Neuroscience Congress

13th Basic and Clinical Neuroscience Congress 2024

The 13th Basic and Clinical Neuroscience Congress was held in Razi Conference Center on December 11-13 in Tehran, Iran. Academic backgrounds of the talks varied from medical to cognitive, theoretical to computational neuroscience. Among the many panels, network science appeared by the “Network Neuroscience” panel chaired by Dr. Khosroabadi.
This topic was integrated in previous years’ panels such as Computational Neuroscience, Computational Modeling In Cognitive Neuroscience or Advanced Statistical Methods In Probabilistic Computational Cognitive Science; however, it had never been dedicated to a separate panel before until now. This year’s panel showcased network science and statistical physics in studying the brain, focusing mainly on how Heider’s theory of balance can be applied to the structural brain network to explain the behavioral divergence in neurodiversity.

Network Neuroscience panel

Dr. Khosroabadi introduced cognitive neuroscience to explore global versus local brain connectivity, where pairwise and higher-order interactions give rise to rich dynamics. His insights into structural brain networks highlighted non-random connectivity patterns that are associated with neurocognitive disorders such as ADHD, ASD, and OCD.
Dr. Moghimi presented on self-organized criticality in neuronal dynamics, showing the efficiency of the brain in critical phase transitions by fractal and scale-free distributions. He proposed models to interpret the observed LFP data and connectome characteristics.


Dr. Jafari went into depth on connectivity in the brain, covering Heider balance, AI-driven studies of brain networks, and quantum decision-making frameworks.
Finally, Dr. Saberi and Dr. Moradimanesh, CCNET alumnus, studied the properties of brain subnetworks by comparing energy distributions between neurotypical and ASD groups. Their work underlined the role of excitatory-inhibitory balances in understanding brain function.
In the paper presentation section, CCNET graduate students Mohammad Amin Safai and Abolfazl Haghighifar introduced their recent paper on Information flow in the functional Brian network of ASDs vs Controls by transfer entropy approach via an extended abstract.
Among many other panels, one of the stronger focuses was on AI and machine learning. From the use of AI in disease detection to drawing inspiration from the human brain to build the next generation of AI, presentations ranged across a wide breadth. Examples include third-generation neural networks that use brain-like spiking nodes for dramatically better efficiency and performance.
Evidently, the already interdisciplinary field of neuroscience has the growing potential to embed even more of the mathematical and computational sciences-to strengthen its base and enlarge