Abstract
In this paper, we introduce a novel computational paradigm based on modern control and optimization theory and biological observations. We investigate the ‘minimum-variance principle’ of a controlled dynamical system with noise, assuming that the noise inherent to the control signal is sub-Poisson. In this case, we find that the optimal solution of the stochastic controller is not an explicit function but is composed of a parameterized measure. Moreover, in contrast to the supra-Poisson or Poisson noise, this sort of parameterized measure can achieve precise control performance even in the presence of noise.
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Lu, W., Amari, Si., Feng, J., Waxman, D. (2013). On a Theory of Precise Neural Control in a Noisy System. In: Yamaguchi, Y. (eds) Advances in Cognitive Neurodynamics (III). Springer, Dordrecht. https://doi.org/10.1007/978-94-007-4792-0_23
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DOI: https://doi.org/10.1007/978-94-007-4792-0_23
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