Quasiperiodic rhythms of the inferior olive

The cerebellum modulates thirst

The cerebellum, a phylogenetically ancient brain region, has long been considered strictly a motor control structure. Recent studies have implicated the cerebellum in cognition, sensation, emotion and autonomic function, making it an important target for further investigation. Here, we show that cerebellar Purkinje neurons in mice are activated by the hormone asprosin, leading to enhanced thirst, and that optogenetic or chemogenetic activation of Purkinje neurons induces rapid manifestation of water drinking. Purkinje neuron-specific asprosin receptor (Ptprd) deletion results in reduced water intake without affecting food intake and abolishes asprosin's dipsogenic effect. Purkinje neuron-mediated motor learning and coordination were unaffected by these manipulations, indicating independent control of two divergent functions by Purkinje neurons. Our results show that the cerebellum is a thirst-modulating brain area and that asprosin-Ptprd signaling may be a potential therapeutic target for the management of thirst disorders.

ExaFlexHH: an exascale-ready, flexible multi-FPGA library for biologically plausible brain simulations

Introduction

In-silico simulations are a powerful tool in modern neuroscience for enhancing our understanding of complex brain systems at various physiological levels. To model biologically realistic and detailed systems, an ideal simulation platform must possess: (1) high performance and performance scalability, (2) flexibility, and (3) ease of use for non-technical users. However, most existing platforms and libraries do not meet all three criteria, particularly for complex models such as the Hodgkin-Huxley (HH) model or for complex neuron-connectivity modeling such as gap junctions.

Methods

This work introduces ExaFlexHH, an exascale-ready, flexible library for simulating HH models on multi-FPGA platforms. Utilizing FPGA-based Data-Flow Engines (DFEs) and the dataflow programming paradigm, ExaFlexHH addresses all three requirements. The library is also parameterizable and compliant with NeuroML, a prominent brain-description language in computational neuroscience. We demonstrate the performance scalability of the platform by implementing a highly demanding extended-Hodgkin-Huxley (eHH) model of the Inferior Olive using ExaFlexHH.

Results

Model simulation results show linear scalability for unconnected networks and near-linear scalability for networks with complex synaptic plasticity, with a 1.99 × performance increase using two FPGAs compared to a single FPGA simulation, and 7.96 × when using eight FPGAs in a scalable ring topology. Notably, our results also reveal consistent performance efficiency in GFLOPS per watt, further facilitating exascale-ready computing speeds and pushing the boundaries of future brain-simulation platforms.

Discussion

The ExaFlexHH library shows superior resource efficiency, quantified in FLOPS per hardware resources, benchmarked against other competitive FPGA-based brain simulation implementations.

Mesoscale simulations predict the role of synergistic cerebellar plasticity during classical eyeblink conditioning

According to the motor learning theory by Albus and Ito, synaptic depression at the parallel fibre to Purkinje cells synapse (pf-PC) is the main substrate responsible for learning sensorimotor contingencies under climbing fibre control. However, recent experimental evidence challenges this relatively monopolistic view of cerebellar learning. Bidirectional plasticity appears crucial for learning, in which different microzones can undergo opposite changes of synaptic strength (e.g. downbound microzones-more likely depression, upbound microzones-more likely potentiation), and multiple forms of plasticity have been identified, distributed over different cerebellar circuit synapses. Here, we have simulated classical eyeblink conditioning (CEBC) using an advanced spiking cerebellar model embedding downbound and upbound modules that are subject to multiple plasticity rules. Simulations indicate that synaptic plasticity regulates the cascade of precise spiking patterns spreading throughout the cerebellar cortex and cerebellar nuclei. CEBC was supported by plasticity at the pf-PC synapses as well as at the synapses of the molecular layer interneurons (MLIs), but only the combined switch-off of both sites of plasticity compromised learning significantly. By differentially engaging climbing fibre information and related forms of synaptic plasticity, both microzones contributed to generate a well-timed conditioned response, but it was the downbound module that played the major role in this process. The outcomes of our simulations closely align with the behavioural and electrophysiological phenotypes of mutant mice suffering from cell-specific mutations that affect processing of their PC and/or MLI synapses. Our data highlight that a synergy of bidirectional plasticity rules distributed across the cerebellum can facilitate finetuning of adaptive associative behaviours at a high spatiotemporal resolution.

