Computational Neuroscience Introduction Day
Transcript of Computational Neuroscience Introduction Day
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Computational Neuroscience Introduction Day
• 14.00 Introduction
• 14.30 Computational Neuroscience Groups in Paris
• 15.00 Discussion of papers in groups: Questions
• 15.45 Break
• 16.00 Discussion of papers in groups: Answers
• 16.45 Presentation of Answers
• 17.30 Concluding comments
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A brief introduction to Computational Neuroscience
Christian MachensGroup for Neural Theory
Ecole normale supérieure Paris
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What’s the brain good for?Tree
no neurons
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What’s the brain good for?
C. elegans302 neurons
Treeno neurons
brains generate motion( = behavior)
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What’s the brain good for?
C. elegans302 neurons
Fly1 000 000
Treeno neurons
more complex brains generate a greater variety of behaviors
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What’s the brain good for?
C. elegans302 neurons
Human100 000 000 000
Rat1 000 000 000
Fly1 000 000
Treeno neurons
more complex brains generate a greater variety of behaviors
more complex brainscan learn more behaviors
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Molecules 1 nm
What’s the brain made of?
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Neurons
Synapses
Molecules 1 nm
1 μm
100 μm
What’s the brain made of?
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Maps
Networks
Neurons
Synapses
Molecules 1 nm
1 μm
100 μm
1 mm
1 cm
What’s the brain made of?
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CNS
Systems
Maps
Networks
Neurons
Synapses
Molecules 1 nm
1 μm
100 μm
1 mm
1 cm
10 cm
1 m
What’s the brain made of?
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How does the brain work?
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A physics/engineering approach
Just rebuild the whole thing
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CNS
Systems
Maps
Networks
Neurons
Synapses
Molecules 1 nm
1 μm
100 μm
1 mm
1 cm
10 cm
1 m
The quest for mechanisms:Constructing systems from parts
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CNS
Systems
Maps
Networks
Neurons
Synapses
Molecules 1 nm
1 μm
100 μm
1 mm
1 cm
10 cm
1 m
The quest for mechanisms:Constructing systems from parts
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Biophysics of the membrane voltage:The Hodgkin-Huxley Model
voltage
timeTuesday, September 14, 2010
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Reconstructing neurons:Ralls’ cable theory and compartmental modeling
Detailed compartmental models of single neurons:Large-scale differential equation models
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Reconstructing neuronsSimulating the membrane potential
Llinas & Sugimori (1980)Tuesday, September 14, 2010
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CNS
Systems
Maps
Networks
Neurons
Synapses
Molecules 1 nm
1 μm
100 μm
1 mm
1 cm
10 cm
1 m
The quest for mechanisms:Constructing systems from parts
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Reconstructing circuitsSerial Blockface Scanning Electron Microscopy
courtesy of W.DenkTuesday, September 14, 2010
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Scan brain slices and reconstruct the circuit...
but: the devil is in the details and when it comes to connectivity, details matter!
Reconstructing circuitsThe connectome
http://connectomes.org/Tuesday, September 14, 2010
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Theory of neural networks
r1 r2 r3 rN
Neurons, synapses network activity
ri = −ri + f(N∑
j=1
wijrj + Ii)
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Network dynamicslargely determined by connectivity
Possible dynamics:- stable/ unstable fixed points- limit cycles- chaotic attractors
For N → ∞
- neural networks can compute anything
Note: different attractors can co-existin different parts of the state space!
ri = −ri + f(N∑
j=1
wijrj + Ii)
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(Statistical) theory of neural networks
r1 r2 r3 rN
Neurons, synapses network activity
- only fixed points- synchronous activity- asynchronous activity- Poisson spike trains - oscillations- spatial patterns- ...
Under what conditionsdo you get
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CNS
Systems
Maps
Networks
Neurons
Synapses
Molecules 1 nm
1 μm
100 μm
1 mm
1 cm
10 cm
1 m
The quest for mechanisms:Constructing systems from parts
Tuesday, September 14, 2010
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Connectionist models:From networks to behavior
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A computer science approach
Study the computational problems
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Computation:manipulating information
sound pressure wave
cochleogram(time-frequency
representation of sound)
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Language The other day, I heard this cool jazz CD with this drummer...
Example music:
sheet notes
CD
Sound
Representation of information,more or less lossy
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Why represent information differently?
Example numbers:
XXIII2300010111
Roman SystemDecimal SystemBinary System
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Representations allow for easier algorithms
Example numbers:
XXIII2300010111
in ...?in multiples of 10in multiples of 2
Can you add these numbers?
29+ 33----
XXIX+ XXXIII--------
00011101+ 00100001----------
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29+ 33---- 62
Representations allow for easier algorithms
Example numbers:
XXIII2300010111
in ...?in multiples of 10in multiples of 2
Can you add these numbers?
XXIX+ XXXIII--------
00011101+ 00100001----------
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00011101+ 00100001---------- 00111110
29+ 33---- 62
Representations allow for easier algorithms
Example numbers:
XXIII2300010111
in ...?in multiples of 10in multiples of 2
Can you add these numbers?
