March 7, 2023: New Research from the "Mathematics of Neural Networks"

Members of the "Mathematics of Neural Networks" theme have developed a new approach to analyzing the "dynamics of nonlinear oscillator networks with heterogeneous time delays."  This approach may shed light on long-term memory formation during sleep and other complex neural processes. 

"We introduce a new mathematical approach to studying systems with time delays. As an example, we can consider brain networks, where delays in the connections between brain areas are important. This framework offers, for the first time, analytical predictions of specific patterns that emerge in networks in the presence of time delays. As a direct application, we study waves that emerge in the brain," said theme member Dr. Roberto C. Budzinski, who is also a Postdoctoral Scholar with the Department of Mathematics,

Findings have been published in the latest issue of Physical Review Research.  The study was authored by Budzinski ("Neural Networks" theme member), Tung T. Ngygen ("Neural Networks" contributing researcher), Gabriel B. Benigno (theme member), Jacqueline Doán (theme member), Ján Mináč (Western Fellow), Terrance J. Sejnowski (Salk Institute), Lyle E. Muller (Theme Leader). 

Read the study here: