A theory of cerebellar function

Albus, James S.

doi:10.1016/0025-5564(71)90051-4

Cited by 2,284 publications

(1,569 citation statements)

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“…Each of 1200 (30 Â 40) GCs receives excitatory input (w mf 0X5) from four randomly selected MFs (the set M) and an inhibitory input (w go À0X02) from the GOs. The GCs make nonlinear combinations of the inputs to serve as expansion encoders as originally proposed by Albus (1971). A Instantaneous desired joint angles; B current target joint angles are coded on 10 Â 10 matrices.…”

Section: Cerebellum Modelmentioning

confidence: 99%

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Cerebellar learning of accurate predictive control for fast-reaching movements

Spoelstra

Schweighofer

Arbib

2000

Biological Cybernetics

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Abstract. Long conduction delays in the nervous system prevent the accurate control of movements by feedback control alone. We present a new, biologically plausible cerebellar model to study how fast arm movements can be executed in spite of these delays. To provide a realistic test-bed of the cerebellar neural model, we embed the cerebellar network in a simulated biological motor system comprising a spinal cord model and a six-muscle two-dimensional arm model. We argue that if the trajectory errors are detected at the spinal cord level, memory traces in the cerebellum can solve the temporal mismatch problem between e erent motor commands and delayed error signals. Moreover, learning is made stable by the inclusion of the cerebello-nucleo-olivary loop in the model. It is shown that the cerebellar network implements a nonlinear predictive regulator by learning part of the inverse dynamics of the plant and spinal circuit. After learning, fast accurate reaching movements can be generated.

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Section: Cerebellum Modelmentioning

confidence: 99%

“…The cerebellar cortex is often viewed as an array of perceptrons (Marr 1969;Albus 1971;Ito 1984). In this theory, the granule cells (GCs) ± Purkinje cell (PC) synapses can be modi®ed by climbing ®ber (CF) inputs.…”

Section: Introductionmentioning

confidence: 99%

Cerebellar learning of accurate predictive control for fast-reaching movements

Spoelstra

Schweighofer

Arbib

2000

Biological Cybernetics

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“…The Marr-Albus model of cerebellar function was among the first to postulate variable synaptic weights in a concrete synaptic pathway (the parallel fiber-Purkinje neuron [PF-PN] synapse) as a way to implement a specific memory function, cerebellar learning [2,29]. Subsequent theoretical and experimental work demonstrated that efficient synaptic weight-based memory storage requires bidirectional learning rules [5,28].…”

Section: Introductionmentioning

confidence: 99%

Presynaptically expressed long-term depression at cerebellar parallel fiber synapses

Qiu

Knöpfel

2008

Pflugers Arch - Eur J Physiol

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Plasticity at synapses between parallel fiber (PF) and Purkinje neurons (PN) is widely accepted as a cellular model for certain forms of cerebellar learning. Although PF-PN synapses are known to express bidirectional longterm plasticity at the postsynaptic site, long-term plasticity at the presynaptic site is currently limited to potentiation of the synapses. In this paper, we report on presynaptically expressed PF long-term depression (preLTD) that is observed when presynaptically expressed PF long-term potentiation (preLTP) is pharmacologically prevented. PF preLTD is most efficiently induced by 4 Hz PF stimulation and requires activation of cannabinoid CB1 receptors. Our results indicate that, during preLTD induction, endocannabinoids are released in an NMDA receptor-dependent, but not mGlu1 receptor-dependent, fashion. We conclude that bidirectional plasticity mechanisms exist for both presynaptic and postsynaptic components of cerebellar learning.

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“…Learning a TTC threshold for action might therefore be based on noticing signal combinations. This is of interest because the cerebellum, which has long been viewed as a signal-combination detector (e.g., Albus, 1971 ), has recently been implicated experimentally as (also) a site of adaptive timing (Keele & I vry, 1990;Thompson, 1986;Perrett et al, 1993;Steinmetz, 1990). Work by Bullock, Fiala & Grossberg ( 1994;Fiala, Grossberg & Bullock, 1996) has produced a biochemistry-based model of how phase-advanced action components can be learned by Purkinje cell populations in any predictive task context to which the cerebellum is sensitive.…”

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confidence: 99%