Varun Madan Mohan

This study examines polysynaptic signal propagation in neural mass models across different dynamical regimes and benchmarks results against empirical data.

The paper evaluates neural oscillatory mechanisms for communication in the human brain using MEG data, finding that power and phase coherence in specific frequency bands correlate with communication patterns.

This paper introduces EWC, a method to infer discrete neural communication from continuous time series data, showing it is faster and effective at capturing directional signaling.

This paper introduces a scale-invariant perturbative framework to quantify network-dynamical interactions in brain networks, demonstrating its application to in-silico human resting state data.