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Biological Cybernetics|October 22, 2009
Biologically plausible learning in neural networks: a lesson from bacterial chemotaxisYury P ShimanskyBiological Cybernetics|May 4, 2018
Trans-algorithmic nature of learning in biological systemsYury P ShimanskyBiological Cybernetics|April 3, 2010
Adaptive force produced by stress-induced regulation of random variation intensityYury P ShimanskyBio Systems|March 4, 2026
Mathematical principle underlying the law of entropy increase is vital for actively stable lowering of biosystem entropyYury P ShimanskyThe Behavioral and Brain Sciences|March 12, 2020
Generalization of the resource-rationality principle to neural control of goal-directed movementsNatalia Dounskaia, Yury P ShimanskyThe Behavioral and Brain Sciences|February 16, 2019
Inclusion of neural effort in cost function can explain perceptual decision suboptimalityYury P Shimansky, Natalia DounskaiaExperimental Brain Research|December 1, 2012
Two-phase strategy of neural control for planar reaching movements: I. XY coordination variability and its relation to end-point variabilityMiya K Rand, Yury P ShimanskyExperimental Brain Research|July 2, 2013
Two-phase strategy of neural control for planar reaching movements: II--relation to spatiotemporal characteristics of movement trajectoryMiya K Rand, Yury P ShimanskyBiological Cybernetics|December 4, 2012
Two-phase strategy of controlling motor coordination determined by task performance optimalityYury P Shimansky, Miya K RandBiological Cybernetics|March 5, 2004
A novel model of motor learning capable of developing an optimal movement control law online from scratchYury P Shimansky, Tao Kang, Jiping HePageof 2