Tilo Schwalger: A stochastic mean-field model for integrate-and-fire networks at the mesoscopic scale
| When |
Mar 12, 2025
from 12:15 PM to 01:00 PM |
|---|---|
| Where | Bernstein Center, Hansastr. 9a, Lecture Hall. |
| Contact Name | Gundel Jaeger |
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Abstract
To understand the emergent dynamics of macroscopic neural activity, we need simple coarse-grained models that link to the network of spiking neurons at the microscopic scale. Coarse-grained models, consistent with microscopic dynamics, are also essential for simulating large-scale brain activity. The intermediate mesoscopic scale of finite-size networks is particularly critical as it captures finite-size fluctuations. However, deriving simple and efficient stochastic models that accurately describe the mesoscopic activity of finite-size spiking networks remains a largely unresolved theoretical problem.
In my presentation I will introduce a simple stochastic mean-field model for finite-size networks of leaky integrate-and-fire spiking neurons. I will begin by reviewing an accurate stochastic integral equation for the population activity of these networks (Schwalger et al. 2017) and then demonstrate how this equation can be used to derive simple stochastic delay differential equations (SDDEs) for neural population dynamics. The theoretical reduction is based on a novel eigenfunction expansion. The SDDE models successfully reproduce the non-stationary dynamics and fluctuations of mesoscopic population activities, enabling rapid simulations of large cortical circuits at the mesoscopic scale.