Comparison of derivative-based and correlation-based methods to estimate effective connectivity in neural networks.

ABSTRACT: Inferring and understanding the underlying connectivity structure of a system solely from the observed activity of its constituent components is a challenge in many areas of science. In neuroscience, techniques for estimating connectivity are paramount when attempting to understand the network structure of neural systems from their recorded activity patterns. To date, no universally accepted method exists for the inference of effective connectivity, which describes how the activity of a neural node mechanistically affects the activity of other nodes. Here, focussing on purely excitatory networks of small to intermediate size and continuous node dynamics, we provide a systematic comparison of different approaches for estimating effective connectivity. Starting with the Hopf neuron model in

SUBMITTER: Laasch N 

PROVIDER: S-EPMC11825726 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature