Summary: Neural systems are highly recurrent, nonlinear networks that must balance stability and flexibility to support robust information processing. A growing body of theory suggests that the brain operates near critical regimes, often described as the edge of chaos, where dynamics are both stable enough to be controllable and rich enough to enable valuable computation. Maintaining this balance is challenging: biologically realistic networks are sparse, structured, and low dimensional, properties that complicate traditional notions of synaptic balance and make static stability constraints difficult to enforce. Moreover, ongoing sensory input and behavioral demands continuously perturb network dynamics, requiring regulation on multiple timescales.
This workshop brings together diverse perspectives on how neural networks achieve, lose, and regain dynamical stability. Topics may include adaptive and plastic mechanisms that modulate effective connectivity, the role of nonlinear and history-dependent dynamics, and data-driven approaches for inferring time-varying stability from electrophysiological recordings. The workshop will also explore implications for neurological states characterized by altered excitability, such as epilepsy or anesthesia, and discuss how stimulation, control-theoretic frameworks, and modern system-identification methods can be used to probe and influence network stability. By integrating theory, modeling, and experimental insights, the workshop aims to foster cross-disciplinary dialogue on principled strategies for understanding and controlling complex brain dynamics.
Tentative program: 9:00-9:10: Introduction – Brian Lundstrom, MD, PhD, Mayo Clinic 9:10-9:30: Paul Bogdan, PhD, University of Southern California - Theoretical Foundations of NeuroAI: A Modeling Framework Motivated by Living Neuronal Network Dynamics 9:30-9:50: Leandro Fosque, PhD, Washington University in St. Louis - Criticality and Adaptation in Neural Systems 9:50-10:10: Srishty Aggarwal, Indian Institute of Science - Adaptation, Ageing, and Stability in Recurrent Brain Networks 10:10-10:30: Discussion
10:30-11:00: Break
11:00-11:20: Emily Pereira, PhD, Texas Tech University - Stabilizing Fractional Dynamical Networks Suppresses Epileptic Seizures 11:20-11:40: Tom Richner, PhD, Mayo Clinic - Adaptation Modulates Effective Connectivity and Network Stability 11:40-12:00: Audrey Sederberg, PhD, Georgia Institute of Technology - Critical Dynamics, Effective Dimensionality, and Flexible Learning in Neural Systems 12:00-12:30: Discussion
