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Designing Artificial Minds. Harri Valpola. Computational neuroscience group Laboratory of computational engineering Helsinki University of Technology http://www.lce.hut.fi/~harri/. “The great end of life is not knowledge but action” — Thomas Henry Huxley (1825-1895).

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Designing artificial minds
Designing Artificial Minds

Harri Valpola

Computational neuroscience group

Laboratory of computational engineering

Helsinki University of Technology

http://www.lce.hut.fi/~harri/

Computational Neuroscience Group, LCE

Helsinki University of Technology


“The great end of life is not knowledge but action”

—Thomas Henry Huxley (1825-1895)

Computational Neuroscience Group, LCE

Helsinki University of Technology


Sea squirts our distant cousins
Sea Squirts — Our Distant Cousins

  • Sea-squirts are common marine animals

  • Two stages of development: larva and adult

  • Larvae look very much like tadpoles

Picture:

http://www.jgi.doe.gov/News/ciona_4panel.jpg

Computational Neuroscience Group, LCE

Helsinki University of Technology


No movement no brain
No Movement  No Brain

Picture:

http://www.gulfspecimen.org/images/LeatherySeaSquirt.jpg

Computational Neuroscience Group, LCE

Helsinki University of Technology


Smooth elegant skillful
Smooth, Elegant, Skillful

Picture:

http://www.africanbushsafaris.com/fotos%20touren/Oryx.jpg

Picture:

http://www.ebroadcast.com.au/blahdocs/uploads/tiger_running_sml_2784.jpg

Computational Neuroscience Group, LCE

Helsinki University of Technology


Evolution of the motor system
Evolution of the Motor System

  • Spinal cord: “simple” reflexes and rhythmic movement

  • Brain stem: more complex reflexes

  • Cerebellum / midbrain structures: complex motor coordination

  • Basal ganglia: action selection

  • Hippocampal formation: navigation

  • Neocortex: integration and planning

Computational Neuroscience Group, LCE

Helsinki University of Technology


Methods
Methods

  • Synthetic approach: learning by building

  • Neural network simulations

  • Real and simulated robots

Computational Neuroscience Group, LCE

Helsinki University of Technology


Early motor system
Early Motor System

  • Spinal cord: “simple” reflexes and rhythmic movement

  • Brain stem: more complex reflexes

  • Cerebellum / midbrain structures: complex motor coordination

  • Basal ganglia: action selection

  • Hippocampal formation: navigation

  • Neocortex: integration and planning

Computational Neuroscience Group, LCE

Helsinki University of Technology


Prediction and anticipation
Prediction and Anticipation

Computational Neuroscience Group, LCE

Helsinki University of Technology


Adaptive motor control based on a cerebellar model
Adaptive Motor Control Based on aCerebellar Model

Computational Neuroscience Group, LCE

Helsinki University of Technology


Prediction and anticipation1
Prediction and Anticipation

Computational Neuroscience Group, LCE

Helsinki University of Technology


Self supervised learning in control
Self-Supervised Learning in Control

  • Corrections are made by a large number of “reflexes” (spinal cord, brain stem, cortex / basal ganglia).

  • Cerebellar system learns to control using the reflexes as teaching signals.

Picture of cerebellar system

Computational Neuroscience Group, LCE

Helsinki University of Technology


Reflexes
Reflexes

Stretch reflex

Opto-kinetic reflex

Picture:

http://www.inma.ucl.ac.be/EYELAB/neurophysio/perception_action/vestibular_optokinetic_reflex_fichiers/image004.jpg

Picture:

http://www.cs.stir.ac.uk/courses/31YF/Notes/musstr.jpg

Computational Neuroscience Group, LCE

Helsinki University of Technology


Robot reflex
Robot “Reflex”

Computational Neuroscience Group, LCE

Helsinki University of Technology


The cerebellar system
The Cerebellar System

Picture of cerebellar cortex

Computational Neuroscience Group, LCE

Helsinki University of Technology


Long term depression ltd guided by climbing fibres
Long-Term Depression (LTD) Guided by Climbing Fibres

Picture of LTD in Purkije cells

Computational Neuroscience Group, LCE

Helsinki University of Technology


Vestibulo ocular reflex
Vestibulo-Ocular Reflex

Picture:

http://www.uq.edu.au/nuq/jack/VOR.jpg

Computational Neuroscience Group, LCE

Helsinki University of Technology


System level computational neuroscience
System-Level Computational Neuroscience

Questions to be answered:

  • What kind of components are needed for a cognitive architecture?

