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TRANSCRIPT
Check if film is connectedLoad NRM via Birm Load nrmp6 and use guildford config aks2000 training and run naturally
'Digital Neuromodelling Based on the Architecture of the Brain: Basics andApplications'Digital neuromodelling is aimed at understanding the architecture of the brain. I shall concentrate specifically on visual awareness. The principles of a digital neuromon will be described and examples given of simple dynamic modules made of such neurons . A software modelling tool (Neural Representation Modeller) will be introduced and provided for the students to test on their own computers.Examples of work done in this area will be given: anaesthesia;planning; visual working memory; imagination mechanisms; visual deficits in Parkinson's disease and explortory robotics.The scope of the discussion will then be broadened to suggest an axiomatic approach to the understanding of neural mechanisms responsible for being conscious.
Igor Aleksander FREng Igor Aleksander FREng Leverhulme FellowLeverhulme Fellow
Emeritus Professor of Neural Systems EngineeringEmeritus Professor of Neural Systems Engineering DEPARTMENT OF DEPARTMENT OF
ELECTRICAL AND ELECTRONIC ENGINEERINGELECTRICAL AND ELECTRONIC ENGINEERINGImperial College, LondonImperial College, London
Visiting Research Fellow, U of SussexVisiting Research Fellow, U of Sussex
TUTORIALTUTORIALDigital Neuromodelling Digital Neuromodelling
TechniquesTechniques
Turin Workshop on Machine Turin Workshop on Machine ConsciousnessConsciousness
A Robot in our lab:A Robot in our lab:Driven by a Driven by a
neuromodel rather neuromodel rather than an AI programthan an AI program
The issue for todayThe issue for today
How does neuromodelling How does neuromodelling differ from AI programs differ from AI programs andand
neural networks?neural networks?
What can it achieve?What can it achieve?
We start with:We start with:
Who was the first person Who was the first person ever to write an AI program?ever to write an AI program?
Who is he?Who is he?
Claude Shannon Claude Shannon of the of the
Bell Telephone LaboratoriesBell Telephone Laboratories
““Programming a computer for playing Programming a computer for playing chess” chess”
Philosophical Magazine, pp 256-275, Philosophical Magazine, pp 256-275, 19501950
Shannon’s Shannon’s * algorithm* algorithmEach boardEach board
position has a position has a valuevalue
1, -----5-------101, -----5-------10MEME
MEME
YOUYOU
77
??
A stacking problemA stacking problem
The search space contains 85 The search space contains 85 possibilities among which the possibilities among which the
computer needs to search.computer needs to search.
A Search A Search ProcessProcess
•Works better in some areas.Works better in some areas.
•Is not pre-programmed but Is not pre-programmed but learns and adapts.learns and adapts.
•It has an architecture from which It has an architecture from which its activity ‘emerges’ and which its activity ‘emerges’ and which has evolved.has evolved.
The Brain:The Brain:
Can it be modelled in these Can it be modelled in these terms?terms?
Where do the models Where do the models start?start?
Neuron: The building brick Neuron: The building brick of the brain (one of 10of the brain (one of 101111))
What were they modelling?
What are the elements of this model?
The Classical 1943 McCulloch and Pitts Neuron Model
F
F=1 iff
x1
x2
x3
xn
WW11
WW22
WW33
WWnn
Xj Wjjj
TT
XXj j WWj j >> TTjj
THIS IS THIS IS NOTNOT WHAT WE WHAT WE DO IN DIGITAL DO IN DIGITAL
NEUROMODELLINGNEUROMODELLING
The Digital Model of a The Digital Model of a NeuronNeuron
•Needs to learn and Needs to learn and generalisegeneralise
•Needs to make efficient Needs to make efficient use of memory (10 million use of memory (10 million neurons)neurons)
•Needs to be fast (10 Needs to be fast (10 million neurons updated in million neurons updated in 1/4 Sec.)1/4 Sec.)
2
A Digital neuron training 1
0 0 0 1 2
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50 0 1 00 0 1 0
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TrainInput array State array
Neuron
2
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Train
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1 0 1 0 1 0 1 0 0 3
Input array State array
Neuron
A Digital neuron training 2
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Recall associated memory
0 0 0 1 2
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Recall
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v
v
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v v
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v v
. .
v
v
v
Input array State array
Neuron
v
So, we have a picture of a So, we have a picture of a neuron but how do we test neuron but how do we test
neural systems on a computer?neural systems on a computer?
We use a software authoring We use a software authoring kit:kit:
NRM: Neural Representation NRM: Neural Representation ModellerModeller
Email: Email: [email protected]@imperial.ac.uk
How does a digital neuron work?How does a digital neuron work?
Colour recognitionColour recognition
??
1 neuron, 10,000 connections1 neuron, 10,000 connections(brain 15,000 connections)(brain 15,000 connections)
How does a single layer ofHow does a single layer ofdigital neurons work?digital neurons work?
namename
3200 neurons, 20,480 connections3200 neurons, 20,480 connections(Brain 100 billion neurons 1000 trillion (Brain 100 billion neurons 1000 trillion connections)connections)
How do we go from a How do we go from a single neuron to single neuron to DEPICTIONS and DEPICTIONS and
MEMORY in some parts MEMORY in some parts of the brain?of the brain?
