The olivary input to the cerebellum dissociates sensory events from movement plans.

Neurons in the inferior olive are thought to anatomically organize the Purkinje cells (P-cells) of the cerebellum into computational modules, but what is computed by each module? Here, we designed a saccade task in marmosets that dissociated sensory events from motor events and then recorded the complex and simple spikes of hundreds of P-cells. We found that when a visual target was presented at a random location, the olive reported the direction of that sensory event to one group of P-cells, but not to a second group. However, just before movement onset, it reported the direction of the planned movement to both groups, even if that movement was not toward the target. At the end of the movement if the subject experienced an error but chose to withhold the corrective movement, only the first group received information about the sensory prediction error. We organized the P-cells based on the information content of their olivary input and found that in the group that received sensory information, the simple spikes were suppressed during fixation, then produced a burst before saccade onset in a direction consistent with assisting the movement. In the second group, the simple spikes were not suppressed during fixation but burst near saccade deceleration in a direction consistent with stopping the movement. Thus, the olive differentiated the P-cells based on whether they would receive sensory or motor information, and this defined their contributions to control of movements as well as holding still.

Effort cost of harvest affects decisions and movement vigor of marmosets during foraging.

Our decisions are guided by how we perceive the value of an option, but this evaluation also affects how we move to acquire that option. Why should economic variables such as reward and effort alter the vigor of our movements? In theory, both the option that we choose and the vigor with which we move contribute to a measure of fitness in which the objective is to maximize rewards minus efforts, divided by time. To explore this idea, we engaged marmosets in a foraging task in which on each trial they decided whether to work by making saccades to visual targets, thus accumulating food, or to harvest by licking what they had earned. We varied the effort cost of harvest by moving the food tube with respect to the mouth. Theory predicted that the subjects should respond to the increased effort costs by choosing to work longer, stockpiling food before commencing harvest, but reduce their movement vigor to conserve energy. Indeed, in response to an increased effort cost of harvest, marmosets extended their work duration, but slowed their movements. These changes in decisions and movements coincided with changes in pupil size. As the effort cost of harvest declined, work duration decreased, the pupils dilated, and the vigor of licks and saccades increased. Thus, when acquisition of reward became effortful, the pupils constricted, the decisions exhibited delayed gratification, and the movements displayed reduced vigor.

Complex spikes perturb movements and reveal the sensorimotor map of Purkinje cells.

Computations that are performed by the cerebellar cortex are transmitted via simple spikes of Purkinje cells (P-cells) to downstream structures, but because P-cells are many synapses away from muscles, we do not know the relationship between modulation of simple spikes and control of behavior. Here, we recorded the spiking activities of hundreds of P-cells in the oculomotor vermis of marmosets during saccadic eye movements and found that following the presentation of a visual stimulus, the olivary input to a P-cell coarsely described the direction and amplitude of the visual stimulus as well as the upcoming movement. Occasionally, the complex spike occurred just before saccade onset, suppressing the P-cell's simple spikes and disrupting its output during that saccade. Remarkably, this brief suppression of simple spikes altered the saccade's trajectory by pulling the eyes toward the part of the visual space that was preferentially encoded by the olivary input to that P-cell. Thus, there is an alignment between the sensory space encoded by the complex spikes and the behavior conveyed by the simple spikes: a reduction in simple spikes is a signal to bias the ongoing movement toward the part of the sensory space preferentially encoded by the olivary input to that P-cell.

Complex spikes perturb movements, revealing the sensorimotor map of Purkinje cells.

The cerebellar cortex performs computations that are critical for control of our actions, and then transmits that information via simple spikes of Purkinje cells (P-cells) to downstream structures. However, because P-cells are many synapses away from muscles, we do not know how their output affects behavior. Furthermore, we do not know the level of abstraction, i.e., the coordinate system of the P-cell's output. Here, we recorded spiking activities of hundreds of P-cells in the oculomotor vermis of marmosets during saccadic eye movements and found that following the presentation of a visual stimulus, the olivary input to a P-cell encoded a probabilistic signal that coarsely described both the direction and the amplitude of that stimulus. When this input was present, the resulting complex spike briefly suppressed the P-cell's simple spikes, disrupting the P-cell's output during that saccade. Remarkably, this brief suppression altered the saccade's trajectory by pulling the eyes toward the part of the visual space that was preferentially encoded by the olivary input to that P-cell. Thus, analysis of behavior in the milliseconds following a complex spike unmasked how the P-cell's output influenced behavior: the preferred location in the coordinates of the visual system as conveyed probabilistically from the inferior olive to a P-cell defined the action in the coordinates of the motor system for which that P-cell's simple spikes directed behavior.

