Integrative neurocybernetic modeling in the era of large-scale neuroscience

Authors:Il Memming Park, Gonzalo G. de Polavieja, Ayesha Vermani, Juan Álvaro Gallego, Kathleen Esfahany, Shreya Saxena, Guillaume Hennequin, Michael Orger, Auke Ijspeert, Matthew Dowling, Daniel McNamee, Srinivas C. Turaga, Zachary Mainen, Alfonso Renart, Joseph J. Paton

Abstract:Large-scale neuroscience is generating rich datasets across animals, brain areas, and behavioral contexts, yet our modeling efforts remain fragmented across isolated experiments. We argue that understanding behavior requires integrative neurocybernetic models: understandable dynamical models that capture the closed-loop coupling of brain, body, and environment, treat the brain as a controller pursuing latent objectives, represent structured variation across scales, and scale to heterogeneous datasets. Such models shift the goal from predicting neural recordings in isolation to inferring the organizing principles that govern neural and behavioral dynamics. We outline a practical route toward this goal by combining nonlinear state-space models and meta-dynamical extensions with scalable inference, knowledge distillation, and mixed open- and closed-loop training. By pooling complementary constraints from recordings, behavior, perturbations, and anatomy, integrative neurocybernetic models can provide statistical amplification, few-shot generalization, and mechanistic insight into shared dynamical structure, individual variation, and the control objectives that govern behavior. This agenda offers a model-centric path from fragmented data to a mechanistic science of how brains produce behavior.

Neurons and Cognition (q-bio.NC); Machine Learning (cs.LG)

arXiv:2604.23903 [q-bio.NC]

(or arXiv:2604.23903v2 [q-bio.NC] for this version)