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Cortex modeling and cortex-inspired computation

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.

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Cortex modeling and cortex-inspired computation

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  1. Cortex modeling and cortex-inspired computation Anders Lansner Dept of Computational Biology KTH and Stockholm University

  2. Synopsis • Methods in neuronal network modeling • Large-scale cortex model example • Perspectives on modeling and brain-inspired computing Albanova Instrumentation Seminar

  3. Goals • 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 Albanova Instrumentation Seminar

  4. Cortical areas and microcircuits Albanova Instrumentation Seminar

  5. Advances in experimental neuroscience • Shortage of data, but rapid development… • E.g. genetic fluorescent marking + confocal tracing of pathways • Livet et al. Nature Nov 2007 Albanova Instrumentation Seminar

  6. Models at multiple levels • (Molecular dynamics) • Sub-cellular level models • Single neuron and synapse models • Microcircuits and networks • Full-scale global network models Albanova Instrumentation Seminar

  7. Types of neuron models • Summing threshold units • Connectionist model  neural network • Integrate-and-fire • Hodgkin-Huxley formalism Albanova Instrumentation Seminar

  8. Single cell models - signal processing • An equivalent electrical circuit model Albanova Instrumentation Seminar

  9. Ohm’s law: Nernst eqn: Equivalent electrical circuit of a membrane patch Albanova Instrumentation Seminar

  10. open closed  First-order kinetics yields:   K+ : p independent gating particles: The gate model”Hodgkin-Huxley model” Albanova Instrumentation Seminar

  11. The Hodgkin-Huxley current equation Albanova Instrumentation Seminar

  12. Nobel Prize 1963 An action potential Albanova Instrumentation Seminar

  13. Synaptic 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 • … Albanova Instrumentation Seminar

  14. Real neuronal networks • Several types of different neurons • Huge numbers • Modules and layers • Quite similar over areas and species! • Computing power limitation … Albanova Instrumentation Seminar

  15. Simulators and simulation oflarge-scale models at KTH • 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) Albanova Instrumentation Seminar

  16. A large-scale cortex model Albanova Instrumentation Seminar

  17. 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 • 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 Albanova Instrumentation Seminar

  18. 70% -1.5 mV mV 70% 1.2 mV 70% 2.5 mV 25% 2.4 mV 230% 0.30 mV 117% 2.5 mV The 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 Albanova Instrumentation Seminar

  19. Tsodyks, Uziel, Markram 2000 Neuron-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 Albanova Instrumentation Seminar

  20. Network layout • 1x1 mm patch • 9 hypercolumns • Each hypercolumn • 100 minicolumns • 100 basket cells • 100 patterns stored • 29700 neurons • 15 million synapses One of the 9 hypercolumns Active minicolumn (30 pyramidal cells) Active basket cell Active RSNP cells Albanova Instrumentation Seminar

  21. 9 hypercolumns • 1x1 mm patch • 9 hypercolumns • Each hypercolumn • 100 minicolumns • 100 basket cells • 100 patterns stored • 29700 neurons • 15 million synapses Albanova Instrumentation Seminar

  22. 100 hypercolumns • 330000 neurons • 161 million synapses  4x4 mm Albanova Instrumentation Seminar

  23. 8 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) Albanova Instrumentation Seminar

  24. The three different cell types3 sec simulation Pyramidal RSNP Basket Albanova Instrumentation Seminar

  25. 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 Albanova Instrumentation Seminar

  26. Perception and associative memory 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 Albanova Instrumentation Seminar

  27. Attractor dynamics:Pattern rivalry Fast ”decision” <100 ms! Albanova Instrumentation Seminar

  28. Log(pISI) 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. Bimodal membrane potential Albanova Instrumentation Seminar

  29. Bistable activity with irregular firing, similar to in vivo 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 Albanova Instrumentation Seminar

  30. Attentional blink – effect of GABA↑ • Attractor activation correlates with percentage of correct probe detections • Time scales different but qualitatively similar results Albanova Instrumentation Seminar

  31. Ongoing 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,… Albanova Instrumentation Seminar

  32. 100 ops/synapse/ms Computing PowerMoore’s law … ? Next generation supercomputers >1M cores IBM BlueGene/L 128K cores GFLOP year Albanova Instrumentation Seminar

  33. EU/FACETS – analog VLSIFrom cortex physiology to VLSI EU/GOSPEL – NoE in Artificial olfaction SSF/Stockholm Brain Institute (SBI) OECD/INCF – International Neuroinformatics Coordinating Facility Albanova Instrumentation Seminar

  34. Conclusions • 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…? Albanova Instrumentation Seminar

  35. Collaborators • 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 Albanova Instrumentation Seminar

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