Sampling effects and measurement overlap can bias the inference of neuronal avalanches
Published in arXiv, 2019
Abstract:
Neuronal avalanches are a prominent example of critical dynamics in the brain, characterized by power-law distributions of activity sizes. However, the measurement and interpretation of these avalanches can be strongly affected by experimental limitations. In this work, we investigate how sampling effects and measurement overlap can bias the inference of critical dynamics. We show that limited sampling can lead to apparent deviations from criticality, while measurement overlap can create artificial correlations that mimic critical behavior. Our results highlight the importance of carefully considering experimental limitations when interpreting neuronal avalanche data.
Recommended citation: Neto, J. P., Spitzner, F. P., & Priesemann, V. (2019). Sampling effects and measurement overlap can bias the inference of neuronal avalanches. arXiv:1911.10824.
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