Communicating neurons: Communicating neurons: memory and imaginationmemory and imagination
Using NRM for Using NRM for Brain ModellingBrain Modelling
Macaque monkey brainMacaque monkey brain
Connections between different visual areasConnections between different visual areas
Visual Awareness:Visual Awareness:
Basic MechanismsBasic Mechanisms
Macaque monkey brain
Visual areas shown in colour
Conscious visionConscious vision
World-centered World-centered reconstruction: reconstruction:
perception, visual perception, visual memorymemory
eyeeye
scsc
musclemuscle
Early, unconscious visionEarly, unconscious visionEye-centeredEye-centered
deconstructiondeconstruction
Foveal eye
SuperiorColliculus
&Motor Areas
PositionExtrastriate areas(Gaze Locked)Visual World
Depiction
Gaze information
j
WORDINPUT
WORDDEPICTION
Visual Working Memory(learned depictions)
Visual AwarenessArea
UTTERANCE
Neural Representation Modeller Neural Representation Modeller
Neural NetNeural Net
Neural NetNeural Net
Neural NetNeural Net
Neural NetNeural Net
Parkinson’s DiseaseParkinson’s Disease
Visual Visual Consciousness Consciousness
DeficitsDeficits
A TYPICAL A TYPICAL TASKTASK
SC
PVCx
R.topic
ExStVCx
Motor
Gaze Locked
Gaze Lock Control
Fov
PeriFov
Eye Pos
Eye MovEye Musc
Auditory
Gaze Dep
Broad
Form/Shape
Form
FF
F
F
F
PARKINSON’S & VISIONPARKINSON’S & VISION))
BASAL G.
•The digital neuron model: an The digital neuron model: an adaptive mapping device.adaptive mapping device.
•The neural module: capable of The neural module: capable of depiction and memory depiction and memory
•The neural architecture: The neural architecture: capable of depictive capable of depictive awareness awareness
So far: ...So far: ...
But what does this tell
But what does this tell
us about personal
us about personal
sensation, about being
sensation, about being
conscious?conscious?
.. an understanding of the .. an understanding of the
mechanisms that are mechanisms that are ESSENTIAL for any organism ESSENTIAL for any organism
to be conscious.to be conscious.
There appear to be FIVEThere appear to be FIVE
Digital Models of the neural Digital Models of the neural brain provide ...brain provide ...
11
Perceptual Perceptual mechanisms mechanisms that that DepictDepict the world the world
22
Imaginational Imaginational mechanisms mechanisms that recall that recall Depictions Depictions of the of the
worldworld
Note: Note: 1 1 and and 2 2 mingle tomingle to provide a sensation of the provide a sensation of the world. world.
Aleksander, I. & Dunmall, B. (2000) Aleksander, I. & Dunmall, B. (2000) Proc R Soc Lond BProc R Soc Lond B, 267, 197-200, 267, 197-200
3 3
Attentional Attentional mechanisms that mechanisms that select which parts of the select which parts of the
world are world are DepictedDepicted
44
Planning Planning mechanisms that mechanisms that cause cause imaginational imaginational
depictions depictions to predict events. to predict events.
55
Affective Affective mechanisms that mechanisms that evaluate planning depictions.evaluate planning depictions.
These axioms lead to a These axioms lead to a depictive architecture that depictive architecture that may be necessary in may be necessary in objects that could be said objects that could be said to be conscious.to be conscious.
These mechanisms are These mechanisms are realisable by the realisable by the
neurocomputation we neurocomputation we have seen.have seen.
They are also the TEST They are also the TEST for the presence of a for the presence of a
minimal form of minimal form of consciousnessconsciousness
A take-home challenge:A take-home challenge:
When the five mechanisms When the five mechanisms are instilled in some robot,are instilled in some robot,
what reason would there be what reason would there be for for denyingdenying that the robot is that the robot is
conscious?conscious?
The issue for todayThe issue for today
How does brain modelling How does brain modelling differ from AI programs and differ from AI programs and
neural networks?neural networks?
What can it achieve?What can it achieve?
Useful properties emerge Useful properties emerge from a specific from a specific
architecture, these include architecture, these include awareness and awareness and
imagination imagination Gets closer to human-like Gets closer to human-like performance in computing performance in computing
systems and robots.systems and robots.
Contacts & Background:Contacts & Background:
emailemail: [email protected]: [email protected]
NRMNRM: [email protected]: [email protected]
Books:Books:
Aleksander/Morton: Introduction to Aleksander/Morton: Introduction to Neural Computing (2nd ed.), Thompson Neural Computing (2nd ed.), Thompson Press, 1995Press, 1995
Aleksander: Impossible Minds: My Aleksander: Impossible Minds: My Neurons My Consciousness, ICPress, Neurons My Consciousness, ICPress, 19961996
Aleksander: How to Build a Mind: Aleksander: How to Build a Mind: Machines with Imagination: Orion Press, Machines with Imagination: Orion Press, 2001 2001