«neuroscience crash course - tackling the brain code» · 2017. 3. 14. · neural networks. neural...
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«Neuroscience Crash Course - Tackling the Brain Code»Pascal Kaufmann
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Start
NeuroscienceCrash Course
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Agenda
I. Tackling the Brain: Motivation- Artificial Brains, interfacing brains with machines, boosting intelligence
II. Neuroscience Crash Course: Principles- Understanding the difference between a brain and a computer
III. Interfacing with brains: From neurointerface to artificial brains- What’s going on in neuroscience
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Agenda
I. Tackling the Brain: Motivation- Artificial Brains and Bird Wings, interfacing brains with machines
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Prometheus – stealing the fire from the Gods
2016, Zurich Landesmuseum
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Tackling the human brain: What is the brain code?
2000Neurohybrid InterfaceProf. Sandro Mussa-IvaldiNorthwestern University, USA
1999Sieving electrodeProf. Martin SchwabPeter FromherzUniversity of Zurich, CHMax Plank Inst., GER
2004 - 2007Laser stimulation of neuronsProf. A. HierlemannPhysical Electronics LabETH Zurich
2003 - 2010+Virtual Neural Tissue (VNT)Prof. Rolf PfeiferArtificial Intelligence LaboratoryUniversity of Zurich
2004 - 2005Neurochip recordingsProf. Henry BaltesInst. For Quantum ElectronicsETH Zurich
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Science...
State of the art interface as realized byresearchers at MIT around 1995
Perfect virtual reality: Scene from „the Matrix“ by the Wachowskis, 1999
Approaches in Neural Interfacing: Science versus Science Fiction
... vs. Science fiction
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Vision today – thinking with the power of 1000 brains
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From fishbrain to Hololens: Boosting Intelligence
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From fishbrain to Hololens: Boosting Intelligence
Malcolm Maciver, Computational Neuroscientist, Northwestern University, 03/2017
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Agenda
II. Neuroscience Crash Course: Principles- Understanding the difference between a brain and a computer
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Study of the Structure of a Wing. Pen and ink "Dissect the bat(…) and on this model arrange the machine”. Leonardo daVinci (1452-1519), "Codice sul volo degli uccelli", 1505, Originalat the Biblioteca Reale, Turin, Italy.
On Artificial Brains and Bird Wings: An Analogy
Many sought to realize their dream to flyby mimicking birds. Man-craftedmachines that much resembled artificialbirds were envisaged, and in spite ofnumberless attempts pursued overcenturies the mystery of flight could notbe unravelled.
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How could we steer a robot with human-like bodydynamics?Can we give life to Cronos?
Example: Cronos humanoid robot in Shanghai
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Example: Roboy 2013
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Neuroscience Crash Course: Foundations
Neural Tissue
Neurites
Growth Cones
Synchronizity
Neural Networks
Neural Plasticity
Intelligence
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Neuroscience Crash Course: Foundations
Neural TissueNeurons are embedded in a chemical environment, where theinterplay between morphology, the environment and physicsdetermine circuitry and connectivity and thence neural dynamics.
Neurites
Growth Cones
Synchronizity
Neural Networks
Neural Plasticity
Intelligence
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Some Neuroscientific foundations
human brain
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Some Neuroscientific foundations
How does the brain structure emerge?
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the neuron
a single neuron: branching astrocyte
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the neuron
a single neuron: branching astrocyte (schematic)
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the neuron
a single neuron: branching astrocyte (schematic)
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the synapse
human braina single neuron: branching astrocytea single neuron: branching astrocyte (schematic)transmitter release at the synaptic cleft
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By the way – why are there gyri and sulci?
human brain
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Btw: How does the shape of the brain evolve?
human brain
Brain corals
Walnut
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Btw: How does the shape of the brain evolve?
How a brain gets ist shape: 02/2016, Harvard, Mahadevan Lab/Harvard SEAS
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Neuroscience Crash Course: Foundations
Neural Tissue
NeuritesThe observation that axons may grow and branch dependent on the dynamics of underlying micro-elements (microtubuli) promotes the implementation of local assembling strategies that are both interesting from a conceptual and computational viewpoint.
Growth Cones
Synchronizity
Neural Networks
Neural Plasticity
Intelligence
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Neuroscience Crash Course: Foundations
Neural Tissue
Neurites
The observation that axons may grow and branch dependent on the dynamics of underlying micro-elements (microtubuli) promotes the implementation of local assembling strategies that are both interesting from a conceptual and computational viewpoint.
