The Role of Error-sensitivity in Motor Adaptation

When we experience an error in a motor task, we adapt our next movement to partially compensate. The process of adaptation can be modeled as u(n+1)=αu(n)+η(n)e(n) where u(n) is the motor command on trial n, α is a decay factor, e(n) is error, and η(n) represents the subjects’ sensitivity to the experienced error. Here, we explore the rules that govern the value of η(n) as well as the brain-regions that are responsible for its evaluation. In Chapter 2, we begin with a puzzle: in motor learning tasks, humans are able to modulate how much they learn from a given error. In some conditions, they learn a large amount, but in other conditions they learn only a small amount. That is, the brain selects how much it is willing to learn from error. We suggest that ‘error-sensitivity’ is modulated by the history of previous errors. What brain region is responsible for determining the amount subjects are willing to learn from an error? Adaptation is critically dependent on the cerebellum, as demonstrated by patient and lesion studies. In Chapter 3 we use transcranial direct current stimulation (tDCS) to alter the function of the cerebellum, and observe its effects on error-sensitivity. We find that increasing the excitability of the cerebellum via anodal tDCS increases the rate of learning, while decreasing iicerebellum excitability via cathodal tDCS decreases error-sensitivity. That is, we suggest the cerebellum is responsible for determining how much subjects are willing to learn from a motor error. How does the cerebellum accomplish the task of adaptation? It is has been proposed that the firing rates of the principal cells of the cerebellum, Purkinje (P-)cells, should encode movement kinematics. Yet, this has remained a long standing puzzle, as no clear encoding of movement kinematics has been found. How the cerebellum learns has been difficult to approach because the problem of encoding remains unresolved. In Chapter 4 we approach this problem from a new direction: we propose that the cerebellum is composed of micro-clusters of P-cells, organized based on their preference for error. When the cells are organized in this manner, a clear encoding of kinematics emerges

Golgi cell mediated inhibition in the cerebellar granule cell layer

The cerebellar cortex integrates multimodal information from mossy fibre (MF) and climbing fibre inputs to perform a variety of computations relating to movement, motor learning and balance. Before MF information can be combined with climbing fibre input in Purkinje cells (PCs) it must pass through the granule cell (GrC) layer wherein it is transformed by the anatomical connectivity and local inhibitory circuit. GrCs receive both tonic and phasic inhibition, the latter arising from the release of gamma aminobutyric acid (GABA) from Golgi cell (GoC) axons. However, the properties of GoC mediated inhibition and its computational significance are not well understood. I have characterised the GoC–GrC synaptic connection using paired whole-cell patch-clamp recordings. My results show that unitary GoC inputs are smaller than previously realised and are frequently mediated purely by spillover from synapses onto adjacent GrCs. I have used the dynamic clamp method to investigate how changes in the frequency and synchrony of spiking in the GoC network can affect GrC computation. I found that changes in GoC firing rate strongly modulate the gain of the GrC input–output (I–O) function, while GoC synchrony can create permissive and non-permissive windows resulting in a patternation of GrC firing that may convey a temporal signal to downstream PCs. GoCs are subject to regulation through the activation of metabotropic glutamate receptors (mGluRs) and nicotinic acetylcholine receptors (nAChRs). I have investigated how these modulatory inputs to GoCs might affect their inhibitory output and show that mGluR activation dramatically reduces GABA release while nAChR activation dramatically increases GABA release from GoCs. My results show that GoCs can exert potent inhibitory control over GrCs that could be relevant to the processing of both temporally coded and rate coded information

Life Sciences Program Tasks and Bibliography for FY 1996

This document includes information on all peer reviewed projects funded by the Office of Life and Microgravity Sciences and Applications, Life Sciences Division during fiscal year 1996. This document will be published annually and made available to scientists in the space life sciences field both as a hard copy and as an interactive Internet web page