XXIX+ XXXIII--------
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Representations can ease certain computations
Example numbers:
XXIII2300010111
in ...?in multiples of 10in multiples of 2
Can you add these numbers?
29+ 33---- 62
XXIX+ XXXIII--------
00011101+ 00100001---------- 00111110
easy
easy
difficu
lt
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Most famous example:“edge detectors” in visual system
Stimulus: black bar
Activity ofa neuron in V1
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Another famous example: Place cells in the hippocampus
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Studying representations in the brain
Experimental work Theoretical work
- perceptual representations: vision, audition, olfaction, etc.- representation of motor variables- “higher-order” representations: decisions short-term memory rewards dreams uncertainty ... you name it ...
- Quantifying information content quest for the neural code, information theory, discriminability, ...- Understanding the computational problems: object recognition, sound recognition, reward maximization
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What we understand now
very little
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What we understand now
very little
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What we need
● biologists
- to probe the brains of animals and humans- to design and carry out clever experiments- to investigate and quantify human and animal behavior
● psychologists
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What we need
r1 = −r1 + f(
N∑
j=1
w1jrj + E1
)
r2 = −r2 + f(
N∑
j=1
w2jrj + E2
)
● physicists, computer scientists, engineers, etc.
- to formulate mathematical theories of information processing- to create biophysical models of neural networks
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Teaching in the Cogmaster
Computational Neuroscience
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R Brette, R da Silveira, S Deneve, B Gutkin, V Hakim, C MachensTuesday, 17-18.30, Salle Seminaire DEC (29, rue d’Ulm, RdC)
Core Classes
CO6 Introduction to Comput. Neuroscience
AT2 Atelier Comput. Neuroscience
CA6(a) Theoretical Neuroscience
CA6(b) Seminar in Quantitative Neuroscience
S2
S1JP Nadal, N Brunel, R Brette, G MongilloThursday, 14-17, ???,
V Benichoux, R Brette, C MachensTuesday, 17-19
C MachensMonday, 10-12
START: 5 Oct
START:
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Many more classes available!!
see cogmaster website!!contact us!!
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Computational Neuroscience Research in the Cogmaster and Beyond
ENS: Group for Neural Theory (Sophie Deneve, Boris Gutkin, Christian Machens, ...)ENS: Equipe Audition (Romain Brette, Victor Benichoux, ...)ENS: Laboratoire de Physique Statistique (Jean-Pierre Nadal, Vincent Hakim, ...)Paris V: Laboratoire de Neurophysique et Physiologie (Nicolas Brunel, David Hansel, ... )
you can find more labs under:
for internship / stages / Master’s thesis: contact the faculty! (email etc.)
http://neurocomp.risc.cnrs.frhttp://cogmaster.net
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The articles you have read:
WT Newsome, KH Britten, JA MovshonNeuronal correlates of a perceptual decision
Neural coding
Reinforcement LearningW Schultz, P Dayan, PR MontagueA neural substrate of prediction and reward
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The Quest for the Neural Code
how is information represented in the brain?Maybe it’s the timing of spikes, rather than their average count (firing rate) that actually carries the information!
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The Quest for the Neural Code
how is information represented in the brain?
how much information does the population contain?Population codes are complicated because you cannot just add the information fromdifferent neurons if these are correlated (if they carry redundant information)
Maybe it’s the timing of spikes, rather than their average count (firing rate) that actually carries the information!
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The Quest for the Neural Code
how is information represented in the brain?
on what time scales is information represented?
how much information does the population contain?
In the article, stimuli are 2 sec long! But monkeys (and humans) integrate motionover much shorter time scales (100s of millisec) - then each neuron contributes less info!
Population codes are complicated because you cannot just add the information fromdifferent neurons if these are correlated (if they carry redundant information)
Maybe it’s the timing of spikes, rather than their average count (firing rate) that actually carries the information!
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Psychology ofAnimal Learning
Edward Thorndike(1874-1949)
How behaviors are learned
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Optimal ControlTheory
Richard Bellman(1920-1984)
Psychology ofAnimal Learning
Edward Thorndike(1874-1949)
How behaviors are learned
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Harry Klopf(1927-???)
Optimal ControlTheory
Artificial Intelligence(Machine Learning)
Richard Bellman(1920-1984)
Psychology ofAnimal Learning
Edward Thorndike(1874-1949)
Marvin Minsky(1927-???)
How behaviors are learned
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Harry Klopf(1927-???)
Optimal ControlTheory
Richard Bellman(1920-1984)
Psychology ofAnimal Learning
Edward Thorndike(1874-1949)
Marvin Minsky(1927-???)
ReinforcementLearning
Richard Sutton(1956-???)
Andrew Barto(1948-???)
Artificial Intelligence(Machine Learning)
How behaviors are learned
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In case you are interested...
9th Computational Neuroscience Day, Wed
"Modeling Network Dynamics"
Salle FessardInstitut de Neurobiologie Alfred FessardCNRS, Bat 331 Avenue de la Terrasse,91190 Gif sur Yvette
Tuesday, September 14, 2010