Matej Mertik

Biography

Research interests of Dr. MatejMertik are in the fields of data technologies, machine learning, and artificial intelligence. Holding a PhD from the Faculty of Electrical Engineering and Computer Science, his work has significantly advanced the understanding of feature space folding through cellular automata models. His tenure at CERN as a scientific associate in the Machine Protection and Electrical Integrity Group (MPE) saw him spearheading the development of innovative data analytics software, leveraging modern machine learning and data mining techniques. In his current role at Alma Mater Europaea, Dr. Mertik has been pivotal in shaping the future of digital technologies education. He played a key role in establishing the groundbreaking Applied Artificial Intelligence PhD program in 2023, demonstrating his commitment to nurturing the next generation of AI experts. Under his leadership, the program has flourished, combining rigorous academic training with real-world applications. His approach is characterized by a blend of theoretical knowledge and practical application, aiming to bridge the gap between advanced computational theories and their implementation in solving complex real-world problems. His commitment to collaborative research and knowledge sharing makes him a valuable contributor to any scientific endeavor. 

Description of the general focus of the symposium "Integrating Spiking Neural Networks in Neurobiology and Computer Science"

The symposium aims to showcase the latest advancements and foster discussions at the intersection of neurobiology and computer science, emphasizing the development and understanding of neural networks.  

Objectives are:  

To explore the complex processes of synaptic activity and neuronal communication through advanced technologies like multi-electrode array (MEA) recordings.  

To discuss the advancements and challenges in Spiking Neural Networks (SNNs) as a bridge between biological neural systems and artificial intelligence.  

To address the challenges in training biologically plausible artificial neural networks and explore strategies to develop models that closely mimic animal brain processes.  

To provide a platform for early-career researchers to present their findings and engage with a diverse audience of experts in neurobiology, computer science, and related fields.  

Key topics will include:  

The role of advanced recording technologies in understanding neural dynamics.  

The development and potential of SNNs in mimicking biological neural processes.  

The challenges and future directions in training biologically accurate neural network models.  

The symposium is particularly relevant given the rapid advancements in both neurobiology and computer science. Integrating these disciplines is crucial for developing more accurate models of brain function, which has significant implications for understanding neural disorders and advancing AI technology. Additionally, the symposium will serve as a critical platform for interdisciplinary collaboration and knowledge exchange, fostering innovation in these fields.  

The symposium is designed for scientists, researchers, and students in neurobiology, computer science, artificial intelligence, and related fields. It will also be beneficial for early- career researchers looking for an opportunity to present their work and engage with established experts.  

We believe that this symposium will be a valuable addition to the Neuronus conference offering attendees a comprehensive insight into the exciting and rapidly evolving field of neural network research. 

Talk: "Integrative Approaches in Spiking Neural Networks: Bridging Machine Learning, Computer Science, and Neurobiology"

Spiking Neural Networks (SNNs) stand at the intersection of machine learning (ML), computer science (CS), and neurobiology, promising to revolutionize computational paradigms by mimicking the temporal dynamics of biological neural systems. This presentation explores the landscape of SNNs, emphasizing the synthesis of concepts from ML, such as learning algorithms and frameworks like BindsNET and SpikeJelly, with the structural and functional insights from neurobiology. We discuss the role of computer science in developing simulation tools like NEST, which enable the mapping of neural architectures. The contribution of neurobiology is underscored by providing biological fidelity to models, influencing both architecture and function. Moreover, we delve into the emerging field of neuromorphic engineering (EE), which aims to translate the computational efficiency of SNNs into hardware implementations. By converging these fields, SNNs hold the potential for creating more efficient, adaptive, and biologically realistic computing systems. The presentation concludes with a discussion on the current challenges and future directions in SNN research, outlining a collaborative path forward for these intertwined disciplines.