The artificial neural network model was created based on analogies to biological counterparts, such as a simplified model of the neuron or a system of retinal neurons. Due to the increasing complexity of tasks and problems with the development of effective methods for learning deep neural networks, solutions based on algebraic structures dominate Today, advanced approaches in machine learning such as deep learning show a number of undesirable features, such as forgetfulness, susceptibility to adversarial examples, the requirement for a large training set, and slow learning. Most of these features do not occur in biological systems, thus it would be beneficial to take an inspiration from them to help training artificial systems. The aim of the project is to analyze high-level behaviors of biological neural systems and to build innovative artificial models by proposing new paradigms of learning and new architectures of computational models.
The Jagiellonian University will run six research groups: Cognitive group (leader Tadeusz Marek), Physics group (leader Maciej A. Nowak), Machine-learning group, Neuro group, BioDataScience group, InfoTech group.
Job offers for
|11:00 - 12:00||Machine-learning group|
|12:10 - 13:10||Physics group|
|13:10 - 14:00||Lunch|
|14:00 - 15:00||Neuro group|
|15:10 - 16:10||Cognitive group|
|16:10 - 16:40||Coffee Break|
|16:40 - 18:40||Group Discussion|
Localization: Room 1146 (first floor). Faculty of Mathematics and Computer Science, Łojasiewicza 6, 30-348 Kraków.
Maciej Wołczyk, Jacek Tabor, Marek Śmieja, "Biologically-Inspired Spatial Neural Networks"
Romuald Janik, "Explaining the Human Visual Brain 2019 competition and workshop"
Additional information: Paper you can find here.
Magda Gawłowska, "EEG 101"
Igor Podolak, "BioNN machine learning"