Using extracellular low frequency signals to improve the spike sorting of cerebellar complex spikes

Organization of reward and movement signals in the basal ganglia and cerebellum

The basal ganglia and the cerebellum are major subcortical structures in the motor system. The basal ganglia have been cast as the reward center of the motor system, whereas the cerebellum is thought to be involved in adjusting sensorimotor parameters. Recent findings of reward signals in the cerebellum have challenged this dichotomous view. To compare the basal ganglia and the cerebellum directly, we recorded from oculomotor regions in both structures from the same monkeys. We partitioned the trial-by-trial variability of the neurons into reward and eye-movement signals to compare the coding across structures. Reward expectation and movement signals were the most pronounced in the output structure of the basal ganglia, intermediate in the cerebellum, and the smallest in the input structure of the basal ganglia. These findings suggest that reward and movement information is sharpened through the basal ganglia, resulting in a higher signal-to-noise ratio than in the cerebellum.

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.

Using deep neural networks to detect complex spikes of cerebellar Purkinje cells

Purkinje cell “complex spikes,” fired at perplexingly low rates, play a crucial role in cerebellum-based motor learning. Careful interpretations of these spikes require manually detecting them, since conventional online or offline spike sorting algorithms are optimized for classifying much simpler waveform morphologies. We present a novel deep learning approach for identifying complex spikes, which also measures additional relevant neurophysiological features, with an accuracy level matching that of human experts yet with very little time expenditure.

Encoding of eye movements explains reward-related activity in cerebellar simple spikes

The cerebellum exhibits both motor and reward-related signals. However, it remains unclear whether reward is processed independently from the motor command or might reflect the motor consequences of the reward drive. To test how reward-related signals interact with sensorimotor processing in the cerebellum, we recorded Purkinje cell simple spike activity in the cerebellar floccular complex while monkeys were engaged in smooth pursuit eye movement tasks. The color of the target signaled the size of the reward the monkeys would receive at the end of the target motion. When the tracking task presented a single target, both pursuit and neural activity were only slightly modulated by the reward size. The reward modulations in single cells were rarely large enough to be detected. These modulations were only significant in the population analysis when we averaged across many neurons. In two-target tasks where the monkey learned to select based on the size of the reward outcome, both behavior and neural activity adapted rapidly. In both the single- and two-target tasks, the size of the reward-related modulation matched the size of the effect of reward on behavior. Thus, unlike cortical activity in eye movement structures, the reward-related signals could not be dissociated from the motor command. These results suggest that reward information is integrated with the eye movement command upstream of the Purkinje cells in the floccular complex. Thus reward-related modulations of the simple spikes are akin to modulations found in motor behavior and not to the central processing of the reward value.NEW & NOTEWORTHY Disentangling sensorimotor and reward signals is only possible if these signals do not completely overlap. We recorded activity in the floccular complex of the cerebellum while monkeys performed tasks designed to separate representations of reward from those of movement. Activity modulation by reward could be accounted for by the coding of eye movement parameters, suggesting that reward information is already integrated into motor commands upstream of the floccular complex.

Cerebellar climbing fibers encode expected reward size

Climbing fiber inputs to the cerebellum encode error signals that instruct learning. Recently, evidence has accumulated to suggest that the cerebellum is also involved in the processing of reward. To study how rewarding events are encoded, we recorded the activity of climbing fibers when monkeys were engaged in an eye movement task. At the beginning of each trial, the monkeys were cued to the size of the reward that would be delivered upon successful completion of the trial. Climbing fiber activity increased when the monkeys were presented with a cue indicating a large reward, but not a small reward. Reward size did not modulate activity at reward delivery or during eye movements. Comparison between climbing fiber and simple spike activity indicated different interactions for coding of movement and reward. These results indicate that climbing fibers encode the expected reward size and suggest a general role of the cerebellum in associative learning beyond error correction.