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Lecture 1 MATHEMATICS OF THE BRAIN with an emphasis on the problem of a universal learning computer (ULC) and a universal learning robot (ULR) Victor Eliashberg Consulting professor, Stanford University, Department of Electrical Engineering. Slide 0.

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Lecture 1

MATHEMATICS OF THE BRAIN

with an emphasis on the problem

of a universal learning computer (ULC)

and a universal learning robot (ULR)

Victor Eliashberg

Consulting professor, Stanford University,

Department of Electrical Engineering

Slide 0


WHAT DOES IT MEAN TO UNDERSTAND THE BRAIN?

1. User understanding.

2. Repairman understanding.

3. Programmer (educator) understanding.

4. Systems developer understanding.

5. Salesman understanding.

Slide 1


TWO MAIN APPROACHES

1. BIOLOGICALLY-INSPIRED ENGINEERING (bionics)

Formulate biologically-inspired engineering / mathematical problems. Try to solve these problems in the most efficient engineering way.

This approach had big success in engineering: universal programmable computer vs. human computer , a car vs. a horse, an airplane vs. a bird.

It hasn’t met with similar success in simulating human cognitive functions.

2. SCIENTIFIC / ENGINEERING (reverse engineering = hacking)

Formulate biologically-inspired engineering or mathematical hypotheses. Study the implications of these hypotheses and try to falsify the hypotheses. That is, try to eliminate biologically impossible ideas!

We believe this approach has a better chance to succeed in the area of brain-like computers and intelligent robots than the first one. Why?

So far the attempts to define the concepts of learning and intelligence per se as engineering/mathematical concepts have led to less interesting problems than the original biological problems.

Slide 2


HUMAN ROBOT

Slide 3



OUR MOST IMPORTANT PERSONAL COMPUTER

12 cranial nerves ; ~1010 neurons in each hemisphere

~1011 neurons

31 pairs of nerves; ~ 107 neurons

8 pairs

12 pairs

5 pairs

6 pairs

Slide 5


The brain has a very large but topologically simple circuitry

The shown cerebellar network has ~1011 granule (Gr) cells and ~2.5 107 Purkinje (Pr) cells. There are around 105 synapses between T-shaped axons of Gr cells and the dendrites of a single Pr cell.

Pr

Memory is stored in such matrices

Slide 6

LTM size:

Cerebelum: N=2,5 107 * 105= 2.51012 B= 2.5 TB.

Neocortex: N=1010 * 104= 1014 B= 100 TB.


Big picture: Cognitive system (Robot,World) circuitry

External system (W,D)

Computing system, B, simulating

the work of human nervous system

Sensorimotor devices, D

B

D

W

Human-like robot (D,B)

External world, W

B(t)is a formal representation ofBat time t, where t=0 is the beginning of learning. B(0) is an untrained brain.B(0)=(H(0),g(0)), where

H(0) = H is the representation of the brain hardware,

g(0) is the representation of initial knowledge (state of LTM)

Slide 7


CONCEPT OF FORCED MOTOR TRAINING circuitry

M

SM

External system (W,D)

Brain (NS,NM,AM)

D

AM

S

NS

Motor control:

W

associations

M

NM

Teacher

During training, motor signals (M) can be controlled byTeacher or by learner (AM) . Sensory signals (S) are received from external system (W,D).

.

Slide 8



Working memory and circuitry

mental imagery

M

AS

D

S

MS

S

associations

S

NS

Motor control

W

S

AM

M

SM

M

associations

M

NM

Teacher

TWO TYPES OF LEARNING

Slide 10


Mental computations (thinking) as an interaction between motor control and working memory (EROBOT.EXE)

Slide 11


Motor and sensory areas of the neocortex motor control and working memory (

Working memory, episodic

memory, and mental imagery

Motor control

AM

AS

Slide 12


Primary sensory and motor areas, association areas motor control and working memory (

Slide 13


Association fibers (neural busses) motor control and working memory (

Slide 14


SYSTEM-THEORETICAL BACKGROUND motor control and working memory (

Slide 15


Type 0: Turing machines motor control and working memory ((the highest computing power)

Type 0

Type 1

Type 1: Context-sensitive grammars

Type 2

Type 2: Context-free grammars

(push-down automata)

Type 3

Type 4

Type 3: Finite-state machines

Type 4: Combinatorial machines

(the lowest computing power)

Fundamental constraint associated with the general levels of computing power

Traditional ANN models are below thered line. Symbolic systems go above the red line but they require a read/write memory buffer.The brain doesn’t have such buffer.

Fundamental problem: How can the human brain achieve the highest level of computing power without a memory buffer?

Slide 16


Type 4: Combinatorial machines motor control and working memory (

X={a,b,c}

PROM

a b c b a c

Y={0,1}

G

010011

f: X×G→Y

y

x

f

General structure of universal programmable

systems of different types

PROMstands forProgrammable Read-Only Memory.

In psychological terms PROM can be thought of as a Long-Term Memory (LTM). Letter G implies the notion of synaptic Gain.

Slide 17


Type 3: motor control and working memory (Finite-state machines

PROM

x

X={a,b,c}

a

b

c

a

b

c

a

a

s

S=Y={0,1}

0

0

1

1

G

0

1

snext

0

1

0

1

1

1

f: X×S×G→S×Y

a

a

y

0

0

0

1

1

1

y

x

f

snext

s

register

Slide 18


Type 0: motor control and working memory (Turing machines(state machines coupled with a read/write external memory)

PROM

f: X×S×G×M→S×M×Y

G

y

M

x

f

s

Memory buffer, e.g, a tape

snext

register

Slide 19


Basic arcitecture of a primitive E-machine motor control and working memory (

Association inputs

Data inputs to ILTM

INPUT LONG-TERM MEMORY (ILTM)

DECODING, INPUT LEARNING

Similarity function

Control inputs

E-STATES (dynamic STM and ITM)

MODULATION, NEXT E-STATE PROCEDURE

Modulated (biased) similarity function

CHOICE

Data inputs to OLTM

Control outputs

Selected subset of active locations of OLTM

OUTPUT LONG-TERM MEMORY (OLTM)

ENCODING, OUTPUT LEARNING

Association outputs

Data outputs from OLTM

Slide 20


The brain as a complex E-machine motor control and working memory (

D

SUBCORTICAL SYSTEMS

SENSORY CORTEX

S1

AS1

ASk

W

D

MOTOR CORTEX

SUBCORTICAL SYSTEMS

M1

AM1

AMm

Slide 21


A GLANCE AT THE SENSORIMOTOR DEVICES motor control and working memory (

Slide 21


VISION motor control and working memory (

Slide 22


EYE motor control and working memory (

Slide 23


EYE MOVEMENT CONTOL motor control and working memory (

Slide 24


AUDITORY AND VESTIBULAR SENSORS motor control and working memory (

Slide 25


AUDITORY PREPROCESSING motor control and working memory (

~100,000,000 cells

~580,000 cells

~4,000 inner hair cells ~12,000 outer hair cells

~390,000 cells

~90,000 cells

~30,000 fibers

Slide 26


OTHER STUFF motor control and working memory (

Slide 27


EMOTIONS motor control and working memory ((1)

Slide 28


EMOTIONS motor control and working memory ((2)

Slide 29


SPINAL MOTOR CONTROL motor control and working memory (

SENSORY FIBERS

MOTOR FIBERS

Slide 30


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