A Computational Mechanism for Unified Gain and Timing Control in the Cerebellum

Yamazaki, Tadashi; Nagao, Soichi

doi:10.1371/journal.pone.0033319

Cited by 67 publications

(103 citation statements)

References 62 publications

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“…As in Yamazaki's work using computational models [24], the granular layer was able to recode MF signals into a high-dimensional sparse spatiotemporal spike pattern, irrespective of whether mossy fibers conveyed constant signals (e.g. during the CS) or time-varying signals (e.g.…”

Section: Discussionmentioning

confidence: 68%

Adaptive Robotic Control Driven by a Versatile Spiking Cerebellar Network

et al. 2014

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The cerebellum is involved in a large number of different neural processes, especially in associative learning and in fine motor control. To develop a comprehensive theory of sensorimotor learning and control, it is crucial to determine the neural basis of coding and plasticity embedded into the cerebellar neural circuit and how they are translated into behavioral outcomes in learning paradigms. Learning has to be inferred from the interaction of an embodied system with its real environment, and the same cerebellar principles derived from cell physiology have to be able to drive a variety of tasks of different nature, calling for complex timing and movement patterns. We have coupled a realistic cerebellar spiking neural network (SNN) with a real robot and challenged it in multiple diverse sensorimotor tasks. Encoding and decoding strategies based on neuronal firing rates were applied. Adaptive motor control protocols with acquisition and extinction phases have been designed and tested, including an associative Pavlovian task (Eye blinking classical conditioning), a vestibulo-ocular task and a perturbed arm reaching task operating in closed-loop. The SNN processed in real-time mossy fiber inputs as arbitrary contextual signals, irrespective of whether they conveyed a tone, a vestibular stimulus or the position of a limb. A bidirectional long-term plasticity rule implemented at parallel fibers-Purkinje cell synapses modulated the output activity in the deep cerebellar nuclei. In all tasks, the neurorobot learned to adjust timing and gain of the motor responses by tuning its output discharge. It succeeded in reproducing how human biological systems acquire, extinguish and express knowledge of a noisy and changing world. By varying stimuli and perturbations patterns, real-time control robustness and generalizability were validated. The implicit spiking dynamics of the cerebellar model fulfill timing, prediction and learning functions.

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Section: Discussionmentioning

confidence: 68%

Adaptive Robotic Control Driven by a Versatile Spiking Cerebellar Network

et al. 2014

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“…In Pavlovian eyeblink conditioning, a conditioned response seems to be consolidated in the cerebellar cortex (29)(30)(31)(32). Even in OKR adaptation, a trace of long-term memory remains in the cerebellar cortex as the decrease in the number of PF-PC synapses (33,34), which might represent eye movement trajectory or information on OKR phase (35)(36)(37). Moreover, consolidation of motor memory in general should be investigated in depth from reflex to voluntary movement, particularly in primates including humans.…”

Section: Discussionmentioning

confidence: 99%

Modeling memory consolidation during posttraining periods in cerebellovestibular learning

