Enhanced learning and sensory salience in a cerebellar mouse autism model

Among the impairments manifested by autism spectrum disorder (ASD) are sometimes islands of enhanced function. Although neuronal mechanisms for enhanced functions in ASD are unknown, the cerebellum is a major site of developmental alteration, and early-life perturbation to it leads to ASD with higher likelihood than any other brain region. Here we report that a cerebellum-specific transgenic mouse model of ASD shows faster learning on a sensory evidence-accumulation task. In addition, transgenic mice showed enhanced sensitivity to touch and auditory cues, and prolonged electrophysiological responses in Purkinje-cell complex spikes and associative neocortical regions. These findings were replicated by pairing cues with optogenetic stimulation of Purkinje cells. Computational latent-state analysis of behavior revealed that both groups of mice with cerebellar perturbations exhibited enhanced focus on current rather than past information, consistent with a role for the cerebellum in retaining information in memory. We conclude that cerebellar perturbation can activate neocortex via complex spike activity and reduce reliance on prior experience, consistent with a weak-central-coherence account in which ASD traits arise from enhanced detail-oriented processing. This recasts ASD not so much as a disorder but as a variation that, in particular niches, can be adaptive.

Transcranial direct current stimulation of cerebellum alters spiking precision in cerebellar cortex: A modeling study of cellular responses

Transcranial direct current stimulation (tDCS) of the cerebellum has rapidly raised interest but the effects of tDCS on cerebellar neurons remain unclear. Assessing the cellular response to tDCS is challenging because of the uneven, highly stratified cytoarchitecture of the cerebellum, within which cellular morphologies, physiological properties, and function vary largely across several types of neurons. In this study, we combine MRI-based segmentation of the cerebellum and a finite element model of the tDCS-induced electric field (EF) inside the cerebellum to determine the field imposed on the cerebellar neurons throughout the region. We then pair the EF with multicompartment models of the Purkinje cell (PC), deep cerebellar neuron (DCN), and granule cell (GrC) and quantify the acute response of these neurons under various orientations, physiological conditions, and sequences of presynaptic stimuli. We show that cerebellar tDCS significantly modulates the postsynaptic spiking precision of the PC, which is expressed as a change in the spike count and timing in response to presynaptic stimuli. tDCS has modest effects, instead, on the PC tonic firing at rest and on the postsynaptic activity of DCN and GrC. In Purkinje cells, anodal tDCS shortens the repolarization phase following complex spikes (-14.7 ± 6.5% of baseline value, mean ± S.D.; max: -22.7%) and promotes burstiness with longer bursts compared to resting conditions. Cathodal tDCS, instead, promotes irregular spiking by enhancing somatic excitability and significantly prolongs the repolarization after complex spikes compared to baseline (+37.0 ± 28.9%, mean ± S.D.; max: +84.3%). tDCS-induced changes to the repolarization phase and firing pattern exceed 10% of the baseline values in Purkinje cells covering up to 20% of the cerebellar cortex, with the effects being distributed along the EF direction and concentrated in the area under the electrode over the cerebellum. Altogether, the acute effects of tDCS on cerebellum mainly focus on Purkinje cells and modulate the precision of the response to synaptic stimuli, thus having the largest impact when the cerebellar cortex is active. Since the spatiotemporal precision of the PC spiking is critical to learning and coordination, our results suggest cerebellar tDCS as a viable therapeutic option for disorders involving cerebellar hyperactivity such as ataxia.

Mid-lateral cerebellar complex spikes encode multiple independent reward-related signals during reinforcement learning

Although the cerebellum has been implicated in simple reward-based learning recently, the role of complex spikes (CS) and simple spikes (SS), their interaction and their relationship to complex reinforcement learning and decision making is still unclear. Here we show that in a context where a non-human primate learned to make novel visuomotor associations, classifying CS responses based on their SS properties revealed distinct cell-type specific encoding of the probability of failure after the stimulus onset and the non-human primate’s decision. In a different context, CS from the same cerebellar area also responded in a cell-type and learning independent manner to the stimulus that signaled the beginning of the trial. Both types of CS signals were independent of changes in any motor kinematics and were unlikely to instruct the concurrent SS activity through an error based mechanism, suggesting the presence of context dependent, flexible, multiple independent channels of neural encoding by CS and SS. This diversity in neural information encoding in the mid-lateral cerebellum, depending on the context and learning state, is well suited to promote exploration and acquisition of wide range of cognitive behaviors that entail flexible stimulus-action-reward relationships but not necessarily motor learning.

