LINOCS: Lookahead Inference of Networked Operators for Continuous Stability

arXiv:2404.18267v1 Announce Type: cross
Abstract: Identifying latent interactions within complex systems is key to unlocking deeper insights into their operational dynamics, including how their elements affect each other and contribute to the overall system behavior. For instance, in neuroscience, discovering neuron-to-neuron interactions is essential for understanding brain function; in ecology, recognizing the interactions among populations is key for understanding complex ecosystems. Such systems, often modeled as dynamical systems, typically exhibit noisy high-dimensional and non-stationary temporal behavior that renders their identification …

abstract arxiv behavior brain complex systems continuous cs.lg cs.sy dynamics ecology eess.sy function inference insights instance interactions key neuron neuroscience operators q-bio.qm stability systems type understanding