Explore cutting-edge mathematical approaches to reconstructing network dynamics from data in this comprehensive workshop featuring leading researchers in neuroscience and complex systems. Delve into data-driven modeling techniques that treat underlying systems as both spatial and temporal structures, examining dynamics reconstruction and characterization across multiple scales. Discover methods for predicting critical transitions from multivariate time series, extracting interactions between brain areas, and understanding the effects of anesthetics on neural networks. Learn about pathological effects on network connectivity and explore modeling approaches to alleviate them, including studies of models that depart from traditional functional connectivity. Examine state change estimation in dynamic functional connectivity using semi-Markov models, correspondences between structural and functional networks, and robust network reconstruction through ergodic basis pursuit. Investigate neural cross-frequency coupling phenomena including delta-alpha interactions, resting state dynamics, anesthesia effects, and sleep patterns. Study adaptive dynamical networks ranging from multiclusters to recurrent synchronization, and analyze structural network changes resulting from traumatic brain injury. Explore the dynamics of higher-order networks and their topological effects, bifurcation theory for random dynamical systems, and emergent hypernetworks in weakly coupled oscillators. Understand thermodynamic formalism for open random dynamical systems and probe the costly dynamics of cognitive effort through advanced mathematical frameworks. Examine rhythm generation by neural circuits, synchronization of oscillators with group interactions, and the fundamental role of time in complex systems. Learn about network bypasses that sustain complexity, methods for inferring coupled oscillatory dynamics from data, and techniques for extracting cycles from spatiotemporal data across dynamic regimes. Discover innovative therapeutic applications including mathematical and physical approaches to treating Parkinson's disease, identification of interactions in complex networked dynamical systems through causation entropy, and self-consistent transfer operators for high-dimensional expanding coupled maps.

Syllabus

Heather Shappell - State change estimation in dynamic functional connectivity w/ semi-Markov models
Klaus Lehnertz - From local to global: correspondences between structural and functional networks
Tiago Pereira - Ergodic basis pursuit induces robust network reconstruction - IPAM at UCLA
Tomislav Stankovski - Neural Cross-frequency Coupling: delta-alpha, resting state, anesthesia, sleep
Serhiy Yanchuk - Adaptive dynamical networks: from multiclusters to recurrent synchronization
Sarah Muldoon - Characterizing differences in structural network changes from traumatic brain injury
Ginestra Bianconi - Dynamics of higher-order networks: effect of topology and triadic interactions
Jaroen Lamb - Towards a bifurcation theory of random dynamical systems - IPAM at UCLA
Deniz Eroglu - Emergent hypernetworks in weakly coupled oscillators - IPAM at UCLA
Sandro Vaienti - Thermodynamic formalism for open random dynamical systems - IPAM at UCLA
Danielle Bassett - Probing the costly dynamics of cognitive effort - IPAM at UCLA
Andrey Shilnikov - Reconstructed rhythm-generation by neural circuits in two sea slugs
Maxime Lucas - Synchronisation of oscillators with group interactions - IPAM at UCLA
Aneta Stefanovska - Time: How it matters - IPAM at UCLA
Ernesto Estrada - Network bypasses sustain complexity - IPAM at UCLA
Arkady Pikovsky - Inferring coupled oscillatory dynamics from data - IPAM at UCLA
Gary Froyland - Extracting cycles from spatiotemporal data and coherent sets across dynamic regimes
Peter Tass - Using Maths and Physics to Treat Parkinson’s With a Vibrating Glove - IPAM at UCLA
Erik Bollt - Identify Interactions in Complex Networked Dynamical Systems through Causation Entropy
Matteo Tanzi - Self-consistent transfer operators for high-dimensional expanding coupled maps