Differential flatness for neuroscience population dynamics -- A preliminary study
arXiv:1612.03314h-index: 34
AI Analysis
For computational neuroscientists, this is an incremental theoretical extension of control theory to neural dynamics without empirical validation.
This study explores differential flatness in neural population dynamics, enabling trajectory tracking and feedforward-to-feedback switching, but provides no quantitative results.
The present document is devoted to structural properties of neural population dynamics and especially their differential flatness. Several applications of differential flatness in the present context can be envisioned, among which: trajectory tracking, feedforward to feedback switching, cyclic character, positivity and boundedness.