7 papers
Structure and Control of Biology-inspired Networks
Zexin Sun, John Baillieul
There is increasing interest in developing the theoretical foundations of networked control systems that illuminate how brain networks function so as to enable sensory perception,…
Koopman-based Deep Learning for Nonlinear System Estimation
Zexin Sun, Mingyu Chen, John Baillieul
Nonlinear differential equations are encountered as models of fluid flow, spiking neurons, and many other systems of interest in the real world. Common features of these systems ar…
Neuromimetic Dynamic Networks with Hebbian Learning
Zexin Sun, John Baillieul
Leveraging recent advances in neuroscience and control theory, this paper presents a neuromimetic network model with dynamic symmetric connections governed by Hebbian learning rule…
Emulation Learning for Neuromimetic Systems
Zexin Sun, John Baillieul
Building on our recent research on neural heuristic quantization systems, results on learning quantized motions and resilience to channel dropouts are reported. We propose a genera…
Model Predictive Control for Neuromimetic Quantized Systems
Zexin Sun, John Baillieul
Based on our recent research on neural heuristic quantization systems, we propose an emulation problem consistent with the neuromimetic paradigm. This optimal quantization problem…
Neuromimetic Linear Systems -- Resilience and Learning
Zexin Sun, John Baillieul
Building on our recent work on {\em neuromimetic control theory}, new results on resilience and neuro-inspired quantization are reported. The term neuromimetic refers to the models…
Neuromimetic Control -- A Linear Model Paradigm
John Baillieul, Zexin Sun
Stylized models of the neurodynamics that underpin sensory motor control in animals are proposed and studied. The voluntary motions of animals are typically initiated by high level…