Multidimensional cerebellar computations for flexible kinematic control of movements

A vector calculus for neural computation in the cerebellum

Null space theory predicts that neurons generate spikes not only to produce behavior but also to prevent the undesirable effect of other neurons on behavior. In this work, we show that this competitive cancellation is essential for understanding computation in the cerebellum. In marmosets, we identified a vector for each Purkinje cell (P cell) along which its spikes displaced the eyes. Two spikes in two different P cells produced superposition of their vectors. In the resulting population activity, the spikes were canceled if their contributions were perpendicular to the intended movement. Mossy fibers provided a copy of the motor commands and the goal of the movement. Molecular layer interneurons transformed these inputs so that the P cell population predicted when the movement had reached the goal and should be stopped.

A vector calculus for neural computation in the cerebellum

Null space theory predicts that a neuron will often generate spikes not to produce behavior, but to prevent another neuron's impact on behavior. Here, we present a direct test of this theory in the brain. In the marmoset cerebellum, spike-triggered averaging identified a vector for each Purkinje cell (P-cell) along which its spikes displaced the eyes. Two spikes in two different P-cells produced superposition of their vectors. In the resulting population activity, the spikes were canceled if their contributions were perpendicular to the intended movement. Mossy fibers provided a copy of the motor commands and the sensory goal of the movement. Molecular layer interneurons transformed these inputs so that the P-cell population predicted when the movement had reached the goal and should be stopped.

GlyT2-Positive Interneurons Regulate Timing and Variability of Information Transfer in a Cerebellar-Behavioral Loop

GlyT2-positive interneurons, Golgi and Lugaro cells, reside in the input layer of the cerebellar cortex in a key position to influence information processing. Here, we examine the contribution of GlyT2-positive interneurons to network dynamics in Crus 1 of mouse lateral cerebellar cortex during free whisking. We recorded neuronal population activity using Neuropixels probes before and after chemogenetic downregulation of GlyT2-positive interneurons in male and female mice. Under resting conditions, cerebellar population activity reliably encoded whisker movements. Reductions in the activity of GlyT2-positive cells produced mild increases in neural activity which did not significantly impair these sensorimotor representations. However, reduced Golgi and Lugaro cell inhibition did increase the temporal alignment of local population network activity at the initiation of movement. These network alterations had variable impacts on behavior, producing both increases and decreases in whisking velocity. Our results suggest that inhibition mediated by GlyT2-positive interneurons primarily governs the temporal patterning of population activity, which in turn is required to support downstream cerebellar dynamics and behavioral coordination.

Comparing the Representation of a Simple Visual Stimulus across the Cerebellar Network

The cerebellum is a conserved structure of the vertebrate brain involved in the timing and calibration of movements. Its function is supported by the convergence of fibers from granule cells (GCs) and inferior olive neurons (IONs) onto Purkinje cells (PCs). Theories of cerebellar function postulate that IONs convey error signals to PCs that, paired with the contextual information provided by GCs, can instruct motor learning. Here, we use the larval zebrafish to investigate (1) how sensory representations of the same stimulus vary across GCs and IONs and (2) how PC activity reflects these two different input streams. We use population calcium imaging to measure ION and GC responses to flashes of diverse luminance and duration. First, we observe that GCs show tonic and graded responses, as opposed to IONs, whose activity peaks mostly at luminance transitions, consistently with the notion that GCs and IONs encode context and error information, respectively. Second, we show that GC activity is patterned over time: some neurons exhibit sustained responses for the entire duration of the stimulus, while in others activity ramps up with slow time constants. This activity could provide a substrate for time representation in the cerebellum. Together, our observations give support to the notion of an error signal coming from IONs and provide the first experimental evidence for a temporal patterning of GC activity over many seconds.

Computer gaming alters resting-state brain networks, enhancing cognitive and fluid intelligence in players: evidence from brain imaging-derived phenotypes-wide Mendelian randomization

The debate on whether computer gaming enhances players' cognitive function is an ongoing and contentious issue. Aiming to delve into the potential impacts of computer gaming on the players' cognitive function, we embarked on a brain imaging-derived phenotypes (IDPs)-wide Mendelian randomization (MR) study, utilizing publicly available data from a European population. Our findings indicate that computer gaming has a positive impact on fluid intelligence (odds ratio [OR] = 6.264, P = 4.361 × 10-10, 95% confidence interval [CI] 3.520-11.147) and cognitive function (OR = 3.322, P = 0.002, 95% CI 1.563-7.062). Out of the 3062 brain IDPs analyzed, only one phenotype, IDP NET100 0378, was significantly influenced by computer gaming (OR = 4.697, P = 1.10 × 10-5, 95% CI 2.357-9.361). Further MR analysis suggested that alterations in the IDP NET100 0378 caused by computer gaming may be a potential factor affecting fluid intelligence (OR = 1.076, P = 0.041, 95% CI 1.003-1.153). Our MR study lends support to the notion that computer gaming can facilitate the development of players' fluid intelligence by enhancing the connectivity between the motor cortex in the resting-state brain and key regions such as the left dorsolateral prefrontal cortex and the language center.

Recent data on the cerebellum require new models and theories

The cerebellum has been a popular topic for theoretical studies because its structure was thought to be simple. Since David Marr and James Albus related its function to motor skill learning and proposed the Marr-Albus cerebellar learning model, this theory has guided and inspired cerebellar research. In this review, we summarize the theoretical progress that has been made within this framework of error-based supervised learning. We discuss the experimental progress that demonstrates more complicated molecular and cellular mechanisms in the cerebellum as well as new cell types and recurrent connections. We also cover its involvement in diverse non-motor functions and evidence of other forms of learning. Finally, we highlight the need to explain these new experimental findings into an integrated cerebellar model that can unify its diverse computational functions.