Browsing by Author "Lisberger, Stephen G"

Smooth pursuit eye movements are movements of the eyes that are used to foveate moving objects. Their precision and adaptation is believed to depend on a constellation of sites across the cerebellum, but only one region’s contribution is well characterized, the floccular complex. Here, I characterize the response properties of neurons in the oculomotor vermis, another major division of the oculomotor cerebellum whose role in pursuit remains unknown. I recorded Purkinje cells, the output neurons of this region, in two monkeys as they executed pursuit eye movements in response to step ramp target motion. The responses of these Purkinje cells in the oculomotor vermis were very different from responses that have been documented in the floccular complex. The simple spikes of these cells encoded movement direction in retinal, as opposed to muscle coordinates. They were less related to movement kinematics, and had smaller values of trial-by-trial correlations with pursuit speed, latency, and direction than their floccular complex counterparts. Unlike Purkinje cells in the floccular complex, simple spike firing rates in the oculomotor vermis remained unchanged over the course of pursuit adaptation, likely excluding the oculomotor vermis as a site of directional plasticity. Complex spikes of these Purkinje cells were only partially responsive to target motion, and did not fall into any clear opponent directional organization with simple spikes, as has been found in the floccular complex. In general, Purkinje cells in the oculomotor vermis were responsive to both pursuit and to saccadic eye movements, but maintained tuning for the direction of these movements along separate directions at a population level. Predictions of caudal fastigial nucleus activity, generated on the basis of our population of oculomotor vermal Purkinje cells, faithfully tracked moment-by-movement changes in pursuit kinematics. By contrast, these responses did not faithfully track moment-by-moments changes in saccade kinematics. These results suggest that the oculomotor vermis is likely to play a smaller role in influencing pursuit eye movements by comparison to the floccular complex.

Because the interactions between feedforward influences are inextricably linked during many motor outputs (including but not limited to walking), the contribution of descending inputs to the generation of movements is difficult to study. Here we take advantage of the relatively small number of descending neurons (DNs) in the Drosophila melanogaster model system. We first characterize the number and distribution of the DN populations, then present a novel load free preparation, which enables the study of descending control on limb movements in a context where sensory feedback can be is reduced while leaving the nervous system, musculature, and cuticle of the animal relatively intact. Lastly we use in-vivo whole cell patch clamp electrophysiology to characterize the role of individual DNs in response to specific sensory stimuli and in relationship to movement. We find that there are approximately 1100 DNs in Drosophila that are distributed across six clusters. Input from these DNs is not necessary for coordinated motor activity, which can be generated by the thoracic ganglion, but is necessary for the specific combinations of joint movements typically observed in walking. Lastly, we identify a particular cluster of DNs that are tuned to sensory stimuli and innervate the leg neuromeres. We propose that a multi-layered interaction between these DNs, other DNs, and motor circuits in the thoracic ganglia enable the diverse but well-coordinated range of motor outputs an animal might exhibit.

Motor preparation is a key component in the control of movement. It allows higher-level cognitive factors, like expectation, to enhance the speed and accuracy of planned movements. Smooth pursuit eye movements are relatively simple, voluntary eye movements that allow ocular tracking of moving objects. Smooth pursuit eye movements are driven by an integration of two signals: visual motion and visual-motor gain. Here, we use smooth pursuit eye movement in macaque monkeys as a model sensorimotor behavior to examine how motor preparation is incorporated into neural circuits responsible for controlling movement. First, we developed a behavioral paradigm that allowed rapid adaptation of expectation and shed light on how expectation-related motor preparation could be incorporated into the smooth pursuit eye movement circuit. We used blocks of trials with different blends of target speeds to influence the pursuit system’s expectation of upcoming target speed; we estimated the effect of expectation with probe trials of equal speed across the different blocks of trials. Pursuit initiation during the probe trials was faster during blocks of trials where most trials presented relatively fast-moving versus slow-moving targets. Contextual modulation of eye speed during pursuit initiation had a larger effect for low-contrast targets, consistent with a Bayesian-like computation that estimates target speed by a reliability-weighted combination between expectation and sensory evidence. Importantly, a model that adjusts the gain of visual-motor transmission predicts the behavioral effects of speed expectation and target contrast. Second, we collected single unit neurophysiological data in the smooth eye movement region of the frontal eye fields (FEFsem) while monkeys tracked targets during the speed context paradigm. Expectation of target speed is encoded as preparatory ramps of firing rate in FEFsem during fixations in advance of any target or eye movement. Following target motion onset, the preparatory activity is used in a Bayesian-like decoding computation that estimates the speed of target motion as a combination of the preparatory activity and visual motion inputs weighted according to the reliability of visual motion. Third, we evaluated what effect preparatory modulation of activity in FEFsem has on visual-motor gain during motor preparation. At the population level, FEFsem preparatory activity ramps up along “output-potent” dimensions and in parallel with a preparatory-related modulation of visual-motor gain. Taken together, these findings suggest that the pursuit system uses a visual-motor gain signal, implemented by FEFsem, to incorporate expectation-related signals into preparation for impending visual motion and smooth pursuit eye movement. This preparatory signal both dials up gain in advance of any visual motion or eye movement and influences the gain that is set during initiation of pursuit in a Bayesian-like manner.

Primates use smooth pursuit eye movements to track moving objects. Pursuit is driven by visual commands to accelerate and supported by eye velocity feedback. We show that degraded motion reliability caused by reduced coherence in a pursuit target created from a moving patch of dots reduces the eye speed during the initiation of pursuit as well as during steady-state tracking. To understand the representation of image speed during pursuit and to ask why degraded motion reduces eye speeds, we recorded from isolated single neurons in the middle temporal area of extrastriate visual cortex (MT). Smooth pursuit is driven by sensory estimates of stimulus speed represented in MT. We sought to determine whether eye speed lags behind target speed for low dot coherence because (i) the speed representation in MT is compromised or (ii) the representation of image speed remains accurate in MT and eye speed is eroded in downstream circuits. We presented moving patches of dots of varying speeds and coherences while recording with microelectrodes from neurons in area MT. During pursuit initiation, the amplitude, but not the tuning, of MT responses depends on dot coherence. The population response gets noisier as coherence reduces the amplitude of neural (and eye movement) responses. To understand how MT drives the initiation and steady-state of pursuit, we asked whether we could decode appropriate motor commands from the MT population response and what were the properties of the successful decoders. During pursuit initiation, decoding eye speed required parallel pathways in a “gain-modulated vector averaging” decoder. One pathway estimated image speed by vector averaging and the other pathway computed the gain of sensory-motor transmission from the amplitude of the MT population response. To reproduce eye speed during steady-state tracking, yet another pathway was needed in the decoder. MT population activity is overall noisier during steady-state tracking but even for low dot coherence gain-modulated vector averaging predicts eye acceleration at a time when the eye is either stable at a speed well below target speed, or even decelerating. We could not account for the failure of eye speed to accelerate to target speed based on unresponsiveness of MT to image motion: pulsing the speed or coherence of the moving dots during steady-state tracking confirmed the responsiveness of area MT throughout pursuit. Instead, we propose a parallel effect of sensory-motor gain signals on the cerebellum’s eye velocity positive motor feedback that normally sustains steady-state eye speed.