Deneysel Tıp Araştırma Enstitüsü & Beyin Araştırmaları Derneği Nörobilim Toplantıları Kompütasyonel Nörobilim Computational Models of the Primary Visual Cortex Dr. Baran Çürüklü Sabancı Üniversitesi İstanbul Üniversitesi, İstanbul Tıp Fakültesi 33 Reform Amfisi (Çocuk Sağlığı ve Hastalıkları Anabilim Dalı) 13 Ekim 2006 Cuma - Saat: 13:00 Abstract A canonical model of the primary visual cortex (V1) is presented. The main goal of this work is to develop mathematical models for understanding information processing in the brain. The V1 model is developed during exposure to (simulated) visual input. Learning rule that governed changes in the synaptic weights was the Bayesian Confidence Propagation Neural Network incremental learning rule. Connectivity pattern demonstrated by this correlation-based network model is similar to that of V1. In 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. This V1 model demonstrates how the recurrent connections can be self-organized and generate a cortex like connectivity pattern. 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. This work is under the scope of computational neuroscience, which is an interdisciplinary field offering theoretical tools for understanding the function of the nervous systems. These tools are mathematical models of the nervous system implemented as computer programs. This relatively young field rests on neuroscience, mathematics, computer science, physics, and cognitive science. Computational neuroscience does also contribute to engineering fields. Many of the models developed for understanding the nervous system have later been used in artificial neural network applications. Biography Dr. Baran Çürüklü is a postdoctoral fellow at the Computer Vision and Pattern Analysis Laboratory (VPALAB), Faculty of Engineering and Natural Sciences, Sabancı University, Turkey, since October 2005. He received the Ph.D. degree in Computer Science from Mälardalen University and the Swedish National Computer Science Graduate School, Sweden, in April 2005. At the moment he is working with biomedical engineering and signal processing at the VPALAB. More specifically; EEG and driver fatigue; EEG signals analysis and mental states; brain-computer interfaces; dynamics of brain waves. His research interests cover a wide range of topics from the function of the brain to artificial intelligence. More specifically, information processing in the brain; visual system; mathematical models of the brain for solving engineering problems. Thus, one of the long term ambitions of the applicant has been to introduce the knowledge that he has gained on information processing in the brain to engineering fields, especially artificial intelligence; exemplified by this application to SSF. Duyuru 2689 kez okunmuştur.