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Baran Çürüklü, Postdoctoral research fellow

From 1st of October I am a member of the Computer Vision and Pattern Analysis Laboratory (VPA), at Sabancı University, Istanbul, headed by Prof. Aytül Erçil. I'll participate in a project called Safe Drive as a post-doctoral researcher. As you can guess the project is about safety issues while driving a car. At the moment the main focus of this project is to collect lots of data from sensors "mounted" on the car as well as the driver. One of the main questions will be if we can detect driver fatigue based on sensory input. More information can be found at the VPA pages.

I was a member of the Artificial Intelligence and Intelligent Systems Group at the Dept. of Computer Science and Electronics (IDE), Mälardalen University. During this time I was involved in the ExAct project. The intention of this project is to help the production industry in developing "dynamic and intelligent frameworks for experience sharing" within and between companies. One of the key issues with regard to this topic is "knowledge recycling".

Contact

Sabancı University, Faculty of Engineering and Natural Sciences, P.O. Box 34956, Tuzla, Istanbul, Turkey

E-mail: baranc 'at' sabanciuniv.edu

Phone: +90 (0)216-483 90 00, Telefax: +46 (0)216-483 95 50

Education

2005: Ph.D. in Computer Science and Engineering, at IDE (former Dept. of Computer Science and Engineering, IDt) / Mälardalen University and the Swedish National Graduate School in Computer Science

2003: Licentiate Thesis in Computer Science and Engineering, at IDt / Mälardalen University and the Swedish National Graduate School in Computer Science

1998: M. Sc. in Computer Engineering, at IDt / Mälardalen University (at ABB Atom)

1995: B. Sc. in Computer Engineering, at IDt / Mälardalen University

Research

My Ph. D. thesis was on information processing within the primary visual cortex / neocortex (see the abstract text below for the details). My supervisor was Professor Anders Lansner at Studies of Artificial Systems, Royal Institute of Technology in Stockholm. More information can be found at the official research page, where you can download most of the papers (including my Ph. D. thesis) as well.

Abstract. In this thesis a model of the primary visual cortex (V1) is presented. The centerpiece of this model is an abstract hypercolumn model, derived from the Bayesian Confidence Propagation Neural Network (BCPNN). This model functions as a building block of the proposed laminar V1 model, which consists of layer 4 and 2/3 components.

The V1 model is developed during exposure to visual input using the BCPNN incremental learning rule. The connectivity pattern demonstrated by this correlation-based network model is similar to that of V1. In both modeled cortical layers local horizontal connections are dense, whereas long-range horizontal connections are sparse. Layer 4 local horizontal connections are biased towards the iso-orientation domain, whereas long-range horizontal connections are equally distributed between all orientation domains. In contrast, both local and long-range horizontal connections of the layer 2/3 are biased towards the iso-orientation domains. The layer 2/3 network is axially specific as well. Thus, this V1 model demonstrates how the recurrent connections can be self-organized and generate a cortex like connectivity pattern.

Furthermore, in both layers inhibition operates within a modeled hypercolumn. This is in line with what is found in the V1, i.e. inhibition is mainly local, whereas excitation extends far beyond the inhibitory network. Observe also that neither excitation nor inhibition dominates the network.

Based on this connectivity pattern the V1 model addresses several response properties of the neurons, such as orientation selectivity, contrast-invariance of orientation tuning, response saturation followed by normalization, cross-orientation inhibition. Configuration-specific facilitation phenomena are explained by the axially specific layer 2/3 long-range horizontal connections. It is hypothesized that spike and burst synchronization might aid this process.

The main conclusion drawn is that it is possible to explain connectivity as well as several response properties of the neurons by a general V1 model, which is faithful to the known anatomy and physiology of the neocortex. Thus, when simplicity is combined with biological plausibility the models can give valuable insight into structure and function of cortical circuitry.

Keywords: primary visual cortex, hypercolumn, cortical microcircuit, attractor network, recurrent artificial neural network, Bayesian confidence propagation neural network, developmental models, intracortical connections, long-range horizontal connections, orientation selectivity, response saturation, normalization, contrast-invariance of orientation selectivity, configuration-specific facilitation, summation pools

Publications

Theses

A Canonical Model of the Primary Visual Cortex. Doctoral dissertation. Mälardalen University Press, 2005.

Layout and Function of the Intracortical Connections within the Primary Visual Cortex. Licentiate thesis. Mälardalen University Press, 2003.

Void Quality Estimation within a Nuclear Reactor Tank Using Artificial Neural Networks. Dept. of Computer Science and Engineering, Master's thesis, Mälardalen University, 1998.

Design of a VLSI Systolic Array. Dept. of Computer Science and Engineering, Bachelor's thesis, Mälardalen University, 1995.

Journal Papers

A model of the summation pools within the layer 4 (area 17). To be appear at the Annual Computational Neuroscience Meeting (CNS), Baltimore, USA, Elsevier, 2004. (with Lansner A)

Reviewed Conference and Workshop Publications

Early Stages of Vision Might Explain Data to Information Transformation. To be appear at the Turkish Symposium on Artificial Intelligence and Neural Networks, Izmir, Turkey, 2004.

Quantitative assessment of the local and long-range horizontal connections within the striate cortex. In proceedings of the special session on ‘Biological Inspired Computer Vision', at the 2nd Computational Intelligence, Robotics and Autonomous Systems (CIRAS), Singapore, IEEE Press, 2003. (with Lansner A)

An abstract model of a cortical hypercolumn. In proceedings of the 9th International Conference on Neural Information Processing (ICONIP), pp. 80–85, Singapore, IEEE Press, 2002. (with Lansner A)

Simulation of synchronization in primary visual cortex. Swedish Artificial Intelligence Society Workshop, Skövde, Sweden, 2001. (with Lansner A)

Spike and burst synchronization in a detailed cortical network model with I-F neurons. In proceedings of the International Conference on Artificial Neural Networks (ICANN), pp. 1095–1102, Vienna, Austria, Springer-Verlag, 2001. (with Lansner A)

Workshop Papers (not reviewed)

What can we learn from the hypercolumns? Putative functional roles of the local and long-range horizontal connections within the primary visual cortex of cat. Studies of Artificial Neural Systems, Royal Institute of Technology, Stockholm, 2004.

The Visual Cortex FPGA Project – Hardware Implementation of the Retina and the Primary Visual Cortex. Robotdalen Robot Vision Workshop, Mälardalen University, Västerås, 2004.

Implementation of BCPNN Network with Spiking Units. Studies of Artificial Neural Systems, Royal Institute of Technology, 1999.