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Engineered and Artificial Systems

Engineered and Artificial Systems. Engineered and Artificial Systems. Methodological and applied research Modelling of learning and perception Bayesian Object Recognition (Jouko Lampinen) Computational Neuroscience via Autonomous Robotics (Harri Valpola) Engineered nanosystems

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Engineered and Artificial Systems

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  1. Engineered and Artificial Systems

  2. Engineered and Artificial Systems • Methodological and applied research • Modelling of learning and perception • Bayesian Object Recognition (Jouko Lampinen) • Computational Neuroscience via Autonomous Robotics (Harri Valpola) • Engineered nanosystems • Biosensing systems (Jukka Tulkki)

  3. HierachicalProbabilisticModels Bayesian Object Recognition Computational Neuroscience via Robotics Nanosystems and applications Engineered and Artificial Systems

  4. Modelling of learning and perception • Learning and perception is central issue in many research topics in LCE • Cohesive research in human and machine perception

  5. Modelling of learning and perception Basic paradigm: perception is active prediction process Action Prediction Sensory input State space model Update Novelty • Generative models / Bayesian inference • Task oriented data driven process • Attention vs. dual control (Optimal control balancing control • errors and estimation errors)

  6. Bayesian Object Recognition Perception as Bayesian Inference perception = prior knowledge + sensory input • Object matching • Sequential Monte Carlo • Clutter, occlusions etc • View-point invariance • 3D models / learnt views • SMC, PMC, MCMC • Segmentation • Data-driven MCMC • Multiple texture classes

  7. Final match Bayesian Object Recognition Matching a face with occlusion by Sequential MC

  8. Bayesian Object Recognition Modeling of Feature Variation due to 3D Rotations Sequential MC matching of 3D shape models

  9. Bayesian Modelling of Perception • Research goals: • Efficient and scalable algorithms • Expectation propagation • Particle Filters / MCMC with data driven proposals • Computational models of the biological perception • Hierarchical Bayesian inference in the visual cortex (following Mumford, Friston, etc) • Modelling adaptation of auditory system as dual control process • Hierarchy of learning and perception • Category learning - from features to object classes • Perceptual grouping processes

  10. Cognitive neuroscience Bio-inspired robotics Complex networks Machine learning We study how the components of a cognitive architecture interact System-level view to behaving and learning systems System-level computational neuroscience Computational Neuroscience

  11. Build the mind of a complete autonomous agent • Study the development of mind in interaction with the environment Once we are done, the system: Learns abstract concepts Knows what it is doing and why Has its own will and can make decision Can imagine an plan Has fine motor control and can navigate Can interact and communicate with others Goals

  12. Neural network simulations • Real and simulated robots Methods

  13. Timeline • Adaptive motor control • Learn abstract features serving behavioral goals • Combine attention and learning • Reward-based learning of orienting behaviors • Imagination • Navigation • Planning • Episodic memory • Communication and language 2006 2007 2008 2009 2010 2011

  14. Collaboration inside LCE Complex networks and agent-based models: • Competitive processes in networks (non-equilibrium dynamics) • Adaptation of network weights and topology Cognitive systems: • Experimental research of attention and perceptual learning

  15. External collaboration Attention modelling: Deco’s group in Barcelona Robotics and comp. neuroscience: EU projects: RobotCUB, ICEA Machine learning: Info-lab, TKK

  16. Biosensing systems Optical systems • Quantum dot fluorescent labelling • Autofluorescence • Chemi-/bioluminescence • Fourier transform IR spectroscopy • Holographic sensors • Surface plasmon resonance • Surface-enhanced Raman spectroscopy Sebastian Köhler and Jukka Tulkki, a new project started 2006

  17. Coherent optical flip-flop (COFF) • A bistable system of phase locked lasers • Nonlinear feedback obtained through interference of coherent signals • Meets most of the critical requirements of an integratable flip-flop • Logic gates with small modifications Jani Oksanen and Jukka Tulkki, Apl. Phys. Lett. (2006)

  18. Operation principle (1/3) • A phase locked laser amplifier

  19. Operation principle (2/3) • Adding a coherent bias signal makes the output nonlinear: Pout=(P1½ - Pbias½)2

  20. Operation principle (3/3) • Combining two laser amplifiers makes a bistable system with two stable states

  21. A schematic of the COFF

  22. Information capacity Bitts/s stimulus 10 Signal gain 1 1 10 recorded cell voltage 0 10 0,1 1 10 100 Frequency (Hz) -1 10 -2 10 0.5 5 50 500 Hz Biomorphic networks T. Häyrynen and J.Tulkki, collaboration with M. Weckströn Univ. of Oulu and J. ahopelto and M. Åberg VTT Microelectronics Centre Research of SET-based neural circuits with high parallelism and low dissipation S G1 G3 G2 G4 D Multigate SET schematically

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