neutral-theory-neural-dynamics

Model neural avalanches without criticality by applying neutral drift theory to brain networks. This framework simulates non-critical, scale-free-like activity, enabling researchers to analyze avalanche size distributions without requiring system fine-tuning.

Neutral drift theory in brain network dynamics is an alternative mechanism to criticality for explaining scale-free neural activity. It models non-critical dynamical regimes where neural avalanches emerge through neutral drift rather than precise parameter tuning at a critical point.

Yes, neutral drift simulation applies to EEG and fMRI data analysis. The framework supports criticality testing and avalanche analysis across both neuroimaging modalities to evaluate non-critical dynamical regimes in brain networks.

Test criticality hypotheses by running neutral drift simulations to generate avalanche size distributions, then applying power-law analysis to compare the predictions against criticality-based hypotheses. This helps evaluate whether observed dynamics require fine-tuning or emerge from neutral drift.

Neural avalanche modeling with neutral drift does not require fine-tuning parameters. The framework generates scale-free-like activity through neutral drift, deliberately avoiding the parameter adjustments necessary for maintaining dynamics at a critical point.

Provide neural activity data as input to run a neutral drift neural dynamics simulation. The analysis tracks avalanche sizes and distributions from this data, allowing you to observe non-critical dynamical regimes without needing specialized preprocessed formats.