Cortex modeling and cortex-inspired computation

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STOCKHOLM BRAIN INSTITUTE Cortex modeling and cortex- Cortex modeling and cortex- inspired computation inspired computation Anders Lansner Dept of Computational Biology KTH and Stockholm University

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Cortex modeling and cortex-inspired computation. Anders Lansner Dept of Computational Biology KTH and Stockholm University. Synopsis. Methods in neuronal network modeling Large-scale cortex model example Perspectives on modeling and brain-inspired computing. Goals. - PowerPoint PPT Presentation

Transcript of Cortex modeling and cortex-inspired computation

Page 1: Cortex modeling and cortex-inspired computation

STOCKHOLM BRAIN INSTITUTE

Cortex modeling and cortex-Cortex modeling and cortex-inspired computationinspired computation

Anders Lansner

Dept of Computational BiologyKTH and Stockholm University

Page 2: Cortex modeling and cortex-inspired computation

November 15, 2007 Albanova Instrumentation Seminar 2

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SynopsisSynopsis

• Methods in neuronal network modeling

• Large-scale cortex model example

• Perspectives on modeling and brain-inspired computing

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GoalsGoals

• Models of neurons and neuronal networks• 1985 - …• Today high demand from neuroscience labs• Enables understanding of the brain

• Brain-like/inspired algorithms and architectures• Beyond ”neural networks”, ”neurocomputing”• ”Artificial brains” … on silicon

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Cortical areas and microcircuitsCortical areas and microcircuits

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Advances in experimental Advances in experimental neuroscienceneuroscience

• Shortage of data, but rapid development…

• E.g. genetic fluorescent marking + confocal tracing of pathways

• Livet et al. Nature Nov 2007

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Models at multiple levelsModels at multiple levels

• (Molecular dynamics)• Sub-cellular level models• Single neuron and synapse models• Microcircuits and networks• Full-scale global network models

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Types of neuron modelsTypes of neuron models

• Summing threshold units• Connectionist model neural network• Integrate-and-fire• Hodgkin-Huxley formalism

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Single cell models - signal Single cell models - signal processingprocessing

• An equivalent electrical circuit model

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Equivalent electrical circuit of a Equivalent electrical circuit of a membrane membrane patchpatch

Ohm’s law:

Nernst eqn:

( )

ln

i i m i

outi

in

I g V E

CRTE

zF C

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The gate modelThe gate model”Hodgkin-Huxley model””Hodgkin-Huxley model”

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First-order kinetics yields:

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p independent gating particles: py

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The Hodgkin-Huxley current The Hodgkin-Huxley current equationequation

1,

1

,1

1

jj

jj

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jj

k

jkkj

m R

VV

R

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dt

dVC

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An action potentialAn action potential

Nobel Prize 1963

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Synaptic transmissionSynaptic transmission

• Simple conductance based model• Square pulse, Gamma function• Voltage dependence (NMDA)

• Detailed model of single spine• Postsynaptic receptor kinetics• Biochemical networks• Neuromodulation

• Electrical synapses• Graded transmitter release• Synaptic plasticity

• Short-term, ms - s• Long-term, s – yrs

• …

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Real neuronal networksReal neuronal networks

• Several types of different neurons• Huge numbers• Modules and layers

• Quite similar over areas and species!• Computing power limitation …

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• GENESIS• NEURON• SPLIT simulator

• Hammarlund & Ekeberg 1998

• SPLIT parallel setup, optimization• Djurfeldt et al. 2005

• PGENESIS, parallel NEURON• PDC/KTH

• Lenngren, KTH/PDC• Blue Gene/L

• 1024 dual core nodes (1/64 of full machine)

Simulators and simulation ofSimulators and simulation oflarge-scale models at KTHlarge-scale models at KTH

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A large-scale cortex modelA large-scale cortex model

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Hebbian synapses and cell assembliesHebbian synapses and cell assemblies

”LTP”Bliss and Lömo, 1973Levy and Steward, 1978

Hebb D O, 1949: The Organization of Behavior

• Cell assembly = mental object

• Gestalt perception• Perceptual completion• Figure-background separation• Perceptual rivalry

• Milner P: Lateral inhibition

• After activity 500 ms• Persistent, sustained• Fatigue = Adaptation, synaptic depression

• Association chains• Temporally asymmetric synaptic plasticity

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The KTH layer 2/3 modelThe KTH layer 2/3 model

• Top-down driven model of associative memory• 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

117% 2.5 mV 230% 0.30 mV

70% -1.5 mV mV

70% 1.2 mV

70% 2.5 mV

25% 2.4 mV

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Neuron-synapse propertiesNeuron-synapse properties

