生命ナノシステム科学
物理博士

ミケレット・ルジェロ

知覚情報科学
e-mail:ruggero@yokohama-cu.ac.jp(全)後期2011年

THE INFORMATION, what is it?

I like cakes!
ケーキは好き！
Mi piacciono i dolci !

Same INFORMATION contents, different representation

The abstract idea of Yes/No

Yes/Nocoder

decoder

Yes/NoCan we measure amount of information ?

Yes/Nocoder

decoder

Information amount

Yes/No音、光、力、化知覚

生命光情報
臭新情報

味音

生き物
情報を知覚音

反応

動き
温度情報
分析

Neuron and other Cells basic characteristics
1. There is a membrane. Internal/External ion density is different
2. On the membrane there are channels. If a channel opens(for any reason), the density difference provoke migration of ions,migration provoke difference of potential.
3. The Opening and closing of channels depends on Potential
Difference between inside/outside of cell. When potential rise
too much, a channel can close, and some other may open. A steep
Spike of potential is generated (action potential).
4. There is a rest time (refractory period), after a Spike the cellcannot do anything else, but rest.

THE TONGUE: the TASTE sense（味覚、みかく）舌The Sweet Receptor function:
Each receptor contains 2 subunits designated T1R2 andT1R3 and is coupled to G proteins.
The complex of G proteins has been named gustducin
because of its similarity in structure and action to thetransducin that plays such an essential role in rodvision.
Activation of gustducin triggers a cascade of intracellular
reactions:- activation of adenylyl cyclase- formation of cyclic AMP (cAMP)- the closing of K+ channels that leads todepolarization of the cell.
Emission of a electric discharge signal
This signal is called SPIKESPIKE signal istransmitted
directly to theBRAIN

First Neuron Model

1907 byLapicquefrom Q=CV

Hodgkin-Huxley Model (1952)

Outside Cell

Inside Cell

K+Na+Ca+Hodgkin-Huxley Model (1952)
J. Physiol. Vol 117 500-540, 1952 by A.L. Hodgkin and A.F. Huxley.

Based on Giant Squid experiments

d muu= m 1 − m −m mdt
dnuu= n 1 − n −n ndt
dh= uh 1 − h −uh hdt

From Experiments

Sym Matlabd m=m  u 1 − m −m u  mdtdn=n  u 1 − n −n  u  ndt
dh=h  u 1 − h −h  u  hdt

plot(y)mequilibrium

From Experiments

tExponentialfittingWe see that the equilibrium m and n increase with u whereas hdecreases. Thus, if some external input causes the membrane
voltage to rise, the conductance of sodium channels increases
due to increasing m. As a result, positive sodium ions flow into
the cell and raise the membrane potential even further. If thispositive feedback is large enough, an action potential isinitiated.

+dU-nm*hAction
potential
RestovershootAt high values of u the sodium conductance is shut off due to the factor
h. As indicated the `time constant' is always larger than . Thus thevariable h which closes the channels reacts more slowly to the voltage
increase than the variable m which opens the channel. On a similar slow
time scale, the potassium (K+) current sets in. Since it is a current inoutward direction, it lowers the potential. The overall effect of thesodium and potassium currents is a short action potential followed by anegative overshoot; The amplitude of the spike is about 100 mV

Spike rate

Auto-oscillation is possible

The Hodgkin-Huxley equations may also be studied for constant
input I(t) = I0 for t > 0. (The input is zero for t 0). If the value I0 islarger than a critical value I 6A/cm2, we observe regular spiking;We may define a firing rate = 1/T where T is the inter-spikeinterval. The firing rate as a function of the constant input I0
defines the gain function plotted.

THE SKIN: the TOUCH sense（触覚）THE SKIN: the TOUCH sense（触覚）信号

t0
Ruffini Endings

スロー
圧力センサーt

A mechanical deformation of the sensory dendrite causes adepolarization to occur within the dendrite. This dendrite is at thetip of a unipolar neuron; The sensory dendrite is embedded within
the sensor corpuscle. Any depolarization that is large enough in thedendrite itself will cause an action potential to be generated at thetrigger zone. This action potential will run all the way down theaxon and reach the brain.

圧力センサー

Krause corpuscole

信号t0t

Responds to hairdisplacement.
Wraps around hairfollicle in, of course,
hairy skin.

