An

introduction

to

Neural Networks

Patrick van der SmagtBen Krose..

sigmoidsgn semi-linearii i

original discriminant functionafter weight update

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switchespatterninput

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switchreference

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a very slow approximation

gradient

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number of learning samples

errorrate

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learning set

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number of hidden units

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weight vectorpattern vector

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h(i,k)

Iteration 0 Iteration 200 Iteration 600 Iteration 1900

lateral distance

excitation

LTMLTMSTM activity pattern

STM activity pattern

category representation field

feature representation field F1

F2

input

neurons

neurons

N

M++

+− +

++

−

+

+

F1

F2

G2

G1 reset

input

backward LTM from:

patterninput

4321outputoutputoutputoutput

activenotnot

active

notactive active

not

notactive

notactive

activenot

reinforcementinternal

reinforcement

state vector

detectorreinforcement

ASE systemdecoder

ACE

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tool frame

base frame

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NetworkNeural Plant

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manipulator

modelinverse dynamics

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units

units45 output

29 hidden

sharp left sharp right

30x32 video input retina

8x32 range finderinput retina

straight ahead

66

neuronneuron

1 2neuronneuronneuron

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neuron3 4 5

76neuron neuron

(unused)

orderingof the neurons:

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Bus interface

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Bus interface

or Symbolics)(DEC VAX

Bus interface

or Symbolics)

Front end 3

Front end 2

Front end 1

(DEC VAXFront end 0

Nexus

32

10

sequencersequencer

sequencersequencer

Connection Machine I/O System

16,384 ProcessorsConnection Machine

16,384 ProcessorsConnection Machine

16,384 ProcessorsConnection MachineConnection Machine

16,384 Processors

displayGraphicsData

VaultDataVault

DataVault

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Warp Interface & Host

address

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cell1

cell cell

outputs

inputs

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receptor for

row 5laser for

row 1laser for

maskweight

column 6

Intensity

outV

outV

3.6

3.4

3.2

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2.4

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(b)(a)

+

+−

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ganglionoutput

photo-receptor

δ j

j

j

a

LP

processorlearning

neural state registers

learning functions

learning register

memory

synaptic

readable

and

loadable

parallel

controland

functionlinearnon-

controllearning

accumulator

alu

addersof

tree

mul

mul

mul

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Fct. Fct. Fct. Fct.