Estimating and controlling dynamical systems through coordinated spikes Spike Coding Networks for Kalman filtering and LQR control
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2022-05-03
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en
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Abstract
Biological neural networks are able to control animal bodies
robustly and efficiently, through highly sparse and irregular spiking patterns.
In contrast, current implementations of control via artificial spiking
neural networks often do not adhere to the spiking patterns found in
the brain. In the neuroscience theory of Spike Coding Networks (SCNs),
irregularity, sparsity, and robustness naturally arise as a consequence of
coordinated spiking across a neural network, but it is not clear how to use
SCNs for control. Here, we build upon the SCN framework and describe
an analytical solution for estimation and control of dynamical systems
with spiking networks, without the need for neural learning and optimization.
The resulting networks work as spiking equivalents to a state estimator
(Kalman filter) and an optimal controller (Linear–quadratic regulator).
Results on numerical experiments, using the spring-mass-damper
and cartpole systems, show that the networks retain their irregular and
sparse spiking patterns, whilst being robust against neural death. The
framework offers a powerful perspective on how precise control can be
achieved through biological and artificial neural networks, and opens the
way for deploying fast and efficient neuromorphic controllers.
Keywords: Spiking Neural Networks · State Estimation · Optimal Control
· Dynamical Systems.
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Faculteit der Sociale Wetenschappen