• Realistic amplitude of PSP:s in largest network model

• Sparse connectivity (stochastic)• Synaptic depression• Asymmetric cell-cell connectivity• 3D geometry delays

• 0.1 - 1m/s conduction speed

Tsodyks, Uziel, Markram 2000

Local basket cell

Local pyramidal

Local RSNP

Distant pyramidal

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Network layoutNetwork layout

• 1x1 mm patch• 9 hypercolumns• Each hypercolumn

• 100 minicolumns• 100 basket cells

• 100 patterns stored• 29700 neurons• 15 million synapses

Active minicolumn

(30 pyramidal cells)

Active basket cell

Active RSNP cells

One of the 9 hypercolumns

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9 hypercolumns9 hypercolumns

• 1x1 mm patch• 9 hypercolumns• Each hypercolumn

• 100 minicolumns• 100 basket cells

• 100 patterns stored• 29700 neurons• 15 million synapses

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100 hypercolumns100 hypercolumns

4x4 mm

• 330000 neurons

• 161 million synapses

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8 rack BG/L simulation8 rack BG/L simulation

• 22x22 mm cortical patch• 22 million cells, 11 billion synapses

• 8K nodes, co-processor mode• 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

• 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)

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The three different cell typesThe three different cell types3 sec simulation3 sec simulation

Basket

RSNP

Pyramidal

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• 2000+ neurons• 250000+ synapses• 5 s = 600 s on PC

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

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Perception and associative Perception and associative memory performancememory performance

• Pattern reconstruction• Figure-background• Pattern completion and rivalry• 50 – 100 ms

• Sustained after-activity• 150 ms – 2 sec• NMDACa, KCa modulation

• Robust to parameter changes and scaling• Cortical long-range recurrent excitation strong

enough to support attractor dynamics

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Attractor dynamics:Attractor dynamics:Pattern rivalryPattern rivalry

Fast ”decision” <100 ms!

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Bimodal membrane potentialBimodal membrane potential

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.

Log(pISI)

Exponential fit

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Bistable activity with irregular firing, Bistable activity with irregular firing, similar to similar to in vivoin vivo recordings recordings

• Ground state stable only in larger networks with many patterns stored

• Increase in irregularity in active cortical states is a challenges for persistent activity models

• This L2/3 network model• displays irregular fluctuation driven low-rate firing• operates in a high-conductance regime of balanced

excitatory and inhibitory currents• is stable to synchronization even with blocked NMDAR

• Details under investigation

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Attentional blink – effect of GABAAttentional blink – effect of GABA↑↑

• Attractor activation correlates with percentage of correct probe detections

• Time scales different but qualitatively similar results

0 40 80 120 160 200 240 280 3200

20

40

60

80

100

milliseconds

% a

cti

va

ted

att

rac

tors

GABA baseline

GABA 150%

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Ongoing workOngoing work+ Layer 4

• Selective feature detectors• V1 model with

• learned orientation map (LISSOM) patchy horizontal L2/3 connectivity

+ Layer 5• Martinotti cells, local (delayed) inhibition to superficial

layers• Pyramidals, cortico-cortical connections

• Analysing L2/3 dynamics, spiking statistics, conductances, intracellular potentials• Non-orthogonal stored memories

• Better synthetic VSD, BOLD signals• Modelling interacting areas … using parallel NEURON• Scalable abstract connectionist cortex model

• Cortical area module, on-line learning, network-of-networks,…

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1980 1985 1990 1995 2000 2005 2010 2015 2020 2025

Computing PowerComputing PowerMoore’s law …Moore’s law …

year

GF

LO

P

IBM BlueGene/L 128K cores

Next generation supercomputers

>1M cores

100 ops/synapse/ms?

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EU/FACETS – analog VLSIEU/FACETS – analog VLSI From cortex physiology to VLSIFrom cortex physiology to VLSI

EU/GOSPEL – NoE in Artificial olfactionSSF/Stockholm Brain Institute (SBI)OECD/INCF – International Neuroinformatics Coordinating Facility

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ConclusionsConclusions

• Computational models are enabling tools in brain science

• Human brain level computing power in 10-15 yrs

• Brain mysteries likely to be largely uncovered at that time

• A principled understanding of brain function will emerge• Great benefits!

• Brain-like computing and AI• Consequences for society…?

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CollaboratorsCollaborators

• Model development• Mikael Lundqvist, PhD student

• David Silverstein, Phd student

• Parallel simulation• Mikael Djurfeldt , PhD student

• Örjan Ekeberg, Assoc Prof

• Data analysis• Martin Rehn , postdoc