Dendrites + Axons = Neurites
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Neuroscience Crash Course: Foundations
Neural Tissue
Neurites
Growth ConesThe scanning of the local environment for chemical gradients performed by the growth cone occurs ceaselessly and constitutes an interesting strategy that is widely applied in nature. By varying the sensitivity of surface receptors the growth cone may select distinct pathways embedded within a chemical environment.
Dendrites
Synchronizity
Neural Networks
Neural Plasticity
Intelligence
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Diploma Thesis: Growing an Intelligent Artficial Neural Network
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Motivation : biological Neurons
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Diploma Thesis: Growing an Intelligent Artficial Neural Network
The ant analogy: intelligent microtubuli ?
Kalil, K. et al. (2000) "Common Mechanisms Underlying Growth Cone Guidance and Axon Branching", J. Neurobio. 44:145-158.Penrose, R., (2000) "The Large, the Small and the Human Mind", UK, Cambridge University Press
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Developmental Strategies: Ant Analogy
Neuron (A) and three different ant raiding patterns of ant-colonies(B-D). Note the branching structures that are a result of eitherneural development and ant raiding patterns (adapted fromCamazine et al., 2001).
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Diploma Thesis: Growing an Intelligent Artficial Neural Network
- How does a neuron grow? - What are the underlying principles?- How does nervous tissue emerge?- How does it process information?- Where are the roots of Intelligence?
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Neuroscience Crash Course: Foundations
Neural Tissue
Neurites
Growth Cones
SynchronizityTononi and Edelman showed in simulations that fast changes in synaptic efficacy and spontaneous activity may rapidly establish a transient but globally coherent process. They then pointed out the possibility of solving the ‘binding problem’ by neuronal synchronization and thus neuronal coherence (Singer, 1995; Edelman et al, 2000).
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Neuroscience Crash Course: Foundations
Neural Tissue
Neurites
Growth Cones
Synchronizity
Neural NetworksNeural Plasticity
Intelligence
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First Artificial Neural Network –Frank Rosenblatt’s Perceptron (1957)
"the embryo of an electronic computer that [the Navy] expects will be able to walk, talk, see, write, reproduce itself and be conscious of its existence”
New York Times (1958)
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Artificial Neural Networks: Principle
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The last 60 years in ANN research…
input layer
hidden layer
output layer
Artificial Neural Network 1957
“The Perceptron”110 tons
+ 60 years of research+ Trillions $ of cash+ 1’000’000’000’000 x
computing power
2017 ?
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input layer
hidden layer
output layer
input layer
hidden layer 1
output layer
hidden layer 2
hidden layer X
Artificial Neural Network 1957
“The Perceptron”110 tons
Artificial Neural Network 2017
“Deep Learning”
The last 60 years in ANN research…
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150mio b.c. 1490
19032010 1896
1783
1670Artificial Flight: Developments in Aviation
Artificial Thinking: Development in AI “Deep Learning” (2017)
400 b.c.
Development in Aviation and AI: A comparison
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Neuroscience Crash Course: Foundations
Neural Tissue
Neurites
Growth Cones
Synchronizity
Neural Networks
Neural PlasticityIntelligence
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Longterm Potentiation (LTP)
no stimulation (3) weak stimulation (2) tetanic stimulation (1)
Problem:
Relating plasticity to behavior is very difficult: Behavioral studies cannot beperformed while precisely recording cellular responses. Any movement preventsaccurate electrophysiological recordings.
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Neurons are integrate and fire units (simplification)
…Neurons are integrate and Fire UnitsBiological foundations of perceptrons…
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VNT Neurons: Simple Integrate and Fire Units
Integrate and Fire Units… …underlying STDP
Right Panel: Spike Timing Dependent Plasticity as a universal learning rule? Adapted from L. F. Abbott andSacha B. Nelson (2000), “Synaptic plasticity: taming the beast“, Nature Neuroscience 2000.
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Longterm Potentiation (LTP)
no stimulation (3) weak stimulation (2) tetanic stimulation (1)
Problem:
Relating plasticity to behavior is very difficult: Behavioral studies cannot beperformed while precisely recording cellular responses. Any movement preventsaccurate electrophysiological recordings.
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Longterm Potentiation (LTP)
Axons 1, 2 and 3 (pre) transmitting signals to a pyramidal neuron (post).
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Longterm Potentiation (LTP)
B: Current applied to axons 1, 2 and 3.
-> equivalent post-synaptic spikes.
C: 3 different stimulation protocols applied.
-> equivalent post-synaptic spikes
D: After one hour application of same currentas at the beginning.
-> Potentiated, long-lasting response.
