4 papers with code • 0 benchmarks • 0 datasets
One of the primary goals of systems neuroscience is to relate the structure of neural circuits to their function, yet patterns of connectivity are difficult to establish when recording from large populations in behaving organisms.
With the vertex confidence and edge connectivity, we can naturally organize more relevant vertices on the affinity graph and group them into clusters.
Determining functional brain connectivity is crucial to understanding the brain and neural differences underlying disorders such as autism.
In all of our simulated data, the differential covariance-based methods achieved better or similar performance to the GLM method and required fewer data samples.