Project funded by the Future and Emerging Technologies arm of the IST Programme FET-Open scheme IST-2000-26463 ~ INPRO (Information Processing by Natural.

Slides:

Advertisements

Similar presentations

Introduction to Neural Networks 2. Overview  The McCulloch-Pitts neuron  Pattern space  Limitations  Learning.

Advertisements

Vision, Hearing, and Smell: the Best-Known Senses

Analog VLSI Neural Circuits CS599 – computational architectures in biological vision.

Presentation for KoKom Bergen, Norway 10 August 2005 Helsinki University of Technology (HUT) HUT and User Studies Presented by: Delia Berrouard

Image Segmentation by Complex-Valued Units Cornelius Weber and Stefan Wermter Hybrid Intelligent Systems School of Computing and Technology University.

Neural Cross Correlation For Radio Astronomy Chipo N Ngongoni Supervisor: Professor J Tapson Department of Electrical Engineering, University of Cape Town.

Project funded by the Future and Emerging Technologies arm of the IST Programme NEUMIC Project No. IST Neurons and Modified CMOS Integrated.

Brian Merrick CS498 Seminar.  Introduction to Neural Networks  Types of Neural Networks  Neural Networks with Pattern Recognition  Applications.

FP6 Thematic Priority 2: Information Society Technologies Dr. Neil T. M. Hamilton Executive Director.

1 Part I Artificial Neural Networks Sofia Nikitaki.

Artificial Neural Networks URI BME Aleksey Gladkov.

COGNITIVE NEUROSCIENCE

Project funded by the Future and Emerging Technologies arm of the IST Programme FET-Open scheme Neural Robot Control Cornelius Weber Hybrid Intelligent.

Introduction to Neural Network Justin Jansen December 9 th 2002.

2007Theo Schouten1 Introduction. 2007Theo Schouten2 Human Eye Cones, Rods Reaction time: 0.1 sec (enough for transferring 100 nerve.

Alan L. Yuille. UCLA. Dept. Statistics and Psychology. Neural Prosthetic: Mind Reading. STATS 19 SEM Talk 6. Neural.

MIRROR Project Review IST Brussels – December 1 st, 2003.

Cognitive Neuroscience How do we connect cognitive processes in the mind with physical processes in the brain?

Neural Networks Lab 5. What Is Neural Networks? Neural networks are composed of simple elements( Neurons) operating in parallel. Neural networks are composed.

Future Brain Professor Keith Kendrick. Future Brain.

Traffic Sign Recognition Using Artificial Neural Network Radi Bekker

David Willshaw Institute for Adaptive & Neural Computation School of Informatics University of Edinburgh UK INCF Neuroinformatics.

Microwaves and Fiber Optics Lab of the Institute of Communications and Computer Systems (MFOL/ICCS) Athens – IST event 2/6/2003.

Artificial Neural Nets and AI Connectionism Sub symbolic reasoning.

The Von Neumann Model The approach to solving problems is to process a set of instructions and data stored in locations. The instructions are processed.

Eduard Petlenkov, Associate Professor, TUT Department of Computer Control

Pulsed Neural Networks Neil E. Cotter ECE Department University of Utah.

Explorations in Neural Networks Tianhui Cai Period 3.

IST – OMNIVIEWS Omnidirectional Visual System Final Review September 27-28, 2001 Lisbon Final Review September 27-28, 2001 Lisbon.

Project funded by the Future and Emergent Technologies arm of the IST Programme - FET Keyaction Life-like perception IST CICADA Cricket Inspired.

Micro and Nanosystems IST Programme Call Micro/nano based subsystems Francisco J. Ibáñez European Commission

Project funded by the Future and Emerging Technologies arm of the IST Programme FET K.A. line Networks of excellence and working groups the European.

Introduction to Machine Learning Instructor Shie-Jue Lee ( 李錫智 )

Project ArteSImit Artefact Structural Learning through Imitation (TU München, U Parma, U Tübingen, U Minho, KU Nijmegen) Giorgio Panin - TUM.

Project funded by the Future and Emerging Technologies arm of the IST Programme FET-Open scheme Project funded by the Future and Emerging Technologies.

1 Direction scientifique Networks of Excellence objectives  Reinforce or strengthen scientific and technological excellence on a given research topic.

Project funded by the Future and Emerging Technologies arm of the IST Programme FET-Open scheme Project funded by the Future and Emerging Technologies.

Neural Networks Steven Le. Overview Introduction Architectures Learning Techniques Advantages Applications.

Neural Networks in Computer Science n CS/PY 231 Lab Presentation # 1 n January 14, 2005 n Mount Union College.

Introduction: Olfactory Physiology Organic Chemistry Signal Processing Pattern Recognition Computational Learning Electronic Nose Chemical Sensors.

Neural mechanism for empathy YINGYING HAN SOCIAL BRAIN LAB- RODENT TEAM.

MANISH GUPTA. Presentation Outline Introduction Motivation Content Expected Impact Funding Schemes & Budget.

WISE Workflow Based Internet Services Jonel Rabara Trixia Paula Narvasa.

Information Society Technologies CPA 10 “Next generation micro & nano technologies” in WP 2001, call seven DG Information Society European Commission.

DMA 2 KAN Data-flexible multipurpose automated adaptive (k)omplex ANN (or autonomous ANN: A2N2) Neuronal Diversity Why important and prevalent? E.g. aspects:

Future and Emerging Technologies (FET) Future and Emerging Technologies (FET) The roots of innovation Proactive initiative on: Global Computing (GC) Proactive.

Neural Networks. Background - Neural Networks can be : Biological - Biological models Artificial - Artificial models - Desire to produce artificial systems.

1 Azhari, Dr Computer Science UGM. Human brain is a densely interconnected network of approximately neurons, each connected to, on average, 10 4.

How do you get here?

Michael Holden Faculty Sponsor: Professor Gordon H. Dash.

Partnerships Horizon 2020 / Eurostars expert: Dr. Radosław Piesiewicz.

BMI DEVELOPMENT SYSTEMS -SRIKANTH.B. INTRODUCTION The core of this paper is that to operate machines from a remote area. In the given BMI DEVELOPMENT.

       January 3 rd, 2005 The signaling properties of the individual neuron. How we move from understanding individual nerve cells.

Eduard Petlenkov, Associate Professor, TUT Department of Computer Control

Mikolt Csap (Unit G.2 – Creativity)

From CLEF to TrebleCLEF Promoting Technology Transfer

2nd International Congress on Neurotechnology, Electronics and Informatics (Neurotechnix 2014) – Rome, Italy Si elegans: Computational modelling of C.

Fall 2004 Perceptron CS478 - Machine Learning.

Recognition of biological cells – the beginning of study

Neuroinformatics at Edinburgh

POPULAR TYPES OF AUTOMATION SYSTEMS

ISS0023 Intelligent Control Systems Arukad juhtimissüsteemid

Kocaeli University Introduction to Engineering Applications

شبکه عصبی تنظیم: بهروز نصرالهی-فریده امدادی استاد محترم: سرکار خانم کریمی دانشگاه آزاد اسلامی واحد شهرری.

Pulsed Neural Networks

Towards a cost effective housing policy in Flanders and Europe

Pattern Recognition & Machine Learning

New Experiences Enhance Coordinated Neural Activity in the Hippocampus

Machine Learning.

Presentation transcript:

Project funded by the Future and Emerging Technologies arm of the IST Programme FET-Open scheme IST ~ INPRO (Information Processing by Natural Neural Networks) Project funded by the Future and Emerging Technologies arm of the IST Programme FET-Open scheme IST ~ INPRO (Information Processing by Natural Neural Networks) ETHZ Leuven ~ December 3 rd 2002 IST ~ INPRO Information Processing by Natural Neural Networks 1

Project funded by the Future and Emerging Technologies arm of the IST Programme FET-Open scheme IST ~ INPRO (Information Processing by Natural Neural Networks) Project funded by the Future and Emerging Technologies arm of the IST Programme FET-Open scheme IST ~ INPRO (Information Processing by Natural Neural Networks) ETHZ Leuven ~ December 3 rd 2002 IST ~ INPRO Information Processing by Natural Neural Networks What is it all about? Exploration of a novel method of information processing by combining natural neurons with Si- technology because brains have more powerful parallel computing abilities than any contemporary Si-based computer. 2

Project funded by the Future and Emerging Technologies arm of the IST Programme FET-Open scheme IST ~ INPRO (Information Processing by Natural Neural Networks) Project funded by the Future and Emerging Technologies arm of the IST Programme FET-Open scheme IST ~ INPRO (Information Processing by Natural Neural Networks) ETHZ Leuven ~ December 3 rd 2002 Applications Example of parallel processing by cultured neural networks in image processing Possible applications: Image filtering (e.g. sharpening, smoothing) Pattern recognition... 3

Project funded by the Future and Emerging Technologies arm of the IST Programme FET-Open scheme IST ~ INPRO (Information Processing by Natural Neural Networks) Project funded by the Future and Emerging Technologies arm of the IST Programme FET-Open scheme IST ~ INPRO (Information Processing by Natural Neural Networks) ETHZ Leuven ~ December 3 rd 2002 How? Signal processing of neural network data requires long living biological neural networks suitable life-maintaining chambers direct contact of neurons to miniaturized CMOS recording and stimulation electronics through an adapted wetware-hardware interface Signal processing of neural network data comprises recording, compressing, analyzing, and eliciting neural activity IST ~ INPRO Information Processing by Natural Neural Networks 4

Project funded by the Future and Emerging Technologies arm of the IST Programme FET-Open scheme IST ~ INPRO (Information Processing by Natural Neural Networks) Project funded by the Future and Emerging Technologies arm of the IST Programme FET-Open scheme IST ~ INPRO (Information Processing by Natural Neural Networks) ETHZ Leuven ~ December 3 rd 2002 INPRO Participants DE:University of Kaiserslautern WP2: Interfaces, Cell Cultures, Recording; WP4: Testing (+ WP7: Coordination) CH:ETH Zürich, Physical Electronics Lab WP1: Electronics, Transducers; WP4: Testing CH:University of Neuchâtel WP3: Microfluidics; WP4: Testing I:Scuola Internazionale di Studi Superiori Avanzati WP5: Data Evaluation, Recording; WP4: Testing CH:Leister Process Technologies WP6: Plastic/Si Interface, Exploitation and Dissemination 5