Yamazaki¹,

Nagao

Lennon

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

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Long-term depression (LTD) at parallel fiber-Purkinje cell (PF-PC) synapses is thought to underlie memory formation in cerebellar motor learning. Recent experimental results, however, suggest that multiple plasticity mechanisms in the cerebellar cortex and cerebellar/vestibular nuclei participate in memory formation. To examine this possibility, we formulated a simple model of the cerebellum with a minimal number of components based on its known anatomy and physiology, implementing both LTD and long-term potentiation (LTP) at PF-PC synapses and mossy fibervestibular nuclear neuron (MF-VN) synapses. With this model, we conducted a simulation study of the gain adaptation of optokinetic response (OKR) eye movement. Our model reproduced several important aspects of previously reported experimental results in wild-type and cerebellum-related gene-manipulated mice. First, each 1-h training led to the formation of short-term memory of learned OKR gain at PF-PC synapses, which diminished throughout the day. Second, daily repetition of the training gradually formed long-term memory that was maintained for days at MF-VN synapses. We reproduced such memory formation under various learning conditions. Third, long-term memory formation occurred after training but not during training, indicating that the memory consolidation occurred during posttraining periods. Fourth, spaced training outperformed massed training in long-term memory formation. Finally, we reproduced OKR gain changes consistent with the changes in the vestibuloocular reflex (VOR) previously reported in some genemanipulated mice.cerebellum | plasticity | memory consolidation | posttraining period | Marr-Albus-Ito theory L ong-term depression (LTD) at parallel fiber-Purkinje cell (PF-PC) synapses in the cerebellar cortex has been thought to be the major mechanism of motor learning (1). This MarrAlbus-Ito hypothesis (2, 3), however, has been challenged since Miles and Lisberger's proposal (4) that long-term potentiation (LTP) at mossy fiber-vestibular nuclear neuron (MF-VN) synapses, not LTD at PF-PC synapses, underlies vestibuloocular reflex (VOR) gain adaptation (4-7). In a recent study on optokinetic response (OKR) gain adaptation, we found evidence that might resolve the controversy: LTD at PF-PC synapses (PF-LTD) and LTP at MF-VN synapses (MF-LTP) play different roles in OKR adaptation (8-10). Namely, PF-LTD accounts for short-term memory in PCs during 1-h training, whereas MF-LTP forms long-term memory in VN after the 1-h training that accumulates during repeated trials of 1-h training. It thus appears as if short-term memory formed in PCs during 1-h training is transferred to VN after training to consolidate as long-term memory (8-10).To investigate the mechanisms of this memory transfer and posttraining consolidation, we conducted a computer simulation study using a simple theoretical model of the cerebellovestibular system including both LTD and LTP at PF-PC synapses and MF-VN synapses. Although several theoretical models have addressed the question of ...

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“…The first is due to the reduced ability of MFs, compared with PFs, to generate sequences of non-recurrent states (Yamazaki and Tanaka, 2007, 2009; Yamazaki and Nagao, 2012). The learning rule in Equation 4 would lead synaptic weights to their local maximum values (one activity value per different state) allowing plasticity to store temporally correlated information.…”

Section: Methodsmentioning

confidence: 99%

Distributed cerebellar plasticity implements adaptable gain control in a manipulation task: a closed-loop robotic simulation

Garrido

Luque

D’Angelo

et al. 2013

Front. Neural Circuits

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Adaptable gain regulation is at the core of the forward controller operation performed by the cerebro-cerebellar loops and it allows the intensity of motor acts to be finely tuned in a predictive manner. In order to learn and store information about body-object dynamics and to generate an internal model of movement, the cerebellum is thought to employ long-term synaptic plasticity. LTD at the PF-PC synapse has classically been assumed to subserve this function (Marr, 1969). However, this plasticity alone cannot account for the broad dynamic ranges and time scales of cerebellar adaptation. We therefore tested the role of plasticity distributed over multiple synaptic sites (Hansel et al., 2001; Gao et al., 2012) by generating an analog cerebellar model embedded into a control loop connected to a robotic simulator. The robot used a three-joint arm and performed repetitive fast manipulations with different masses along an 8-shape trajectory. In accordance with biological evidence, the cerebellum model was endowed with both LTD and LTP at the PF-PC, MF-DCN and PC-DCN synapses. This resulted in a network scheme whose effectiveness was extended considerably compared to one including just PF-PC synaptic plasticity. Indeed, the system including distributed plasticity reliably self-adapted to manipulate different masses and to learn the arm-object dynamics over a time course that included fast learning and consolidation, along the lines of what has been observed in behavioral tests. In particular, PF-PC plasticity operated as a time correlator between the actual input state and the system error, while MF-DCN and PC-DCN plasticity played a key role in generating the gain controller. This model suggests that distributed synaptic plasticity allows generation of the complex learning properties of the cerebellum. The incorporation of further plasticity mechanisms and of spiking signal processing will allow this concept to be extended in a more realistic computational scenario.

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A Computational Mechanism for Unified Gain and Timing Control in the Cerebellum

Cited by 67 publications

References 62 publications

Adaptive Robotic Control Driven by a Versatile Spiking Cerebellar Network

Adaptive Robotic Control Driven by a Versatile Spiking Cerebellar Network

Modeling memory consolidation during posttraining periods in cerebellovestibular learning

Distributed cerebellar plasticity implements adaptable gain control in a manipulation task: a closed-loop robotic simulation

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