Robustness of VOR and OKR adaptation under kinematics and dynamics transformations
Adrian Haith and Sethu Vijayakumar
In: Sixth IEEE international Conference on Development and Learning (ICDL '07), Jul 2007, London, UK.
Many computational models of vestibulo-ocular
reflex (VOR) adaptation have been proposed, however none of
these models have explicitly highlighted the distinction between
adaptation to dynamics transformations, in which the intrinsic
properties of the oculomotor plant change, and kinematic transformations,
in which the extrinsic relationship between head
velocity and desired eye velocity changes (most VOR adaptation
experiments use kinematic transformations to manipulate the
desired response). We show that whether a transformation is
kinematic or dynamic in nature has a strong impact upon
the speed and stability of learning for different control architectures.
Specifically, models based on a purely feedforward
control architecture, as is commonly used in feedback-error
learning (FEL), are guaranteed to be stable under kinematic
transformations, but are susceptible to slow convergence and
instability under dynamics transformations. On the other hand,
models based on a recurrent cerebellar architecture perform
well under dynamics but not kinematics transformations. We
apply this insight to derive a new model of the VOR/OKR
system which is stable against transformations of both the plant
dynamics and the task kinematics.