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VNT Neurons underlying Spike Timing Dependent Plasticity
VNT Integrate and Fire Units underlying STDP
Adapted from L. F. Abbott and Sacha B. Nelson (2000), “Synapticplasticity: taming the beast“, Nature Neuroscience 2000.
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Neuroscience Crash Course: Foundations
Neural Tissue
Neurites
Growth Cones
Synchronizity
Neural Networks
Neural Plasticity
Intelligence
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What is Artificial Intelligence?
HumanoidsArtificial BrainsTerminatorMatrixCyborgsSingularity
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AI Winter AI Spring
Artificial Intelligence
MachineLearning
Deep Learning
What is Artificial Intelligence?
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Silver, David, et al. "Mastering the game of Go with deep neural networks and tree search" Nature, (2016)
Lee Sedol vs. AlphaGo, 03/2016
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Artificial Neural Networks: Principle
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….your opinion ?
What is Intelligence?What is Intelligence?
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Playing chess
Professor Computer
What is Intelligence?
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Playing chess
What is Intelligence?
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What is Intelligence?
Playing chess
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- In a nutshell: “Intelligence requires a body”
Embodiment: Definition The concept that intelligence involves movement, a large variety of behaviors, adaptivity, skills andmuch more.
When Robots learn how to play soccer
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Rufus T. FireflyPeople involved: 2
“i-Robot” ?People involved: ?
1990 today 2050 ?
Roboy 2013People involved: 80+
The new Artificial Intelligence: Embodiment- 25 years of research at the AI Lab in Zürich
20151995 ?2010
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The Jaquet-Droz Automata, built anno 1770 inNeuchâtel, Switzerland
AI: How much have we progressed since 1770 ?
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Neuroscience Crash Course: You made it !
Neural Tissue
Neurites
Growth Cones
Synchronizity
Neural Networks
Neural Plasticity
Intelligence
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Agenda
III. Interfacing with brains: From neurointerface to artificial brains- What’s going on in neuroscience
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recording signals in nerve cells
recording neural signals: action potentials
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recording signals in nerve cells
high-precision glass electrode for electro-physiological recording of neural activity.
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recording signals in nerve cells
recording neural signals: action potentials
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triggering signals in nerve cells
triggering an action potential by depolarization.
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signal encoding in nerve cells
triggering a burst of action potentials
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visual input: sensory readingsconverted visual input: spikes
recorded output: neural spikes
converted output: motor commands
In collaboration with Mussa-Ivaldi, S., Alford, S., Sanguineti, V., Reger, B., Fleming. K., Kaufmann, P., 2001Northwestern University, University of Illinois, University of Genova, Swiss Federal Institute of Technology
Pascal [email protected]
Artificial Intelligence Laboratory, Sect. Neural InterfacingUniversity of Zurich, Switzerland
Neural Interfacing: Connecting Brains to Robots
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The lamprey: a brain to chat with
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The lamprey: a brain to chat with
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The lamprey: a brain to chat with
Lamprey Brain
Cat Brain
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Neurointerface: Experimental Setup
in vitro preparation converter (Matlab) Khepera
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Neurointerface: Experimental Setup
Core of the neuro-robotic interface: Stimulating electrodes and recording electrodes.The former deliver and the later sense electrical stimulation that was modified by thebrain of the lamprey sitting in the recording chamber.
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Neurointerface: Experimental Setup
abstracted network for predicting the lamprey‘s behavioral responses.
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Neurointerface: Experimental Setup
abstracted network for predicting the lamprey‘s behavioral responses.
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Neurinterfacing: the artificial behavior
1. The axons of the vestibular system are delivered artificial (visual) signals sensed by the robot’s eyes.
2. Instead of adjusting the body’s position in space, the lamprey controls a robot.
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Neurointerface: Experimental Setup
in vitro preparation converter (Matlab) Khepera
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Neurointerface: Experimental Setup
Khepera: star-guest from Switzerland
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Neurointerface: Experimental Setup
Khepera: star-guest from Switzerland
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Neurointerface: Theory versus Experiment
computer simulated behavior experimentally recorded behavior
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Neurointerface: Theory versus Experiment
positive phototaxis mixed taxis negative phototaxis
computer simulation
experimentally recorded behavior
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Working at the interface of computer simulations, neuroscience and robotics to tackle the phenomenon of intelligent behavior.
Growing Artificial Neural Tissue: What brings the Future ?
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84
How about today?
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There is still a long way to go ...
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...and where does that all lead to?
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Employee 2.0 – tapping corporate intelligence
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Employee 3.0 – incorporating technology
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Employee 4.0 – a lot of leisure time for humans
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Start
Thank you !
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