Transcript Biomimetic signal processing
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Cortex modeling and cortex inspired computation
Anders Lansner Dept of Computational Biology KTH and Stockholm University
Synopsis
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Methods in neuronal network modeling
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Large-scale cortex model example
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Perspectives on modeling and brain inspired computing
November 15, 2007 Albanova Instrumentation Seminar 2
Goals
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Models of neurons and neuronal networks
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1985 … Today high demand from neuroscience labs Enables understanding of the brain Brain-like/inspired algorithms and architectures
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Beyond ”neural networks”, ”neurocomputing”
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”Artificial brains” … on silicon November 15, 2007 Albanova Instrumentation Seminar 3
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Cortical areas and microcircuits
November 15, 2007 Albanova Instrumentation Seminar 4
Advances in experimental neuroscience
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Shortage of data, but rapid development…
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E.g. genetic fluorescent marking + confocal tracing of pathways
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Livet et al. Nature Nov 2007 November 15, 2007 Albanova Instrumentation Seminar 5
Models at multiple levels
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(Molecular dynamics) Sub-cellular level models Single neuron and synapse models Microcircuits and networks Full-scale global network models November 15, 2007 Albanova Instrumentation Seminar 6
Types of neuron models
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Summing threshold units Connectionist model
neural network
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Integrate-and-fire
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Hodgkin-Huxley formalism November 15, 2007 Albanova Instrumentation Seminar 7
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Single cell models - signal processing
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An equivalent electrical circuit model November 15, 2007 Albanova Instrumentation Seminar 8
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Equivalent electrical circuit of a membrane patch
Ohm’s law: Nernst eqn:
I i
i m
E i
)
E i
RT zF
ln
C out C in
November 15, 2007 Albanova Instrumentation Seminar 9
The gate model
”Hodgkin-Huxley model”
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y
closed 1
y
First-order kinetics yields:
dy
1
y
y dt y
dy
y
dt
y
y
1
p
independent gating particles: K + :
n
(
V
) 0 .
01
V
e
V
10 10 10 1
n
(
V
)
V
0 .
125
e
80
y p
November 15, 2007 Albanova Instrumentation Seminar 10
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The Hodgkin-Huxley current equation
November 15, 2007
C m dV j dt
k G k
E k
V j
V j
1
V j R j
1 ,
j
V j
1
R j
,
V j j
1
Albanova Instrumentation Seminar 11
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An action potential
Nobel Prize 1963
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Synaptic transmission
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Simple conductance based model
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Square pulse, Gamma function Voltage dependence (NMDA) Detailed model of single spine
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Postsynaptic receptor kinetics
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Biochemical networks Neuromodulation Electrical synapses Graded transmitter release Synaptic plasticity
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Short-term, ms - s
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Long-term, s – yrs … November 15, 2007 Albanova Instrumentation Seminar 13
Real neuronal networks
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Several types of different neurons Huge numbers Modules and layers
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Quite similar over areas and species!
Computing power limitation … November 15, 2007 Albanova Instrumentation Seminar 14
Simulators and simulation of large-scale models at KTH
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GENESIS
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NEURON
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SPLIT simulator
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Hammarlund & Ekeberg 1998
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SPLIT parallel setup, optimization
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Djurfeldt et al. 2005
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PGENESIS, parallel NEURON PDC/KTH
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Lenngren, KTH/PDC Blue Gene/L
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1024 dual core nodes (1/64 of full machine) November 15, 2007 Albanova Instrumentation Seminar 15
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A large-scale cortex model
November 15, 2007 Albanova Instrumentation Seminar 16
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Hebbian synapses and cell assemblies Hebb D O, 1949: The Organization of Behavior
”LTP” Bliss and Lömo, 1973 Levy and Steward, 1978 • • • •
Cell assembly = mental object Gestalt perception
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Perceptual completion Figure-background separation
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Perceptual rivalry
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Milner P: Lateral inhibition After activity
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500 ms Persistent, sustained
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Fatigue = Adaptation, synaptic depression Association chains
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Temporally asymmetric synaptic plasticity Albanova Instrumentation Seminar 17 November 15, 2007
The KTH layer 2/3 model
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70% -1.5 mV mV 70% 1.2 mV 70% 2.5 mV 25% 2.4 mV November 15, 2007
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30% 0.30 mV
1
17% 2.5 mV Top-down driven model of associative memory
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Generic “association cortex”, layers 2/3 Modular: Minicolumns, hypercolumns 3 different cell types: Pyramidal cells, Basket cells, Regular Spiking Non-Pyramidal 2 000 – 20 000 000 model neurons Albanova Instrumentation Seminar 18
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Local RSNP Distant pyramidal
Neuron-synapse properties
Local basket cell November 15, 2007 Local pyramidal Tsodyks, Uziel, Markram 2000
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Realistic amplitude of PSP:s in largest network model Sparse connectivity (stochastic) Synaptic depression Asymmetric cell-cell connectivity 3D geometry
delays
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0.1 - 1m/s conduction speed Albanova Instrumentation Seminar 19
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One of the 9 hypercolumns Active minicolumn (30 pyramidal cells) Active basket cell Active RSNP cells
Network layout
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1x1 mm patch 9 hypercolumns Each hypercolumn
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100 minicolumns 100 basket cells 100 patterns stored 29700 neurons 15 million synapses November 15, 2007 Albanova Instrumentation Seminar 20
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9 hypercolumns
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1x1 mm patch 9 hypercolumns Each hypercolumn
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100 minicolumns 100 basket cells 100 patterns stored 29700 neurons 15 million synapses November 15, 2007 Albanova Instrumentation Seminar 21
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100 hypercolumns
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330000 neurons 161 million synapses
4x4 mm
22 November 15, 2007 Albanova Instrumentation Seminar
8 rack BG/L simulation
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22x22 mm cortical patch
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22 million cells, 11 billion synapses 8K nodes, co-processor mode
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used 360 MB memory/node Setup time = 6927 s Simulation time = 1 s in 5942 s >29000 cpu hours Massive amounts of output data 77 % of linear speedup
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Point-point communication slows (?) Currently (inofficial) world record!