Hair
FollicleEnding最大感
度：20-40
Hz

Meissner corpuscole

振動センサー

信号30220 Hz最大感
度：150300 Hz

Pacinian corpuscoleF =−kxm a =−k xd2 x
m=− k x2dt

Wave equationm

Wave function
pulsation

d2 x k
x =02mdt

x =−sin  t =k =2  f mFrequency depends on material “m” and “k”

温度センサー圧力センサー
化学センサーFree nerve endings

Different types of freenerve endings that
respond to mechanical,thermal or noxious
stimulation.

先端人工触覚センサー：原子間力顕微鏡(AFM: Atomic Force Microscope)Operational Modes: CONTACT-MODEz

L =1 m m
 z =1nm

R2ααL

=arctan  z LL sin  alfa = z
R sin 2 alfa = x
x=2 RzLOperational Modes: VIBRATING-MODES- Phase Changes- Amplitude Changes

The cantilever vibration Physics:

How to measure the cantilever Force:THE NOSE: the OLFACTORY sense（嗅覚、きゅうかく）

The olfactory sensors are located in yellow pigmented areas on eachside of the inner nose. These areas are about 2.5 cm2 in area each,and contain chemoreceptors, which are nerve cells responding tocertain chemicals that are carried to the sensors as gases. The
detailed functioning of these cells does not appear to be known.

The glomeruli, each receiving signals from some 26 000 receptors.
The olfactory bulbs on either side are cross-connected. Finallynerve fibres reach the olfactory areas in the anterior lobes of thebrain.

- The olfactory sense is some
10000 times as sensitive as
taste,
andisprimarily
responsible for the flavours offood.
- The types of olfactory
sensations are 6:
fruity, flowery, resinous, spicy,
foul (rotten), and burned.

Olfactory Cell (receptors)

Mitral Cell

Signal from Mitral Cells

スパイク周波数

スパイクの時間間隔

S
香科密度

香科密度

S=sparsity

Gはガインといます。
香科によってGは違います。
例：Gcamphor=1.0
Glemon=2.5

スパイクの時間間隔

Ganisole=20s
香科密度

s=sparsity

スパイク

Limonene

香科で実験

人工嗅覚

JPL 人工嗅覚デバイス

???

Polymers complex structure

+VIR

THE ELECTRONIC NOSE SMELLS SOMETHING
Each polymer changes its size, and therefore its resistance, by a different amount, making
a pattern of the changee-e-e-ee-e-

If a different compound had caused the air to change, the pattern of the polymer
films' change would have been different:AFM NOSE
検出光源

振動

結合する分子によって振動のパターンが異なる。

THE EAR: the HEARING sense（聴覚、ちょうかく）Frequency

Amplitude

The sound it is a PRESSURE WAVE

The sound it is a PRESSURE WAVE
f=Frequency
λ=Wavelength

空気の圧力P  t = P o sin  t  k x P(t)=2  /=2  fk =2 /z
τP(t)

周波数：

f = 1/τt
強度P(t)

λ

波長：zf=λThe sound SPECTRUM

y=sin  t
y =sin  tsin 2t
y =sin  tsin 3tThe FOURIER trasformation

F []y=sin  tsin3t
∞
1F []= ∫∞ y e −i  tNωy =sin  t

y =sin  tsin 3t∞
1F []= ∫∞ ye −i tN∞
1F []= ∫∞ ye −i tNInner Ear
Middle Ear
Outer Ear

Outer Ear- the “Pinna” is not frequency flat.
Different frequencies are detected atdifferent intensities.

Middle Ear

Inner Ear

The semicircular canals detect angular acceleration. Thereare 3 canals, corresponding to the three dimensions in which
you move, so that each canal detects motion in a single
plane. Each canal is set up as shown below, as a continuous
endolymph-filled hoop. The actual hair cells sit in a small
swelling at the base called the ampula.

Localization of soundThe ability to estimate just where a sound is coming from, sound localization, is dependent on
hearing ability of each of the two ears, and the exact quality of the sound. Since each ear lies onan opposite side of the head, a sound will reach the closest ear first, and its amplitude will belarger in that ear.
The shape of the pinna (outer ear) and of the head itself result in frequency-dependent variationin the amount of attenuation that a sound receives as it travels from the sound source to the ear.
Furthermore, this variation depends not only on the azimuthal angle of the source, but also on
its elevation. This variation is described as the head-related transfer function, or HRTF. As aresult, humans can locate sound both in azimuth and altitude. Most of the brain's ability tolocalize sound depends on interaural (between ears) intensity differences and interaural
temporal, or phase, differences. In addition, humans can also estimate the distance that a soundcomes from, based primarily on how reflections in the environment modify the sound, forexample, as in room reverberation.
Human echolocation is a technique used by some blind humans to navigate within theirenvironment by listening for echoes of clicking or tapping sounds that they emit.

tot1

LOCALIZATION- Phase Difference 位相- Intensity Difference 強度- HRTF

THE ARTIFICIAL EARS: (マイクロホン)

Frequency response
Dynamic Response

Condenser microphones

VCRC = Sd
V C =QC

Condenser microphones have a flatter
frequency response than dynamics

Advantages:
Best overall frequency
response makes this themicrophone of choice formany recording
applications.
Disadvantages:
Expensive
May pop and crack whenclose miked
Requires a battery or
external power supply tobias the plates.

