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Computers - Using your Brains PowerPoint PPT Presentation

Computers - Using your Brains. Jim Austin Professor of Neural Computation. Pentium III. So how complex is it ? 10 12 neurons … 1,000,000,000,000 1000 connections between neurons. One brain can hold ... 1,000,000,000,000,000 numbers !. What do 10 12 neurons look like ?.

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Presentation Transcript

Jim Austin

Professor of Neural Computation

Pentium III

So how complex is it ?

• 1012 neurons … 1,000,000,000,000

• 1000 connections between neurons.

• One brain can hold ...

1,000,000,000,000,000 numbers!

What do 1012 neurons look like ?

• 1600 times Population of the world (6,100,000,000)

• 78,125 times the complexity of the Pentium III

• Equal to the number of stars in our galaxy

4 Meters

4 Meters

4 Meters

Sand

• What are computers good at ?

• Storing data - numbers and facts

• Pushing data around

• What are computers bad at ?

• Being reliable

• Finding information - knowledge

• Doing very complex things - recognizing images

• Learning to do the job them selves!

ACE

Neurons verses Gates

Input 1

Output

Input 2

NAND Gate

Boolean Logic - both inputs OK, output not OK

=

=

Gates - NAND

ALL inputs to be OK for output to be NOT OK

Output

Input 1

Input 2

=

Evolution ?

Should have picked a NAND gate for the brain...

Neuron

Output = threshold (input A x weight A + input B x weight B)

A

+

Inputs

Output

B

“Weights”

Threshold logic - threshold 1 - one or more inputs OK  output OK

=

=

Neuron

At least one OK for output to be OK

=

At least three OK’s for output to be OK

Can also alter connections/importance of inputs

using the weights on the inputs

1

0

1

1

+

3.5

0.5

1

1

Why did this difference develop ?

• “The analysis of the operation of a machine using two indication elements and signals can be conveniently be expressed in terms of a diagrammatic notation introduced, in this context, by Von Neuman and extended by Turing. This was adopted from a notation used by Pits and McCulloch as a possible way of analyzing the operation of the nervous system,…” Calculating Instruments & Machines, D Hartree, 1950, Cambridge University Press.

• Probably dropped due to the development of the silicon chip

• simpler to build Boolean logic gates rather than neuron units.

n

z

k inputs

Functional elements.

Threshold n gate

k n

1

z

Excitation, “OR”

2

z

Excitation, “AND”

ICT Orion Computer

• Used ‘Neuron’ logic - 1962

Learning !

Learning at neuron level =

Adjustment of which inputs are important

Conventional computers have no implicit learning ability

Spot the difference

+

Happy

+

Hungry

Threshold = 2

+

Happy

+

Hungry

+

Happy

+

hungry

Can we build useful systems with neurons ?

Better tolerance to failure

Parallelism/use of threshold logic/distributed memory

Faster operation

Massive parallelism

Threshold logic/neurons

Where the inputs are uncertain

Threshold logic/neurons.

Where we want low power

Asynchronous systems

Use of weights and learning methods.

So what have we done with these ?

Cortex-1

28 Processor cards, each holding 128 hardware neurons.

Each with 1,000,000,000 weights.

16MHz.

PCI based card.

Complete Machine:

400,000,000 neuron evaluations per second

28,000 inputs

30 bits set on input

1,000,000 neurons.

Cortex-1 node

5,120,000,000 neuron weights, 640 neurons.

Text search engines

• Tolerant to spelling errors.

• Finds similar words to those supplied, for example chair, seat, bench.

• Learns these similarities automatically from text.

• Uses neural engine for document storage.

• Estimated 400,000,000 documents searched per second.

Molecular Databases

• One of few systems that deal with the full 3D molecule

Query

Good matches

Thanks...

Aaron Turner

Mick Turner

Vicky Hodge

Julian Young

Anthony Moulds

Zyg Ulanowski

Ken Lees

Michael Weeks

Sujeewa Alwis

John Kennedy

David Lomas

and many others ….

(It’s Brains from Thunderbirds !)