A theory of population coding in the cerebellum In order to move accurately, the brain relies on internal models that predict the sensory consequences of motor commands. Evidence for this idea comes from human behavioral experiments [1-7] and animal lesion studies [8- 11], suggesting that the critical structure for forming internal models is the cerebellum. However, in the cerebellum it is often difficult to relate spiking activity of individual Purkinje cells (P-cells) with behavior: while for some tasks like smooth pursuit eye movements the activity of P-cells is a simple function of eye velocity [12], for most other movements such as saccades [13,14], wrist movements [15], or arm movements [16-19], it is difficult to associate activity of individual P-cells to behavior. Anatomy of the cerebellum suggests that P-cells organize in small groups, together projecting onto a single output nucleus neuron [20]. This anatomy implies that the fundamental computational unit of the cerebellum is not a single P-cell, but a population of P-cells that together converges onto a single output neuron. Thus, population coding in the cerebellum has a specific anatomical meaning: P-cells that converge onto a single output neuron together encode an aspect of behavior [21]. The critical problem is to identify the membership of each population in the living brain. Recently, we demonstrated a way to approach this problem [22]: P-cells that share the same complex spike tuning likely belong to the same population. However, identification of complex spike tuning is exceptionally difficult: complex spikes are rare events that have variable waveform durations. Indeed, the current approach relies on manual labeling of complex spikes, something that cannot be scaled to multi-contact probes. Here, three labs with expertise in marmosets, mice, and macaques have come together to develop algorithms that automate detection and attribution of complex spikes. These algorithms focus on the frequency-domain classification of spikes, and will be tested on high density multi-contact probes. Together, the algorithms and experimental procedures should significantly improve the ability of neuroscientists to tackle the question of population coding in the cerebellum, ultimately resulting in better understanding of how the cerebellum learns to precisely control movements of our body.