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Abstract Brain computations emerge from collective dynamics of distinct neural populations. Behaviors in- cluding reaching and speech are explained by principles of feedback control. However, if feedback control explains neural population dynamics is unknown. We created dimensionality reduction methods that identify subspaces of neural population data that are most feed-forward controllable (FFC) vs. feedback controllable (FBC). We showed that FBC and FFC subspaces diverge for dynamics generated by neuro- anatomical connectivity. In neural recordings from monkey M1/S1 during reaching, FBC subspaces were better decoders of reach kinematics. Compared to FFC subspaces, FBC subspaces emerged from col- lective interactions of a population of neurons with distinct activity profiles. Finally, we revealed that FBC subspaces emphasize rotational dynamics due to enhanced system stability, while FFC subspaces emphasize scaling dynamics. These results demonstrate feedback controllability is a novel, normative theory of neural population dynamics, and connect distinct neuronal populations to differing regimes of emergent dynamics carrying out distinct computations.
Bouchard et al. (Fri,) studied this question.