhaps related to the number of
minicolumns
.

References

1.
Towards cortex sized artificial neural systems, Christopher Johansson and Anders
Lansner
,
Neural Networks
, Vol. 20 #1,
pp48

61, Elsevier, January 2007

2.
The columnar organization of the
neocortex
, Vernon B.
Mountcastle
,
Brain
, Vol. 20 #4, pp701

722, Oxford University
Press, April 1997

3.
The
minicolumn

hypothesis in neuroscience, Daniel P.
Buxhoeveden

and Manuel F. Casanova,
Brain
, Vol. 125 #5, pp935

951, Oxford University Press, May 2002.

4.
Towards cortex sized artificial neural systems, Christopher Johansson and Anders
Lansner
,
Neural Networks
, Vol. 20 #1,
pp48

61, Elsevier, January 2007


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19


Our eyes are in constant motion. Even when we attempt to stare straight at a stationary target, our eyes jump and jiggle impe
rce
ptibly.
Although these unconscious flicks, also known as
microsaccades,

had long been considered mere "motor noise," researchers at the
Salk Institute for Biological Studies found that they are instead actively controlled by the same brain region that instructs

ou
r eyes to
scan the lines in a newspaper or follow a moving object.

Their findings, published in the Feb. 13, 2009 issue of
Science
, provide new insights into the importance of these movements in
generating normal vision.

"For several decades, scientists have debated the function, if any, of these fixational eye movements," says Richard Krauzlis
, P
h.D., an
associate professor in the Salk Institute's Systems Neurobiology Laboratory, who led the current study. "Our results show tha
t t
he
neural circuit for generating microsaccades is essentially the same as that for voluntary eye movements. This implies that
they are
caused by the minute fluctuations in how the brain represents where you want to look.
"

"There was a lot of past effort to figure out what fixational eye movements contribute to our vision," adds lead author Ziad
Haf
ed,
Ph.D., Sloan
-
Swartz Fellow in the Systems Neurobiology Laboratory, "but nobody had looked at the neural mechanism that generates

these movements. Without such knowledge, one could only go so far in evaluating microsaccades' significance and why they actu
all
y
exist."

Wondering whether the command center responsible for generating fixational eye movements resides within the same brain struct
ure

that is in charge of initiating and directing large voluntary eye movements, Hafed decided to measure neural activity in the
sup
erior
colliculus before and during microsaccades.

He not only discovered that the superior colliculus is an integral part of the neural mechanism that controls microsaccades,
but

he
also found that individual neurons in the superior colliculus are highly specific about which particular microsaccade directi
ons

and
amplitudes they command

whether they be, say, rightward or downward or even oblique movements. "
Data from the population of
neurons we analyzed shows that the superior colliculus contains a remarkably precise representation of amplitude and directio
n d
own
to the tiniest of eye movements,"
says Krauzlis.

The Salk researchers, in collaboration with Laurent Goffart, Ph.D., a professor at the Institut de Neurosciences Cognitives d
e l
a
Méditerranée in Marseille, France, also temporarily inactivated a subset of superior colliculus neurons and analyzed the resu
lti
ng
changes in microsaccades. They discovered that a fully functional superior colliculus is required to generate normal microsac
cad
es.

"Because images on the retina fade from view if they are perfectly stabilized, the active generation of fixational eye moveme
nts

by the
central nervous system allows these movements to constantly shift the scene ever so slightly, thus refreshing the images on o
ur
retina and preventing us from going 'blind,'" explains Hafed. "
When images begin to fade, the uncertainty about where to look
increases the fluctuations in superior colliculus activity, triggering a microsaccade,"
adds Krauzlis.

Microsaccades may, however, do more than prevent the world around us from fading when we stare at it for too long. Even when
our

gaze is fixed, our attention can shift to an object at the periphery that attracts our interest. In an earlier study, Hafed d
isc
overed that
although we may avert our eyes from an attractive man or woman, microsaccades will reveal such objects of attraction because
the
ir
direction is biased toward objects to which we are unconsciously attracted
.

By showing in the current study that the superior colliculus is involved in generating microsaccades, Hafed and his colleague
s c
ould
now explain why this happens. "
The superior colliculus is a major determinant of what is behaviorally relevant in our visual
environment, so paying attention to one location or the other alters superior colliculus activity and therefore alters these
eye

movements as well,
" says Hafed.


Involuntary maybe, but certainly not random

12Feb09


www.physorg.com/news153670434.html

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20


The Blue Brain project is the first comprehensive
attempt to reverse
-
engineer the mammalian brain, in
order to understand brain function and dysfunction
through detailed simulations.