  • What are different algorithms good for and how they can be combined?

    The brain is a good solution to these questions  Try to

    understand its algorithms on system level (level of behaviour)

Computational Neuroscience Group, LCE

Helsinki University of Technology


“Without knowledge action is useless and action without knowledge is futile”

—Abu Bakr (c. 573-634)

Computational Neuroscience Group, LCE

Helsinki University of Technology


Components for a cognitive system
Components for a Cognitive System knowledge is futile”

Basal ganglia: selection, reinforcement learning (trial-and-error learning)

Hippocampal formation: one-shot learning, navigation, episodic memory

Neocortex:

  • Represents the state of the world — including oneself

  • Invariant representations, concepts

  • Attention / selection (both sensory and motor)

  • Simulation of potential worlds = planning and thinking

  • Relations and other structured representations (akin to symbolic AI)

Computational Neuroscience Group, LCE

Helsinki University of Technology


Neocortex
Neocortex knowledge is futile”

  • A hierarchy of feature maps: increasing levels of abstraction

  • Bottom-up and top-down/lateral inputs treated differently

  • Local competition

  • Long-range reciprocal excitatory connections

Picture:

http://www.pigeon.psy.tufts.edu/avc/husband/images/Isocrtx.gif

Computational Neuroscience Group, LCE

Helsinki University of Technology


Representations for natural images by independent component analysis ica
Representations for Natural Images by Independent Component Analysis (ICA)

  • http://www.cis.hut.fi/projects/ica/imageica/

  • ICA is an example of ..unsupervised learning.

  • Can learn something like .. V1 simple cells.

Computational Neuroscience Group, LCE

Helsinki University of Technology


Invariant features
Invariant features Analysis (ICA)

  • Group simple features into complex in a hierarchical model.

Picture:

http://cs.felk.cvut.cz/~neurony/neocog/en/images/figure3-1.gif

Computational Neuroscience Group, LCE

Helsinki University of Technology


Complex cells from images
”Complex Cells” from Images Analysis (ICA)

Computational Neuroscience Group, LCE

Helsinki University of Technology


Abstractions and meaning
Abstractions and Meaning Analysis (ICA)

Computational Neuroscience Group, LCE

Helsinki University of Technology


Abstractions and meaning1
Abstractions and Meaning Analysis (ICA)

  • Once we have motor output, we can learn which information is important and meaningful

Left camera

Right camera

Computational Neuroscience Group, LCE

Helsinki University of Technology


Relevance to human enhancement
Relevance to Human Enhancement Analysis (ICA)

How about mind prostheses? New senses (like web-sense)?

Knowing how the brain works would certainly be useful for prostheses, but for healthy persons…

  • Input to the brain is easiest to deliver through existing senses — content matters, not the channel

  • Output from the brain through motor system is more limited  implanted electrodes might surpass this capacity

  • I expect intelligent tools to be far more common than prosthetic devices for a long time, but this doesn’t mean their societal impact would be any smaller

Computational Neuroscience Group, LCE

Helsinki University of Technology


Brain is a good solution for an Analysis (ICA) engineering problem

Neuroscience

Picture:

http://britton.disted.camosun.bc.ca/escher/drawing_hands.jpg

Technology

Computational Neuroscience Group, LCE

Helsinki University of Technology


Kiitos thank you
KIITOS! THANK YOU! Analysis (ICA)

Computational Neuroscience Group, LCE

Helsinki University of Technology


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