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Frens, M.

Publications and source records attributed to Frens, M..

2 recordsLinked to original sources

Cerebellar degeneration reduces memory resilience after extended training

Cerebellar patients are impaired in motor adaptation. Further, motor adaptation can be divided into a fast component and a slow component, and the cerebellum is known to be especially crucial for the slow component. We tested whether the cerebellar deficit in motor adaptation can be ameliorated by training paradigms targeting slow learning using four visuomotor tasks: standard, gradual, overlearning and long intertrial intervals. We measured slow learning in patients and age-matched controls using a standard paradigm developed for reaching movements by Smith in 2006. The paradigm quantifies slow learning as the magnitude of spontaneous recovery of a previously learned and washed-out adaptation. Cerebellar patients had slower learning and reached a lower level of final adaptation, as seen in previous studies. Nevertheless, both groups had robust spontaneous recovery. Moreover, spontaneous recovery was increased in both groups in the overlearning paradigm with no significant difference between them. Computational modeling suggested that increased spontaneous recovery in the Control group reflects a changed slow adaptation system. That is, in the overlearning in controls, the slow system forgot more slowly and had a slower response to errors. In contrast, the same modeling suggests that no such change in the slow system occurred in the Cerebellar group. Rather, increased spontaneous recovery in this group seems to be the result of greater accumulated slow adaptation in the overlearning. We used our modeling results to predict that overlearning should have slower adaptation in the counterperturbation. Modeling further suggested we would not see these difference in the cerebellar patients. Follow-up analysis confirmed these model predictions. Taken together, our results imply that residual slow learning in cerebellar patients is expressed during increased training trials, but the primary cerebellar deficit is not improved.

neuroscience↗

A neuroanatomically grounded optimal control model of the compensatory eye movement system

We present a working model of the compensatory eye movement system. We challenge the model with a data set of eye movements in mice (n=34) recorded in 4 different sinusoidal stimulus conditions with 36 different combinations of frequency (0.1-3.2 Hz) and amplitude (0.5-8{degrees}) in each condition. The conditions included vestibular stimulation in the dark (vestibular-ocular reflex, VOR), optokinetic stimulation (optokinetic reflex, OKR), and two combined visual/vestibular conditions (the visual-vestibular ocular reflex, vVOR, and visual suppression of the VOR, sVOR). The model successfully reproduced the eye movements in all conditions, except for minor failures to predict phase when gain was very low. Most importantly, it could explain the non-linear summation of VOR and OKR when the two reflexes are activated simultaneously during vVOR stimulation. In addition to our own data, we also reproduced the behavior of the compensatory eye movement system found in the existing literature. These include its response to sum-of-sines stimuli, its response after lesions of the nucleus prepositus hypoglossi or the flocculus, characteristics of VOR adaptation, and characteristics of drift in the dark. Our model is based on ideas of state prediction and forward modeling that have been widely used in the study of motor control. However, it represents one of the first quantitative efforts to simulate the full range of behaviors of a specific system. The model has two separate processing loops, one for vestibular stimulation and one for visual stimulation. Importantly, state prediction in the visual processing loop depends on a forward model of residual retinal slip after vestibular processing. In addition, we hypothesize that adaptation in the system is primarily adaptation of this model. In other words, VOR adaptation happens primarily in the OKR loop.

neuroscience↗