In July 2005,
EPFL

and IBM announced an exciting new

research initiative
-

a project to create a biologically accurate, functional model of
the brain using IBM's Blue Gene supercomputer. Analogous in scope to the
Genome Project, the Blue Brain will provide a huge leap in our understanding of
brain function and dysfunction and help us

explore solutions to intractable
problems in mental health and neurological disease.

At the end of 2006, the Blue Brain project had

created a model of the basic
functional unit of the brain, the neocortical column. At the push of a button, the
model

could reconstruct biologically accurate neurons based on detailed
experimental data, and

automatically connect them in a biological manner, a task
that involves positioning around 30 million synapses in precise 3D locations.

In November, 2007, the Blue Brain project reached an important milestone and the
conclusion of its first Phase, with the announcement of an entirely new data
-
driven process for creating, validating, and researching the neocortical column.


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21


About the Blue Brain Project

http://bluebrain.epfl.ch/page18699.html

The
cerebral cortex
, the convoluted "grey

matter" that makes up 80% of the human

brain, is responsible for our ability to

remember, think, reflect, empathize, communicate, adapt to

new situations and plan for the future. The cortex first

appeared in mammals, and it has a fundamentally simple

repetitive structure that is the same across all mammalian

species.

The brain is populated with billions of
neurons
, each connected to thousands of
its neighbors by dendrites and axons, a kind of biological "wiring". The brain
processes information by sending electrical signals from neuron to neuron along
these wires. In the cortex, neurons are organized into
basic functional units,
cylindrical volumes 0.5 mm wide by 2 mm high, each containing about 10,000
neurons

that are connected in an intricate but consistent way. These units operate
much like microcircuits in a computer. This microcircuit, known as the
neocortical
column

(
NCC
), is repeated millions of times across the cortex. The difference
between the brain of a mouse and the brain of a human is basically just volume
-

humans have many more neocortical columns and thus neurons than mice.


This structure lends itself to a systematic modeling approach. And indeed, the
first step of the Blue Brain project is to re
-
create this fundamental microcircuit,
down to the level of biologically accurate individual neurons. The microcircuit can
then be used in simulations.

For an in
-
depth view of the project, read Henry
Markram's

Perspectives article in
the
February 2006 issue of Nature Reviews Neuroscience
.




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22


Building the microcircuit

http://bluebrain.epfl.ch/page19092.html

Modeling

Neurons


Neurons are not all alike
-

they

come in

a

variety of complex shapes. The precise shape and structure of a

neuron

influences

its electrical properties and connectivity with

other neurons. A

neuron's electrical properties

are determined to a large extent by a

variety of ion channels distributed in varying densities throughout the cell's

membrane. Scientists have been collecting data on neuron morphology and electrical

behavior of the juvenile rat in the laboratory for many years, and this data is used as

the basis for a model that is run on the Blue Gene to recreate each of the 10,000

neurons in the
NCC
.

Modeling

connections


To model the neocortical column, it is essential to

understand the composition, density and distribution of the numerous cortical cell types. Each class of
cells is present in specific layers of the column. The precise density of each cell type and the volume of
the space it occupies provides essential information for cell positioning and constructing the foundation
of the cortical circuit. Each neuron is connected to thousands of its neighbors at points where their
dendrites or axons touch, known as synapses. In a column with 10,000 neurons, this translates into
trillions of possible connections. The Blue Gene is used in this extremely computationally intensive
calculation to fix the synapse locations, "jiggling" individual neurons in 3D space to find the optimal
connection scenario.

Modeling the column


The result of all these calculations is a re
-
creation, at the cellular level, of the
neocortical column, the basic microcircuit of the brain. In this case, it's the cortical column of a juvenile
rat. This is the
only

biologically accurate replica to date of the
NCC

-

the neurons are biologically realistic
and their connectivity is optimized. This would be impossible without the huge computational capacity of
the Blue Gene. A model of the
NCC

was completed at the end of 2006.



In November, 2007, The Blue Brain Project officially announced the conclusion of Phase I of the project,
with three specific achievements:

1.
A new modeling framework for automatic, on
-
demand construction of neural circuits built from
biological data

2.
A new simulation and calibration process that automatically and systematically analyzes the biological
accuracy and consistency of each revision of the model

3.
The first cellular
-
level neocortical column model built entirely from biological data that can now serve as
a key tool for simulation
-
based research

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23