Engineered and Artificial Engineered and Artificial SystemsSystems

Engineered and Artificial Engineered and Artificial SystemsSystems

Methodological and applied researchMethodological and applied research

Modelling of learning and perceptionModelling of learning and perception

• Bayesian Object Recognition (Jouko Lampinen)• Computational Neuroscience via Autonomous

Robotics (Harri Valpola)

Engineered nanosystems Engineered nanosystems

• Biosensing systems (Jukka Tulkki)

Engineered and Artificial Engineered and Artificial SystemsSystems

HierachicalProbabilistic

Models

Bayesian Object

Recognition

Computational Neurosciencevia Robotics

Nanosystems and applications

Modelling of learning and Modelling of learning and perceptionperception

Learning and perception is central issue in many Learning and perception is central issue in many research topics in LCEresearch topics in LCE

• Cohesive research in human and machine Cohesive research in human and machine perceptionperception

Modelling of learning and Modelling of learning and perceptionperception

Basic paradigm: perception is active prediction Basic paradigm: perception is active prediction processprocess

• Generative models / Bayesian inferenceGenerative models / Bayesian inference• Task oriented data driven processTask oriented data driven process• Attention vs. dual control Attention vs. dual control (Optimal control balancing (Optimal control balancing controlcontrol errors and estimation errors)errors and estimation errors)

State space State space modelmodel

PredictionPrediction Sensory inputSensory input

NoveltyNoveltyUpdateUpdate

ActionAction

Bayesian Object RecognitionBayesian Object Recognition

Perception as Bayesian Inference Perception as Bayesian Inference

perception = prior knowledge + sensory inputperception = prior knowledge + sensory input

Object matchingObject matching

• Sequential Monte CarloSequential Monte Carlo• Clutter, occlusions etcClutter, occlusions etc

View-point invarianceView-point invariance• 3D models / learnt views3D models / learnt views• SMC, PMC, MCMCSMC, PMC, MCMC

SegmentationSegmentation

• Data-driven MCMCData-driven MCMC• Multiple texture classesMultiple texture classes

Bayesian Object RecognitionBayesian Object Recognition

Matching a face with occlusion by Sequential MC Matching a face with occlusion by Sequential MC

Final match

Bayesian Object RecognitionBayesian Object Recognition

Sequential MC matching of 3D shape modelsSequential MC matching of 3D shape models

Modeling of Feature Variation due to 3D RotationsModeling of Feature Variation due to 3D Rotations

Bayesian Modelling of PerceptionBayesian Modelling of Perception

Research goals: Research goals:

• Efficient and scalable algorithmsEfficient and scalable algorithms• Expectation propagation Expectation propagation

• Particle Filters / MCMC with data driven proposals Particle Filters / MCMC with data driven proposals

• Computational models of the biological Computational models of the biological perceptionperception

• Hierarchical Bayesian inference in the visual cortex Hierarchical Bayesian inference in the visual cortex (following Mumford, Friston, etc) (following Mumford, Friston, etc)• Modelling adaptation of auditory system as dual control Modelling adaptation of auditory system as dual control process process

• Hierarchy of learning and perception Hierarchy of learning and perception • Category learning - from features to object classesCategory learning - from features to object classes• PerceptualPerceptual grouping processes grouping processes

Computational NeuroscienceComputational Neuroscience

System-level view to behaving and learning systems

We study how the components of a cognitive architecture interact

Machinelearning

System-levelcomputationalneuroscience

Cognitiveneuroscience

Complexnetworks

Bio-inspiredrobotics

GoalsGoals

• Build the mind of a complete Build the mind of a complete autonomous agentautonomous agent

• Study the development of mind in Study the development of mind in interaction with the environmentinteraction with the environment

Once we are done, the system:Once we are done, the system:

1.1. Learns abstract conceptsLearns abstract concepts2.2. Knows what it is doing and whyKnows what it is doing and why3.3. Has its own will and can make decisionHas its own will and can make decision4.4. Can imagine an planCan imagine an plan5.5. Has fine motor control and can navigateHas fine motor control and can navigate6.6. Can interact and communicate with Can interact and communicate with

othersothers

MethodsMethods

• Neural network simulationsNeural network simulations

• Real and simulated robotsReal and simulated robots

TimelineTimeline

2006

2007

2008

2009

2010

2011

•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

Collaboration inside LCECollaboration inside LCE

Complex networks and agent-based Complex networks and agent-based models:models:

• Competitive processes in networks Competitive processes in networks (non-equilibrium dynamics) (non-equilibrium dynamics)

• Adaptation of network weights and Adaptation of network weights and topologytopology

Cognitive systems:Cognitive systems:• Experimental research of attention Experimental research of attention

and perceptual learning and perceptual learning

External collaborationExternal collaboration

Machine Machine learning:learning:

Info-lab, TKKInfo-lab, TKK

Attention Attention modelling:modelling:

Deco’s group inDeco’s group inBarcelonaBarcelona

Robotics and comp.Robotics and comp.neuroscience:neuroscience:EU projects:EU projects:RobotCUBRobotCUB, ICEA, ICEA

Biosensing systems

Sebastian Köhler and Jukka Tulkki, a new project started 2006

Optical systems– Quantum dot fluorescent

labelling– Autofluorescence– Chemi-/bioluminescence– Fourier transform IR

spectroscopy– Holographic sensors– Surface plasmon

resonance– Surface-enhanced Raman

spectroscopy

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)Jani Oksanen and Jukka Tulkki, Apl. Phys. Lett. (2006)

Operation principle (1/3)

• A phase locked laser amplifier -1

0

1

Gai

n

Frequency

Mirrorloss

Input power

Out

put P

ower

Laser mode

Signal mode

Operation principle (2/3)

• Adding a coherent bias signal makes the output nonlinear: Pout=(P1

½ - Pbias½)2

Input powerO

utpu

t Pow

erPBIAS

P1

Operation principle (3/3)

• Combining two laser amplifiers makes a bistable system with two stable states

PBIAS

P1

PBIAS

P2

P1

Labilepoint

Stablepoint

P2

A schematic of the COFF

Filter out 2

Filter out 1

Filter out 3

2

1

Invertedoutput at 1

L1

L2

t b

t b

t a

t a

E B2 at 2

SET at 3

RESET at 3

Output at 2

E B1 at 1

Biomorphic networksBiomorphic networks

recorded cell voltage

stimulus

0.5 5 50 50010-2

10-1

100

101

Hz

Signal gain

1 10 1000,1

1

10

Information capacity

Frequency (Hz)

Bitts/s

S

D

G1

G2

G3

G4

Multigate SET schematically

T. Häyrynen and J.Tulkki, collaboration with M. Weckströn Univ. of OuluT. Häyrynen and J.Tulkki, collaboration with M. Weckströn Univ. of Ouluand J. ahopelto and M. Åberg VTT Microelectronics Centreand J. ahopelto and M. Åberg VTT Microelectronics Centre

Research of SET-basedResearch of SET-basedneural circuits with highneural circuits with highparallelism and low parallelism and low dissipationdissipation