Newest papers

Spontaneous brain-wide rotational dynamics constrain cue-evoked behavior

Sihan Chen, Yunlong Xu, Doruk Efe Gökmen, Michel Fruchart, Brent Doiron, Vincenzo Vitelli · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.25061 · cited by 0 works (OpenAlex) · open-access PDF

Spanish

El descubrimiento de las representaciones biológicas de las pequeñas dimensiones de la dinámica neural en todo el cerebro y su relación con el comportamiento sigue siendo un desafío central. Aquí, mostramos que la dinámica de tareas de rotación espontánea en todo el cerebro restringe el comportamiento y el aprendizaje de la cue-evocada. Para establecer sistemáticamente estos hallazgos, desarrollamos una técnica de reducción de la dimensión de la dinámica neural en todo el cerebro llamada Análisis de Componentes de Rotación (RCA) que optimiza la detección de modos de rotación de baja varianza (captoencephalography) incluso en ensayos individuales tempranos. Hemos aplicado la RCA a los datos de comportamiento y de aprendizaje deducido de los monos y los humanos. Para establecer sistemá

Machine translation (Meta M2M100); the English original follows.

Abstract

Uncovering low-dimensional representations of brain-wide neural dynamics and their relationship to behavior remains a central challenge. Here, we show that spontaneous brain-wide rotational dynamics constrain cue-evoked behavior and learning. To systematically establish these findings, we developed a dimensionality-reduction technique called Rotational Component Analysis (RCA) that optimizes the detection of low-variance rotational modes even within individual trials. We applied RCA to motor-cortical spike-train data from monkeys and humans, mouse cortex-wide calcium imaging, and human functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG). In mice, a rotational mode identified from activity recorded at rest predicts cue-evoked behavior: the phase of rotation when a randomly timed cue arrives predicts whether the animal subsequently pulls the lever. We explain this surprisingly strong correlation using both a recurrent neural network (RNN) and a minimal two-dimensional model of response learning, in which the intrinsic rotational mode serves as a dynamical constraint on response and learning. We further identify double-rotation modes containing two centers of rotation that separate preparatory from task dynamics in both mouse and human data. Remarkably, corresponding modes also organize neural dynamics during a non-motor human task, and rotational irreversibility captures early psychosis even when principal component analysis shows no group difference. Our work establishes rotational dynamics as low-dimensional coordinates that constrain how ongoing brain states are converted into behavior. More broadly, RCA provides a versatile tool for detecting hidden nonreciprocal dynamics in biological and physical datasets.

Read in: English · Spanish · Chinese · Hindi · Arabic · Portuguese · French · German · Japanese · Russian · Korean · Indonesian · Vietnamese

Topics: Magnetoencephalography, Dynamics (music), Rotation (mathematics), Physics, Rotational dynamics, Rotation around a fixed axis

Sources: OpenAlex (CC0), arXiv, Crossref. Agent Exchange