The integrated brain network that controls respiration

Respiration is a brain function on which our lives essentially depend. Control of respiration ensures that the frequency and depth of breathing adapt continuously to metabolic needs. In addition, the respiratory control network of the brain has to organize muscular synergies that integrate ventilation with posture and body movement. Finally, respiration is coupled to cardiovascular function and emotion. Here, we argue that the brain can handle this all by integrating a brainstem central pattern generator circuit in a larger network that also comprises the cerebellum. Although currently not generally recognized as a respiratory control center, the cerebellum is well known for its coordinating and modulating role in motor behavior, as well as for its role in the autonomic nervous system. In this review, we discuss the role of brain regions involved in the control of respiration, and their anatomical and functional interactions. We discuss how sensory feedback can result in adaptation of respiration, and how these mechanisms can be compromised by various neurological and psychological disorders. Finally, we demonstrate how the respiratory pattern generators are part of a larger and integrated network of respiratory brain regions.

Purkinje Cell Activity Resonation Generates Rhythmic Behaviors at the Preferred Frequency of 8 Hz

Neural activity exhibits oscillations, bursts, and resonance, enhancing responsiveness at preferential frequencies. For example, theta-frequency bursting and resonance in granule cells facilitate synaptic transmission and plasticity mechanisms at the input stage of the cerebellar cortex. However, whether theta-frequency bursting of Purkinje cells is involved in generating rhythmic behavior has remained neglected. We recorded and optogenetically modulated the simple and complex spike activity of Purkinje cells while monitoring whisker movements with a high-speed camera of awake, head-fixed mice. During spontaneous whisking, both simple spike activity and whisker movement exhibit peaks within the theta band. Eliciting either simple or complex spikes at frequencies ranging from 0.5 to 28 Hz, we found that 8 Hz is the preferred frequency around which the largest movement is induced. Interestingly, oscillatory whisker movements at 8 Hz were also generated when simple spike bursting was induced at 2 and 4 Hz, but never via climbing fiber stimulation. These results indicate that 8 Hz is the resonant frequency at which the cerebellar-whisker circuitry produces rhythmic whisking.

A review of motor neural system robotic modeling approaches and instruments

In this review, we are considering an actively developing tool in neuroscience-robotic modeling. The new perspective and existing application fields, tools, and methods are discussed. We try to determine starting positions and approaches that are useful at the beginning of new research in this field. Among multiple directions of the research is robotic modeling on the level of muscles fibers and their afferents, skin surface sensors, muscles, and joints proprioceptors. Some examples of technical implementation for physical modeling are reviewed. They are software and hardware tools like event-related modeling algorithms, reduced neuron models, robotic drives constructions. We observe existing drives technologies and prospective electric motor types: switched reluctance and transverse flux motors. Next, we look at the existing examples and approaches for robotic modeling of the cerebellum and spinal cord neural networks. These examples show practical methods for the model neural network architecture and adaptation. Those methods allow the use of cortical and spinal cord reflexes for the network training and apply additional artificial blocks for data processing in other brain structures that transmit and receive data from biologically realistic models.

Designing AAV Vectors for Monitoring the Subtle Calcium Fluctuations of Inferior Olive Network in vivo

Adeno-associated viral (AAV) vectors, used as vehicles for gene transfer into the brain, are a versatile and powerful tool of modern neuroscience that allow identifying specific neuronal populations, monitoring and modulating their activity. For consistent and reproducible results, the AAV vectors must be engineered so that they reliably and accurately target cell populations. Furthermore, transgene expression must be adjusted to sufficient and safe levels compatible with the physiology of studied cells. We undertook the effort to identify and validate an AAV vector that could be utilized for researching the inferior olivary (IO) nucleus, a structure gating critical timing-related signals to the cerebellum. By means of systematic construct generation and quantitative expression profiling, we succeeded in creating a viral tool for specific and strong transfection of the IO neurons without adverse effects on their physiology. The potential of these tools is demonstrated by expressing the calcium sensor GCaMP6s in adult mouse IO neurons. We could monitor subtle calcium fluctuations underlying two signatures of intrinsic IO activity: the subthreshold oscillations (STOs) and the variable-duration action potential waveforms both in-vitro and in-vivo. Further, we show that the expression levels of GCaMP6s allowing such recordings are compatible with the delicate calcium-based dynamics of IO neurons, inviting future work into the network dynamics of the olivo-cerebellar system in behaving animals.

