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.
Sensory stimuli are represented by noisy sensory neurons in the brain. In the cortex, most neurons of the sensory areas respond preferentially to some sensory input. For instance, in the visual cortex, some neurons respond preferentially to the edges with a specific orientation in space. In the middle temporal (MT) area, neurons respond preferentially to motion in particular direction (Albright 1984). This change in response of sensory neurons with the characteristics of the sensory input is referred to as the tuning function.
The response from many neurons needs to be pooled together in order to reconstruct the sensory input (the hidden cause) from the firing rates of neurons. There is an optimal way to combine the neurons from the population in order to reconstruct the direction of the moving stimuli (Jazayeri and Movshon 2006). An optimal combination is obtained when the weights of each neuron are adjusted with respect to their tuning function.
However, it was currently unknown of these optimal gain could arise in the brain. In this theoretical paper, Habenschuss and colleagues suggest that spike-timing dependent plasticity, an elementary plasticity rule, can lead to optimal gains.
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?
So, one of my last paper is out in JNP. It talks about how predictive smooth pursuit eye movements are affected in frontotemporal lobar degeneration (left brain) but not in Alzheimer's disease (right brain). Have a look at it and feel free to comment it below.
Usually, motor memories are created in the lab by perturbing the trajectory of the hand (e.g. force field perturbation Shadmerh and Mussa-Ivaldi, 1994) or the trajectory of the cursor (e.g. visuomotor rotation Krakauer et al. 2005). Initially, these perturbations lead to large errors that are reduced over the course of trials through learning. This learning depends on an error-dependent process (as discussed in Smith et al. 2006) that takes into account the error on one trial in order to update the motor commands for the next movements. Unfortunately, the resulting motor memories quickly fade away once the perturbation disappears. This forgetting of motor memories has been a major obstacle for the translation of motor learning paradigms to rehabilitation therapies because the beneficial effects of motor training do not last long. In a new paper, Shmuelof and colleagues present a novel technique that makes motor memories resistant to forgetting and opens up new avenues for the translation of motor learning paradigms to rehabilitation.
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?
Immobilization of an arm to favor the use of the other arm is a major component of Constrained-Induced Therapy (CIT). In stroke patients, this therapy improves the motor function of the affected hand. However, the neurological effect of this therapy has not been studied in non-patients populations.
In a recent paper published in the journal Neurology, Swiss scientists took advantage of arm immobilization after arm injury as a proxy for constrained-induced therapy in non-stroke patients. They investigated the effect of limb immobilization on brain structure, especially on gray and white matter plasticity.
or Memory interference at the single neuron level
Could Roger Federer be a world champion at tennis and table tennis at the same time? A new study suggests that it depends on the motor cortex neurons encoding those skills. If the same neurons are involved in tennis and table tennis, then the two tasks will interfere one with the other. If different sets of neurons are used, then Roger Federer could become a tennis table champion while maintaining his tennis ranking.
written by Jean-Jacques Orban de Xivry
Scientist in the motor control field.