Dynamic microphones (Electromagnetic Microphones)

Dynamics do not usually have thesame flat frequency response ascondensers. Instead they tend tohave tailored frequency responses
for particular applications.
However they have a good
Dynamic response.

=∮ B.dA−d e=dt

=∮ B.dA−d e=dt

LASER microphone (Spy Microphone)LASER microphone (Spy Microphone)Intensity
光の強度Gaussian curve

xht
Lig

Glass, mirror 窓の表面、ガラスなど窓に反射するときに音の強度によって反射角度は少し変更します。角度変更によって、検出に得られた光の信号は同じように変更します。

光Source 光源

Detector 検出

Sound Signal
音の信号The sound system, a summary: documentaryTHE VISION, What is light ?

140 A.D., Claudius PtolemySnell law (1621)

Principle of Fermat (1650, Snell+29 years!)
フェルマット原則
Out of all possible paths, lighttakes the path which requires
the less timen=c

V 光のスピード
c= 300.000Km / sec

土壌?

+水
Snell Law
(1650)

n1 X F=n2 E Cn1 E C = n 2 X F
n1 XCsin  i= n 2 XCsin  r
n 1 sin  i= n 2 sin  r

干渉


d



Δxn =d ∗sin n =0,1,2,3. ...

Interference: Max condition

チャージを存在Q

Coulomb法:

q ｒ

チャージを動くと

Ampere法

静電場

Faraday法

チャージを存在
静電場（Coulomb法）
チャージを動く 変動磁場 変動電場
(Ampere法) (Faraday法)

τ

E(t)

周波数：f = 1/τ

t

E(t)

λ

波長：zf=λ搬伝

RGB

“RGB”の色で全部の色を作る。この事は光の特性ではな
い、人間の目の特性です（！）
人間は 「Trichromatic Vision」
動物の世界の中に「Tetrachromacy」「Pentachromats」などがありますTHE VISION, How does it work ?

RGB

ConesBlindSpot !

Neurons

フォベラ

FoveaRods

grThis diagram is based on thevalues of r, g, and b (where r= R/(R+G+B), g = G/
(R+G+B), and b = B/
(R+G+B)). Furthermore,because r + g + b = 1, if twovalues are known, the thirdcan always be calculated andthe b value is usuallyomitted. The r and g values
together constitute thechromaticity of a sample.

Are we pre-wired, or we learn ?!?

Possible

Impossible!

We have to eat this!
(vitamin A)
E------

r=3mmλ=400nmsameanotheronly

Edgeenhancement!

manyonly

“RGB”の色で全部の色を作る。この事は光の特性ではな
い、人間の目の特性です（！）
人間は 「Trichromatic Vision」
動物の世界の中に「Tetrachromacy」「Pentachromats」などがありますIs a chess machine “intelligent” ?
- The artificial Intelligence Boom: (~1970)- The Turing Machine

Alan Turing, MathematicianLondon 1912-1954

A Turing Machine read and write on apaper tape accordingly to a set of rules.
Information on the tape are instructions
or results.
Turing demonstrated mathematically thatwith an infinite tape such machine could
be programmed to perform any definableoperation.

TuringMachine
Tape withdata

Turing ask himself if a Machine can beintelligent.
He defined the Turing Test:

http://testing.turinghub.com/
www.fil.ion.ucl.ac.uk/~asaygin/tt/ttest.html

Does Turing Test define an “Intelligent” Machine ?
The Chinese Room Experiment.

John Searle
1980

NO! And the digital computer is a Chinese Room!

Brain is Auto-associative

There is no square here.
We see a square, because brain “knows” a square andauto-associate the image to the pattern

Why we dont understand theBrain yet ?
• We do not have a “paradigm”• Roads and Aliens example

The neocortex is in 6 layers

Many pyramidal
neurons

Lots of Axons, few neurons

About 3 10^10 cells
only in neocortex

What a neuron does ?
• It emit a “spike”, depending on input signalsarriving at the synapses.
• After 5 msec, it reset itself (200Hz).
• More signal to synapses, more easy to spike(Hebbian Learning).

