Significance The 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.

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, semi-automated 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 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.

Analysis of electrophysiological data from Purkinje cells (P-cells) of the cerebellum presents challenges for spike detection. 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. Here, we analyzed data from over 300 P-cells in marmosets, macaques, and mice, using an open-source, semi-automated software called P-sort that addresses the spike identification and attribution problems. 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 seemingly disparate clusters, or 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 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.

The information that the brain transmits from one region to another is often viewed through the lens of firing rates. However, if the output neurons could vary the timing of their spikes with respect to each other, then through synchronization they could highlight information that may be critical for control of behavior. In the cerebellum, the computations that are performed by the cerebellar cortex are conveyed to the nuclei via inhibition. Yet, synchronous activity entrains nucleus neurons, making them fire. Does the cerebellar cortex rely on spike synchrony within populations of Purkinje cells (P-cells) to convey information to the nucleus? We recorded from multiple P-cells while marmosets performed saccadic eye movements and organized them into populations that shared a complex spike response to error. Before movement onset, P-cells transmitted information via a rate code: the simple spike firing rates predicted the direction and velocity of the impending saccade. However, during the saccade, the spikes became temporally aligned within the population, signaling when to stop the movement. Thus, the cerebellar cortex relies on spike synchronization within a population of P-cells, not individual firing rates, to convey to the nucleus when to stop a movement.

We prefer to decline effortful rewards, but if the circumstances require it, we will move only slowly to harvest them. Why should economic variables such as reward and effort affect movement vigor? In theory, our decisions and movements both contribute to a measure of fitness in which the objective is to maximize rewards minus efforts, divided by time. To test 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. We varied the effort cost of harvest by moving the food tube with respect to the mouth. Theory predicted that the subjects would respond to the increased effort costs by working longer, stockpiling food before commencing harvest, but reduce their movement vigor to conserve energy. Indeed, in response to the increased effort costs of harvest, marmosets increased their work duration but reduced their movement vigor. 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 lick and tongue vigor increased. Thus, when acquisition of reward became effortful, there was a global change in the state of the brain: the pupils constricted, the decisions exhibited delayed gratification, and the movements displayed reduced vigor.