Understanding Neural Avalanches and Entropy in Simulated Brain Networks
Event Type
Research Presentation
Academic Department
Physics
Location
Dana Science Building, 2nd floor
Start Date
24-4-2026 1:00 PM
End Date
24-4-2026 2:30 PM
Description
Cortical neurons fire in cascades called neural avalanches, which follow scale-free patterns typical of systems at a critical point balanced between order and randomness. Using computer simulations, we compare two models of criticality in brain-like networks. The traditional model shows clear patterns only when large groups of neurons are observed. A newer model by Jones et al. (2023) reveals similar structure even in small subsets. To test whether this newer model also supports efficient information flow, we analyzed Shannon entropy across the network. Our results show that entropy peaks near a critical point, supporting the idea that information capacity is maximized at criticality, even in the newer model.
Understanding Neural Avalanches and Entropy in Simulated Brain Networks
Dana Science Building, 2nd floor
Cortical neurons fire in cascades called neural avalanches, which follow scale-free patterns typical of systems at a critical point balanced between order and randomness. Using computer simulations, we compare two models of criticality in brain-like networks. The traditional model shows clear patterns only when large groups of neurons are observed. A newer model by Jones et al. (2023) reveals similar structure even in small subsets. To test whether this newer model also supports efficient information flow, we analyzed Shannon entropy across the network. Our results show that entropy peaks near a critical point, supporting the idea that information capacity is maximized at criticality, even in the newer model.
Comments
Under the direction of Dr. Jacob Barfield.