3 papers
eess.SY2023
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…
eess.SY2023
On the complexity of linear systems: an approach via rate distortion theory and emulating systems
Eric Wendel, John Baillieul, Joseph Hollmann
We define the complexity of a continuous-time linear system to be the minimum number of bits required to describe its forward increments to a desired level of fidelity, and compute…
eess.SY2023
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…