Good inPrediction
Classification

Vernon Mountcastle idea (1978)• Brain is uniform• Different area perform same
operation• There is a common algorithm• Animal experiments confirm
Mountcaste ideas.

The “Temporal Patterns”• Vision, Audio, Smell, Taste, Touch... all are“streams” of spikes from neurons.
• There “streams” come from sensory districtsin the brain• The “streams” are kind of patterns in space
and time.
• Vision too is a stream of spikes, as well assound and all the other senses.

Brain is a pattern machine• Brain use memory to solve
problems.
• Example of “catch ball”,
computer can perform asimple catchball like achild can.

The “invariant” representation• Brain has a way to memorize
things “invariantly” from details.
• We can recognize a dog from acat from any angle or any
position.
• Computers cannot !

The hierarchy• Brain use a hierarchy to store informations.
• We remember and do things in sequence.

4 Visual regions in the brain
Each box is divided in the 6layers.

The 6 cortex layers

A global scale hierarchySequences of sequences• Brain create hierarchical sequences ofsequences.

Brain is a “prediction” machine• Sequences are stored in synaptic strength• Once a new sequence arrive, it is comparedand if it is known it activate the “invariant”
circuits• If the new sequence is not known, it will
signal it to higher hierarchical levels.
• Many examples in everyday life

Vernon Mountcastle idea (1978)• Brain is uniform• Different area perform sameoperation• There is a common algorithm• Animal experiments confirm
Mountcaste ideas.

The “Temporal Patterns”• Vision, Audio, Smell, Taste, Touch... all are“streams” of spikes from neurons.
• There “streams” come from sensory districtsin the brain• The “streams” are kind of patterns in space
and time.
• Vision too is a stream of spikes, as well assound and all the other senses.

Brain is a pattern machine• Brain use memory to solve
problems.
• Example of “catch ball”,
computer can perform asimple catchball like achild can.

The “invariant” representation• Brain has a way to memorize
things “invariantly” from details.
• We can recognize a dog from acat from any angle or anyposition.
• Computers cannot !

The hierarchy• Brain use a hierarchy to store informations.
• We remember and do things in sequence.

4 Visual regions in the brain
Each box is divided in the 6layers.

The 6 cortex layers

A global scale hierarchySequences of sequences• Brain create hierarchical sequences ofsequences.

Brain is a “prediction” machine• Sequences are stored in synaptic strength• Once a new sequence arrive, it is comparedand if it is known it activate the “invariant”
circuits• If the new sequence is not known, it will
signal it to higher hierarchical levels.
• Many examples in everyday life

The “BRAIN”
characteristics• Brain is uniform (same algorithm)• Temporal patterns (sequence of SPIKES, likemusic)• Hebbian-Learning (repeat strengthen the network )• Invariant abilities (holistic memory, like holograms)• Hierarchy structure (sequence of sequences)• Brain is a “prediction” machine (new=interesting,known=boring ==> Derivative, Fourier)Brain is a simple machine!
• If we understand the principles we can makeactually brain-like machines.

On Intelligence, Jeff Hawkins (2004)

The future of intelligence• Capacity: Brain has no problems witherrors. We loose 1000 neurons perday, but we make no mistakes.
• Connectivity: the brain is sointerconnected. It looks impossible to
make a machine such completelyinterconnected. However thetelephone system is just the same!

Inconceivable applications• Vision• Security• Weather• Speech• Smart cars
• Unimaginable global-scale applications

Greatimprovement• Silicon chip
• Hard disk
• DNA sequencing• Fiber optics• Software

Stuck !
• Batteries• Motors• Robots

Brain-like computers future• Speed (compare 200Hz with.... GHz !)• Capacity (more than 6 layers, more space,more sensors)• Replicability (humans take years to learn
walking or speaking...)• Sensing system: sonar, radar, infrared...
(brains can work with pattern of any kind ofphysical stimuli, on a global scale too)How long it will take?
• Compare with cell phones or the internet.
• Usually research stays still for years, thenexplodes.
• If we break the brain algorithm we will
progress like never mankind did before.

Why we dont understand theBrain yet ?
• We do not have a “paradigm”• Roads and Aliens example

We study the Brain by tests
We study the brain by Illusions (錯覚、知覚の間違い)

The squares marked A and B are the same shade of gray.