Limits...
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 VOR task. Three exemplificative trials of the session1 (40 trials of acquisition + 20 trial of extinction) of VOR task carried out by the neurorobot are described by displaying the IO, PC, and DCN activity. (A) 1st trial, (B) 40th trial, (C) 45th trial.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4340181&req=5

Figure 6: Real-robot VOR task. Three exemplificative trials of the session1 (40 trials of acquisition + 20 trial of extinction) of VOR task carried out by the neurorobot are described by displaying the IO, PC, and DCN activity. (A) 1st trial, (B) 40th trial, (C) 45th trial.

Mentions: The onset of the vestibular stimulus, i.e., the onset of MF activity, initiated the generation of the state coding within the GR layer, and also provided the excitatory drive to DCN cells. The decoding of the gaze error reached continuously the Purkinje cells through the IOs. The Purkinje cells in turn inhibited the DCNs. At the beginning of the acquisition phase, the Purkinje cells were spontaneously active, supplying tonic inhibition to the DCNs (Figure 6A). After acquisition, PC+ activity was decreased. Summing up all the presynaptic (constant or plastic) inputs to DCN+, DCN+ neurons began to fire so as to continuously counterbalance the head movement, minimizing the gaze error (Figure 6B). Then during extinction trials, PC activity was progressively re-increased; and DCN+ decreased the output motor commands actuating eye motion (Figure 6C).


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 VOR task. Three exemplificative trials of the session1 (40 trials of acquisition + 20 trial of extinction) of VOR task carried out by the neurorobot are described by displaying the IO, PC, and DCN activity. (A) 1st trial, (B) 40th trial, (C) 45th trial.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 6: Real-robot VOR task. Three exemplificative trials of the session1 (40 trials of acquisition + 20 trial of extinction) of VOR task carried out by the neurorobot are described by displaying the IO, PC, and DCN activity. (A) 1st trial, (B) 40th trial, (C) 45th trial.
Mentions: The onset of the vestibular stimulus, i.e., the onset of MF activity, initiated the generation of the state coding within the GR layer, and also provided the excitatory drive to DCN cells. The decoding of the gaze error reached continuously the Purkinje cells through the IOs. The Purkinje cells in turn inhibited the DCNs. At the beginning of the acquisition phase, the Purkinje cells were spontaneously active, supplying tonic inhibition to the DCNs (Figure 6A). After acquisition, PC+ activity was decreased. Summing up all the presynaptic (constant or plastic) inputs to DCN+, DCN+ neurons began to fire so as to continuously counterbalance the head movement, minimizing the gaze error (Figure 6B). Then during extinction trials, PC activity was progressively re-increased; and DCN+ decreased the output motor commands actuating eye motion (Figure 6C).

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.