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Found 7 papers, 0 papers with code
Koopman-based Deep Learning for Nonlinear System Estimation
Nonlinear differential equations are encountered as models of fluid flow, spiking neurons, and many other systems of interest in the real world.
Neuromimetic Dynamic Networks with Hebbian Learning
Leveraging recent advances in neuroscience and control theory, this paper presents a neuromimetic network model with dynamic symmetric connections governed by Hebbian learning rules.
On the complexity of linear systems: an approach via rate distortion theory and emulating systems
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 this quantity using the rate distortion function of a Gaussian source of uncertainty in those increments.
Emulation Learning for Neuromimetic Systems
Building on our recent research on neural heuristic quantization systems, results on learning quantized motions and resilience to channel dropouts are reported.
Model Predictive Control for Neuromimetic Quantized Systems
Based on our recent research on neural heuristic quantization systems, we propose an emulation problem consistent with the neuromimetic paradigm.
Neuromimetic Linear Systems -- Resilience and Learning
Tying these ideas together with our previous work on resilient stability, a resilient separation principle is established.
Neuromimetic Control -- A Linear Model Paradigm
Stylized models of the neurodynamics that underpin sensory motor control in animals are proposed and studied.
