A major challenge in neuroscience is to infer the sites and directions of neural plasticity that underlie learned changes in behavior. In particular, abundant feedback pathways in the brain impede reasoning about cause and effect based on neural recording data alone. We approached this problem by studying the interactions between feedback, neural activity, and plasticity in the context of vestibulo-ocular reflex learning, a closed-loop motor learning paradigm.

Our strategy was to fit a series of circuit models to a large set of neural and behavioral data. Each model differed in the strength of efference copy feedback to Purkinje cells, ranging from no feedback to very strong feedback. The primary dataset before learning was obtained from male rhesus monkeys (Macaca mulatta) trained to perform a visual fixation task, and includes neural activity from Purkinje cells in the cerebellar flocculus and horizontal eye velocity measurements in response to a wide range of vestibular and visual stimuli. Data after learning was obtained from previous publications.

Whereas each model fit the extracellular recording and behavioral data, the patterns of plasticity predicted by the models fundamentally differed, with the direction of plasticity at a key site changing from depression to potentiation as feedback strength increased. We find that models with weak or no efference copy feedback to Purkinje cells are consistent with climbing fiber-driven long term depression at parallel fiber-Purkinje cell synapses and explain all experimental observations, including paradoxical changes in neural activity during a closed-loop visual task that appear to contradict the underlying plasticity. These results demonstrate how learning-related changes in neural activity can appear to contradict the sign of the underlying plasticity when either internal feedback or feedback through the environment is present.

Neural and behavioral data before learning (“Dataset 1”) were obtained from two male rhesus monkeys (Macaca mulatta) trained to perform a visual fixation task. A subset of this dataset has been published previously (Kimpo et al., 2014; Raymond & Lisberger, 1998). Briefly, neural responses were recorded extracellularly from Purkinje cells in the floccular complex of the cerebellar cortex while the monkeys made horizontal eye movements in response to various combinations of visual and vestibular stimuli. Vestibular stimuli consisted of passive whole-body rotation in the horizontal plane. Visual stimuli consisted of a horizontally moving target subtending 0.5° of visual angle, which was accompanied by a larger black-and-white pattern subtending 20° to 30° of visual angle for all stimulus conditions except for smooth pursuit. Four combinations of visual and vestibular stimuli were delivered: head movements in the dark (“Vestibular only,” which elicits the VOR), visual target motion with the head stationary (“visual only”, which elicits smooth pursuit eye movements), visual target and head motion at the same speed but in opposite directions (“vestibular + visual”, also referred to as ×2 because accurate tracking requires compensatory eye movements that are twice as large as normal), and visual target and head motion at the same speed and in the same direction (“vestibular – visual”, also referred to as ×0 because accurate tracking requires no rotation of the eyes in their sockets, and also known as “VOR cancellation” since normal VOR eye movements are suppressed). These combinations were delivered as sine waves in stimulus velocity with frequencies of 0.5 Hz, 2 Hz, 5 Hz, and 10 Hz at ±10 ⁰/s, or as steps in stimulus velocity with durations of 80 ms, 150 ms, 250 ms, and 500 ms at 15 ⁰/s. Smooth pursuit data were only available for 0.5 Hz sine waves (delivered at ±31.4 ⁰/s), resulting in a total of 25 distinct conditions. Eye position (angle of the eye relative to the head) was measured using the scleral search coil method.

Eye velocity and neural activity from Dataset 1 were further processed as follows. Eye velocity was calculated by differentiating eye position. Saccades were removed from eye velocity traces using an automatic threshold algorithm with a threshold of 30 ⁰/s. To allow comparison across datasets and with previous studies, we analyzed horizontal gaze velocity Purkinje cells (HGVPs), which are the largest subpopulation of floccular Purkinje cells and are widely considered to be important for horizontal gaze control (Katoh et al., 2015; Lisberger, Pavelko, Bronte-Stewart, et al., 1994). Purkinje cells were considered HGVPs if (1) during smooth pursuit, firing rate was modulated by at least ±10 sp/s and the phase difference between peak firing rate and peak ipsiversive eye velocity was less than 45°; (2) during VOR cancellation, firing rate was modulated by at least ±10 sp/s and the phase difference between peak firing rate and peak ipsiversive head velocity was less than 45°; and (3) firing rate modulation was greater during horizontal than during vertical smooth pursuit. Purkinje cell firing rates were calculated by convolving raw simple spikes times with a 10 ms standard deviation Gaussian filter. Baseline firing rates were removed by subtracting a moving average calculated over an 11 s window. Eye velocity and Purkinje cell firing rates were then averaged across stimulus cycles for each cell. Neurons were only recorded on one side of the brain, so to account for the corresponding population in the opposite hemisphere, Purkinje cell responses to ipsiversive stimulation were averaged together with the inverted response to contraversive stimulation. Finally, data were averaged across all cells to create mean Purkinje cell firing rate and mean eye velocity traces for each stimulus condition.