1994 Special Issue: A neural model of timed response learning in the cerebellum
Neural Networks - Special issue: models of neurodynamics and behavior
Searching a Scalable Approach to Cerebellar Based Control
Applied Intelligence
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
A model of corticostriatal plasticity for learning oculomotor associations and sequences
Journal of Cognitive Neuroscience
Adaptive force generation for precision-grip lifting by a spectral timing model of the cerebellum
Neural Networks - 2003 Special issue: Advances in neural networks research — IJCNN'03
A new constructing method of cerebellum model applying to DIVA model
CCDC'09 Proceedings of the 21st annual international conference on Chinese Control and Decision Conference
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From the dawn of modern neural network theory, the mammalian cerebellum has been a favored object of mathematical modeling studies. Early studies focused on the fanout, convergence, thresholding, and learned weighting of perceptual-motor signals within the cerebellar cortex. This led in the proposals of Albus (iMathematical Biosciences, vol. 10, pp. 25–61, 1971; iJournal of Dynamic Systems, Measurement, and Control, vol. 97, pp. 220–227, 1975) and Marr (iJournal of Physiology (London), vol. 202, no. 2, pp. 437–470, 1969) to the still viable idea that the granule cell stage in the cerebellar cortex performs a sparse expansive recoding of the time-varying input vector. This recoding reveals and emphasizes combinations (of input state variables) in a distributed representation that serves as a basis for the learned, state-dependent control actions engendered by cerebellar outputs to movement related centers. Although well-grounded as such, this perspective seriously underestimates the intelligence of the cerebellar cortex. Context and state information arises asynchronously due to the heterogeneity of sources that contribute signals to compose the cerebellar input vector. These sources include radically different sensory systems—vision, kinesthesia, touch, balance and audition—as well as many stages of the motor output channel. To make optimal use of available signals, the cerebellum must be able to sift the evolving state representation for the most reliable predictors of the need for control actions, and to use those predictors even if they appear only transiently and well in advance of the optimal time for initiating the control action. Such a icerebellar adaptive timing competence has recently been experimentally verified (Perrett, Ruiz, and Mauk, iJournal of Neuroscience, vol. 13, no. 4, pp. 1708–1718, 1993). This paper proposes a modification to prior, population, models for cerebellar adaptive timing and sequencing. Since it replaces a population with a single element, the proposed Recurrent Slide and Latch (RSL) model is in one sense maximally efficient, and therefore optimal from the perspective of scalability.