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Abstract Neural ordinary differential equations (ODEs) are widely used in neuroscience to model the collective activity of neurons during behavioral tasks. The high dimensionality of their parameter and activity spaces, however, often make it challenging to infer and interpret the fundamental features of their dynamics. In this study, we employ recent nonlinear dynamical system techniques to uncover the core dynamics of several Neural ODEs used in contemporary neuroscience.
Abstract Neural ordinary differential equations (ODEs) are widely used in neuroscience to model the collective activity of neurons during behavioral tasks. The high dimensionality of their parameter and activity spaces, however, often make it challenging to infer and interpret the fundamental features of their dynamics. In this study, we employ recent nonlinear dynamical system techniques to uncover the core dynamics of several Neural ODEs used in contemporary neuroscience.
1. Introduction Dimensionality reduction for datasets representing high-dimensional nonlinear mechanical systems is of crucial importance in science and technology. Low-dimensional models are expected to reduce computational cost and capture the essential physics of a high-dimensional system from data.
A number of countries have developed, at national or state level, quality registries that contain individualised data on patient characteristics, health status, medical interventions and outcomes after treatment.1 ,2 These registries offer new opportunities to perform large-scale population-based studies in the field of perioperative medicine.
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