P-sort: an open-source software for cerebellar neurophysiology

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.

A spike-sorting method based on hierarchical clustering to discriminate extracellularly recorded simple spikes and complex spikes from cerebellar Purkinje cells

Background: In the cerebellar cortex, Purkinje cells are the only output neurons and exhibit two types of discharge. Most Purkinje cell discharges are simple spikes, which are commonly appearing action potentials exhibiting a rich variety of firing patterns with a rate of up to 400 Hz. More infrequent discharges are complex spikes, which consist of a short burst of impulses accompanied by a massive increase in dendritic Ca2+ with a firing rate of around 1 Hz. The discrimination of these spikes in extracellular single-unit recordings is not always straightforward, as their waveforms vary depending on recording conditions and intrinsic fluctuations. New Method: To discriminate complex spikes from simple spikes in the extracellular single-unit data, we developed a semiautomatic spike-sorting method based on divisive hierarchical clustering. Results: Quantitative evaluation using parallel in vivo two-photon Ca2+ imaging of Purkinje cell dendrites indicated that 96.6% of the complex spikes were detected using our spike-sorting method from extracellular single-unit recordings obtained from anesthetized mice. Comparison with Existing Method(s): No reports have conducted a quantitative evaluation of spike-sorting algorithms used for the classification of extracellular spikes recorded from cerebellar Purkinje cells. Conclusions: Our method could be expected to contribute to research in information processing in the cerebellar cortex and the development of a fully automatic spike-sorting algorithm by providing ground-truth data useful for deep learning.

Cerebellar complex spikes multiplex complementary behavioral information

Purkinje cell (PC) discharge, the only output of cerebellar cortex, involves 2 types of action potentials, high-frequency simple spikes (SSs) and low-frequency complex spikes (CSs). While there is consensus that SSs convey information needed to optimize movement kinematics, the function of CSs, determined by the PC’s climbing fiber input, remains controversial. While initially thought to be specialized in reporting information on motor error for the subsequent amendment of behavior, CSs seem to contribute to other aspects of motor behavior as well. When faced with the bewildering diversity of findings and views unraveled by highly specific tasks, one may wonder if there is just one true function with all the other attributions wrong? Or is the diversity of findings a reflection of distinct pools of PCs, each processing specific streams of information conveyed by climbing fibers? With these questions in mind, we recorded CSs from the monkey oculomotor vermis deploying a repetitive saccade task that entailed sizable motor errors as well as small amplitude saccades, correcting them. We demonstrate that, in addition to carrying error-related information, CSs carry information on the metrics of both primary and small corrective saccades in a time-specific manner, with changes in CS firing probability coupled with changes in CS duration. Furthermore, we also found CS activity that seemed to predict the upcoming events. Hence PCs receive a multiplexed climbing fiber input that merges complementary streams of information on the behavior, separable by the recipient PC because they are staggered in time.

Population calcium responses of Purkinje cells in the oculomotor cerebellum driven by non-visual input

The climbing fiber input to the cerebellum conveys instructive signals that can induce synaptic plasticity and learning by triggering complex spikes accompanied by large calcium transients in Purkinje cells. In the cerebellar flocculus, which supports oculomotor learning, complex spikes are driven by image motion on the retina, which could indicate an oculomotor error. In the same neurons, complex spikes also can be driven by non-visual signals. It has been shown that the calcium transients accompanying each complex spike can vary in amplitude, even within a given cell, therefore, we compared the calcium responses associated with the visual and non-visual inputs to floccular Purkinje cells. The calcium indicator GCaMP6f was selectively expressed in Purkinje cells, and fiber photometry was used to record the calcium responses from a population of Purkinje cells in the flocculus of awake behaving mice. During visual (optokinetic) stimuli and pairing of vestibular and visual stimuli, the calcium level increased during contraversive retinal image motion. During performance of the vestibulo-ocular reflex in the dark, calcium increased during contraversive head rotation and the associated ipsiverse eye movements. The amplitude of this non-visual calcium response was comparable to that during conditions with retinal image motion present that induce oculomotor learning. Thus, population calcium responses of Purkinje cells in the cerebellar flocculus to visual and non-visual input are similar to what has been reported previously for complex spikes, suggesting that multimodal instructive signals control the synaptic plasticity supporting oculomotor learning.

P-sort: an open-source software for cerebellar neurophysiology

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.