Effort cost of harvest affects decisions and movement vigor of marmosets during foraging.

Our decisions are guided by how we perceive the value of an option, but this evaluation also affects how we move to acquire that option. Why should economic variables such as reward and effort alter the vigor of our movements? In theory, both the option that we choose and the vigor with which we move contribute to a measure of fitness in which the objective is to maximize rewards minus efforts, divided by time. To explore this idea, we engaged marmosets in a foraging task in which on each trial they decided whether to work by making saccades to visual targets, thus accumulating food, or to harvest by licking what they had earned. We varied the effort cost of harvest by moving the food tube with respect to the mouth. Theory predicted that the subjects should respond to the increased effort costs by choosing to work longer, stockpiling food before commencing harvest, but reduce their movement vigor to conserve energy. Indeed, in response to an increased effort cost of harvest, marmosets extended their work duration, but slowed their movements. These changes in decisions and movements coincided with changes in pupil size. As the effort cost of harvest declined, work duration decreased, the pupils dilated, and the vigor of licks and saccades increased. Thus, when acquisition of reward became effortful, the pupils constricted, the decisions exhibited delayed gratification, and the movements displayed reduced vigor. Significance statement: Our results suggest that as the brainstem neuromodulatory circuits that control pupil size respond to effort costs, they alter computations in the brain regions that control decisions, encouraging work and delaying gratification, and the brain regions that control movements, reducing vigor and suppressing energy expenditure. This coordinated response suggests that decisions and actions are part of a single control policy that aims to maximize a variable relevant to fitness: the capture rate.

Synchronous spiking of cerebellar Purkinje cells during control of movements.

SignificanceThe information that one region of the brain transmits to another is usually viewed through the lens of firing rates. However, if the output neurons could vary the timing of their spikes, then, through synchronization, they would spotlight information that may be critical for control of behavior. Here we report that, in the cerebellum, Purkinje cell populations that share a preference for error convey, to the nucleus, when to decelerate the movement, by reducing their firing rates and temporally synchronizing the remaining spikes.

P-sort: an open-source software for cerebellar neurophysiology.

Analysis of electrophysiological data from Purkinje cells (P-cells) of the cerebellum presents unique challenges to spike sorting. Complex spikes have waveforms that vary significantly from one event to the next, raising the problem of misidentification. Even when complex spikes are detected correctly, the simple spikes may belong to a different P-cell, raising the danger of misattribution. To address these identification and attribution problems, we wrote an open-source, semiautomated software called P-sort, and then tested it by analyzing data from P-cells recorded in three species: marmosets, macaques, and mice. Like other sorting software, P-sort relies on nonlinear dimensionality reduction to cluster spikes. However, it also uses the statistical relationship between simple and complex spikes to merge disparate clusters and split a single cluster. In comparison with expert manual curation, occasionally P-sort identified significantly more complex spikes, as well as prevented misattribution of clusters. Three existing automatic sorters performed less well, particularly for identification of complex spikes. To improve the development of analysis tools for the cerebellum, we provide labeled data for 313 recording sessions, as well as statistical characteristics of waveforms and firing patterns of P-cells in three species.NEW & NOTEWORTHY Algorithms that perform spike sorting depend on waveforms to cluster spikes. However, a cerebellar Purkinje-cell produces two types of spikes; simple and complex spikes. A complex spike coincides with the suppression of generating simple spikes. Here, we recorded neurophysiological data from three species and developed a spike analysis software named P-sort that relies on this statistical property to improve both the detection and the attribution of simple and complex spikes in the cerebellum.