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Distributed cerebellar plasticity implements generalized multiple-scale memory components in real-robot sensorimotor tasks.

Casellato C, Antonietti A, Garrido JA, Ferrigno G, D'Angelo E, Pedrocchi A - Front Comput Neurosci (2015)

Bottom Line: Moreover, if applied to real-time control of a neurorobot, the cerebellar model has to deal with a real noisy and changing environment, thus showing its robustness and effectiveness in learning.A biologically inspired cerebellar model with distributed plasticity, both at cortical and nuclear sites, has been used.Through a productive plasticity transfer from cortical to nuclear sites, the distributed cerebellar controller showed in both tasks the capability to optimize learning on multiple time-scales, to store motor memory and to effectively adapt to dynamic ranges of stimuli.

View Article: PubMed Central - PubMed

Affiliation: NeuroEngineering And Medical Robotics Laboratory, Department Electronics, Information and Bioengineering, Politecnico di Milano Milano, Italy.

ABSTRACT
The cerebellum plays a crucial role in motor learning and it acts as a predictive controller. Modeling it and embedding it into sensorimotor tasks allows us to create functional links between plasticity mechanisms, neural circuits and behavioral learning. Moreover, if applied to real-time control of a neurorobot, the cerebellar model has to deal with a real noisy and changing environment, thus showing its robustness and effectiveness in learning. A biologically inspired cerebellar model with distributed plasticity, both at cortical and nuclear sites, has been used. Two cerebellum-mediated paradigms have been designed: an associative Pavlovian task and a vestibulo-ocular reflex, with multiple sessions of acquisition and extinction and with different stimuli and perturbation patterns. The cerebellar controller succeeded to generate conditioned responses and finely tuned eye movement compensation, thus reproducing human-like behaviors. Through a productive plasticity transfer from cortical to nuclear sites, the distributed cerebellar controller showed in both tasks the capability to optimize learning on multiple time-scales, to store motor memory and to effectively adapt to dynamic ranges of stimuli.

No MeSH data available.


Real-robot two-session Pavlovian task: models comparison. The histogram reports the performances (ΔDCN) of the 1-plasticity and 3-plasticity models in the three EBCC conditions (ISI1, ISI2, and ISI3) in both sessions (s1 and s2). Bars indicate the mean across the 20 tests and the relative standard deviation. *Corresponds to significant statistical difference between the two cerebellar models (p < 0.01).
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Figure 4: Real-robot two-session Pavlovian task: models comparison. The histogram reports the performances (ΔDCN) of the 1-plasticity and 3-plasticity models in the three EBCC conditions (ISI1, ISI2, and ISI3) in both sessions (s1 and s2). Bars indicate the mean across the 20 tests and the relative standard deviation. *Corresponds to significant statistical difference between the two cerebellar models (p < 0.01).

Mentions: When comparing the two models in terms of ΔDCNs1, ΔDCNs2, and CR latency for each ISI, a significant difference came out only for the ΔDCNs2 in all the three ISIs (Figure 4): in ISI1, t = −5.8; p = 9.3289e-07; in ISI2, t = −5.8; p = 1.2288e-06; in ISI3, t = −4.6; p = 4.0573e-05. Hence, the learning in the re-testing phase was significantly faster when the neurorobot was controlled by the 3-plasticity model than by the 1-plasticity model (Figure 5A).


Distributed cerebellar plasticity implements generalized multiple-scale memory components in real-robot sensorimotor tasks.

Casellato C, Antonietti A, Garrido JA, Ferrigno G, D'Angelo E, Pedrocchi A - Front Comput Neurosci (2015)

Real-robot two-session Pavlovian task: models comparison. The histogram reports the performances (ΔDCN) of the 1-plasticity and 3-plasticity models in the three EBCC conditions (ISI1, ISI2, and ISI3) in both sessions (s1 and s2). Bars indicate the mean across the 20 tests and the relative standard deviation. *Corresponds to significant statistical difference between the two cerebellar models (p < 0.01).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4340181&req=5

Figure 4: Real-robot two-session Pavlovian task: models comparison. The histogram reports the performances (ΔDCN) of the 1-plasticity and 3-plasticity models in the three EBCC conditions (ISI1, ISI2, and ISI3) in both sessions (s1 and s2). Bars indicate the mean across the 20 tests and the relative standard deviation. *Corresponds to significant statistical difference between the two cerebellar models (p < 0.01).
Mentions: When comparing the two models in terms of ΔDCNs1, ΔDCNs2, and CR latency for each ISI, a significant difference came out only for the ΔDCNs2 in all the three ISIs (Figure 4): in ISI1, t = −5.8; p = 9.3289e-07; in ISI2, t = −5.8; p = 1.2288e-06; in ISI3, t = −4.6; p = 4.0573e-05. Hence, the learning in the re-testing phase was significantly faster when the neurorobot was controlled by the 3-plasticity model than by the 1-plasticity model (Figure 5A).

Bottom Line: Moreover, if applied to real-time control of a neurorobot, the cerebellar model has to deal with a real noisy and changing environment, thus showing its robustness and effectiveness in learning.A biologically inspired cerebellar model with distributed plasticity, both at cortical and nuclear sites, has been used.Through a productive plasticity transfer from cortical to nuclear sites, the distributed cerebellar controller showed in both tasks the capability to optimize learning on multiple time-scales, to store motor memory and to effectively adapt to dynamic ranges of stimuli.

View Article: PubMed Central - PubMed

Affiliation: NeuroEngineering And Medical Robotics Laboratory, Department Electronics, Information and Bioengineering, Politecnico di Milano Milano, Italy.

ABSTRACT
The cerebellum plays a crucial role in motor learning and it acts as a predictive controller. Modeling it and embedding it into sensorimotor tasks allows us to create functional links between plasticity mechanisms, neural circuits and behavioral learning. Moreover, if applied to real-time control of a neurorobot, the cerebellar model has to deal with a real noisy and changing environment, thus showing its robustness and effectiveness in learning. A biologically inspired cerebellar model with distributed plasticity, both at cortical and nuclear sites, has been used. Two cerebellum-mediated paradigms have been designed: an associative Pavlovian task and a vestibulo-ocular reflex, with multiple sessions of acquisition and extinction and with different stimuli and perturbation patterns. The cerebellar controller succeeded to generate conditioned responses and finely tuned eye movement compensation, thus reproducing human-like behaviors. Through a productive plasticity transfer from cortical to nuclear sites, the distributed cerebellar controller showed in both tasks the capability to optimize learning on multiple time-scales, to store motor memory and to effectively adapt to dynamic ranges of stimuli.

No MeSH data available.