no code implementations • 1 May 2024 • 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.
no code implementations • 3 Oct 2023 • 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 rules.
no code implementations • 4 May 2023 • 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.
no code implementations • 19 Dec 2022 • Zexin Sun, John Baillieul
Based on our recent research on neural heuristic quantization systems, we propose an emulation problem consistent with the neuromimetic paradigm.
no code implementations • 10 May 2022 • Zexin Sun, John Baillieul
Tying these ideas together with our previous work on resilient stability, a resilient separation principle is established.
no code implementations • 27 Apr 2021 • John Baillieul, Zexin Sun
Stylized models of the neurodynamics that underpin sensory motor control in animals are proposed and studied.