It is widely believed that cerebellar plasticity is driven by climbing fiber inputs. For instance, David Marr (1969) suggested that the climbing fiber input served as a teacher for post-synaptic Purkinje cells, which has found some empirical support (Najafi & Medina, 2013).
However, the timing of complex spikes during saccade adaptation (Catz, Dicke, & Thier, 2008) suggests that climbing fiber input might not be the sole teacher in the cerebellum. In this study, Nguyen-Vu and colleagues tested the hypothesis that Purkinje cells themselves can drive adaptation of the vestibulo-ocular reflex (VOR), a form of motor learning.
In this study, Nguyen-Vu and colleagues demonstrates that there exists more than one teacher for cerebellar learning and that changes in Purkinje cell activity can drive motor learning.
The brain makes use of noisy sensory inputs to produce eye, head, or arm motion. In most instances, the brain combines this sensory information with predictions about future events. Here, we propose that Kalman filtering can account for the dynamics of both visually guided and predictive motor behaviors within one simple unifying mechanism. Our model relies on two Kalman filters: (1) one processing visual information about retinal input; and (2) one maintaining a dynamic internal memoryof target motion. The outputs of both Kalman filters are then combined in a statistically optimal manner, i.e., weighted with respect to their reliability. The model was tested on data from several smooth pursuit experiments and reproduced all major characteristics of visually guided and predictive smooth pursuit. This contrasts with the common belief that anticipatory pursuit, pursuit maintenance during target blanking, and zero-lag pursuit of sinusoidally moving targets all result from different control systems. This is the first instance of a model integrating all aspects of pursuit dynamics within one coherent and simple model and without switching between different parallel mechanisms. Our model suggests that the brain circuitry generating a pursuit command might be simpler than previously believed and only implement the functional equivalents of two Kalman filters whose outputs are optimally combined. It provides a general framework of how the brain can combine continuous sensory information with a dynamic internal memory and transform it into motor commands.
Brain-machine-interfaceI requires a set of neurons to change their activity in order to control a cursor on a screen or a robotic arm. Neural activity from the primary motor cortex or the parietal reach region (PRR) is mapped to cursor or robotic arm movements through a decoder. Monkeys can learn to modulate their neural activity to become efficient at that task. In this paper (Hwang et al. 2013), Hwang and colleagues demonstrate that the mod.ulation of neural activity during learning is shaped by the existing neural structure.
Some kids are very efficient crawler. One can therefore wonder why these kids ever start walking. This question is central to the motor control field: what motivates people to change or adapt their behavior if they are already successful?
Writing is a very common skill but becomes difficult for disabled persons. Even when the limbs are not able to move anymore, the eyes still can. Therefore a method that could enable voluntary smooth pursuit eye movements and transform them into words would reestablish communication in severely disabled persons.
Until now, no one had succeeded in eliciting reliable smooth-pursuit eye movements in humans without a moving target on the screen. Smooth-pursuit eye movements in the absence of a target was only possible for short periods of time in anticipation of target motion onset (Barnes 2008) or during transient blanking of a moving target (Orban de Xivry et al.2008) but these movements cannot be voluntarily controlled. An article by Lorenceau (2012) precisely describes such an experiment.
Reorganization of motor networks
Anodal transcranial direct current stimulation (tDCS) of the dominant hemisphere improves learning and retention of a new skill performed with the dominant hand (e.g. Reis et al. 2009). These effects have been demonstrated several times in young healthy subjects for different learning tasks. In stroke patients, both anodal tDCS of the ipsilesional hemisphere (Hummel et al. 2005) and cathodal stimulation of the contralesional hemisphere improve motor functions of the paretic hand (Fregni et al. 2005). These tDCS protocols are balancing the activities of motor areas of both hemispheres by increasing activity in the ipsilesional hemisphere and decreasing activity in the contralesional hemisphere (Stagg et al.2012) and affect how the brain is reorganized after stroke (picture, Grefkes et al. 2011)
The question here is, if cathodal tDCS of the contralesional (unaffected) hemisphere is able to improve motor function of the paretic hand, can this protocol also improve motor learning in these patients?