Djurfeldt M, Lundqvist M, Johansson C, Rehn M, Ekeberg Ö, and Lansner A (2007): Brain-scale simulation of the neocortex on the Blue Gene/L supercomputer. IBM J R&D (in press) November 15, 2007 Albanova Instrumentation Seminar 23
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The three different cell types
3 sec simulation Pyramidal RSNP Basket 24 November 15, 2007 Albanova Instrumentation Seminar
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2000+ neurons 250000+ synapses 5 s = 600 s on PC November 15, 2007 Lundqvist M, Rehn M, Djurfeldt M and Lansner A (2006). Attractor dynamics in a modular network model of the neocortex. Network: Computation in Neural Systems: 17, 253-276 Albanova Instrumentation Seminar 25
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Perception and associative memory performance
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Pattern reconstruction
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Figure-background Pattern completion and rivalry 50 – 100 ms Sustained after-activity
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150 ms – 2 sec NMDA Ca , K Ca modulation Robust to parameter changes and scaling Cortical long-range recurrent excitation strong enough to support attractor dynamics November 15, 2007 Albanova Instrumentation Seminar 26
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Attractor dynamics:
Pattern rivalry November 15, 2007 Fast ”decision” <100 ms!
Albanova Instrumentation Seminar 27
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Bimodal membrane potential
Log(p ISI ) Exponential fit Jeffrey Anderson, Ilan Lampl, Iva Reichova, Matteo Carandini, and David Ferster. Stimulus dependence of two-state fluctuations of membrane potential in cat visual cortex.
Nat. Neurosci.,
3(6):617 – 621, 2000.
November 15, 2007 Albanova Instrumentation Seminar 28
Bistable activity with irregular firing, similar to in vivo recordings
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Ground state stable only in larger networks with many patterns stored
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Increase in irregularity in active cortical states is a challenges for persistent activity models
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This L2/3 network model
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displays irregular fluctuation driven low-rate firing operates in a high-conductance regime of balanced excitatory and inhibitory currents
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is stable to synchronization even with blocked NMDAR
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Details under investigation November 15, 2007 Albanova Instrumentation Seminar 29
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Attentional blink – effect of GABA
↑ GABA baseline GABA 150%
100 80 60 40 20 0 0 40 80 120 160 200
milliseconds
240 280 320 • •
Attractor activation correlates with percentage of correct probe detections Time scales different but qualitatively similar results November 15, 2007 Albanova Instrumentation Seminar 30
Ongoing work
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Layer 4
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Selective feature detectors
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V1 model with
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learned orientation map (LISSOM)
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patchy horizontal L2/3 connectivity Layer 5
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Martinotti cells, local (delayed) inhibition to superficial layers
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Pyramidals, cortico-cortical connections Analysing L2/3 dynamics, spiking statistics, conductances, intracellular potentials
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Non-orthogonal stored memories Better synthetic VSD, BOLD signals Modelling interacting areas … using parallel NEURON Scalable abstract connectionist cortex model
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Cortical area module, on-line learning, network-of networks,… November 15, 2007 Albanova Instrumentation Seminar 31
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Computing Power
Moore’s law …
1E +1 0 1E +0 9 1E +0 8 1E +0 7 1E +0 6 1E +0 5 10 00 0 10 00 100 10 1 1980 0, 1 100 ops/synapse/ms
IBM BlueGene/L 128K cores
1985 1990 1995 2000 2005 2010 2015 2020 2025 year
?
Next generation supercomputers >1M cores November 15, 2007 Albanova Instrumentation Seminar 32
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EU/FACETS – analog VLSI
From cortex physiology to VLSI EU/GOSPEL – NoE in Artificial olfaction SSF/Stockholm Brain Institute (SBI) OECD/INCF – International Neuroinformatics Coordinating Facility November 15, 2007 Albanova Instrumentation Seminar 33
Conclusions
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Computational models are enabling tools in brain science
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Human brain level computing power in 10-15 yrs Brain mysteries likely to be largely uncovered at that time
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A principled understanding of brain function will emerge
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Great benefits!
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Brain-like computing and AI
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Consequences for society…?
November 15, 2007 Albanova Instrumentation Seminar 34
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Collaborators
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Model development
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Mikael Lundqvist, PhD student
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David Silverstein, Phd student Parallel simulation
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Mikael Djurfeldt , PhD student
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Örjan Ekeberg, Assoc Prof Data analysis
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Martin Rehn , postdoc November 15, 2007 Albanova Instrumentation Seminar 35