Olivocerebellar control of movement symmetry

Coordination of bilateral movements is essential for a large variety of animal behaviors. The olivocerebellar system is critical for the control of movement, but its role in bilateral coordination has yet to be elucidated. Here, we examined whether Purkinje cells encode and influence synchronicity of left-right whisker movements. We found that complex spike activity is correlated with a prominent left-right symmetry of spontaneous whisker movements within parts, but not all, of Crus1 and Crus2. Optogenetic stimulation of climbing fibers in the areas with high and low correlations resulted in symmetric and asymmetric whisker movements, respectively. Moreover, when simple spike frequency prior to the complex spike was higher, the complex spike-related symmetric whisker protractions were larger. This finding alludes to a role for rebound activity in the cerebellar nuclei, which indeed turned out to be enhanced during symmetric protractions. Tracer injections suggest that regions associated with symmetric whisker movements are anatomically connected to the contralateral cerebellar hemisphere. Together, these data point toward the existence of modules on both sides of the cerebellar cortex that can differentially promote or reduce the symmetry of left and right movements in a context-dependent fashion.

Purkinje cells translate subjective salience into readiness to act and choice performance

The brain selectively allocates attention from a continuous stream of sensory input. This process is typically attributed to computations in distinct regions of the forebrain and midbrain. Here, we explore whether cerebellar Purkinje cells encode information about the selection of sensory inputs and could thereby contribute to non-motor forms of learning. We show that complex spikes of individual Purkinje cells change the sensory modality they encode to reflect changes in the perceived salience of sensory input. Comparisons with mouse models deficient in cerebellar plasticity suggest that changes in complex spike activity instruct potentiation of Purkinje cells simple spike firing, which is required for efficient learning. Our findings suggest that during learning, climbing fibers do not directly guide motor output, but rather contribute to a general readiness to act via changes in simple spike activity, thereby bridging the sequence from non-motor to motor functions.

Input and output organization of the mesodiencephalic junction for cerebro-cerebellar communication

Most studies investigating the impact of the cerebral cortex (CC) onto the cerebellum highlight the role of the pons, which provides the mossy fibers to the cerebellum. However, cerebro‐cerebellar communication may also be mediated by the nuclei of the mesodiencephalic junction (MDJ) that project to the inferior olive (IO), which in turn provides the climbing fibers to the molecular layer. Here, we uncover the precise topographic relations of the inputs and outputs of the MDJ using multiple, classical, and transneuronal tracing methods as well as analyses of mesoscale cortical injections from Allen Mouse Brain. We show that the caudal parts of the CC predominantly project to the principal olive via the rostral MDJ and that the rostral parts of the CC predominantly project to the rostral medial accessory olive via the caudal MDJ. Moreover, using triple viral tracing technology, we show that the cerebellar nuclei directly innervate the neurons in the MDJ that receive input from CC and project to the IO. By unraveling these topographic and prominent, mono‐ and disynaptic projections through the MDJ, this work establishes that cerebro‐cerebellar communication is not only mediated by the pontine mossy fiber system, but also by the climbing fiber system.

A neural circuit state change underlying skilled movements

In motor neuroscience, state changes are hypothesized to time-lock neural assemblies coordinating complex movements, but evidence for this remains slender. We tested whether a discrete change from more autonomous to coherent spiking underlies skilled movement by imaging cerebellar Purkinje neuron complex spikes in mice making targeted forelimb-reaches. As mice learned the task, millimeter-scale spatiotemporally coherent spiking emerged ipsilateral to the reaching forelimb, and consistent neural synchronization became predictive of kinematic stereotypy. Before reach onset, spiking switched from more disordered to internally time-locked concerted spiking and silence. Optogenetic manipulations of cerebellar feedback to the inferior olive bi-directionally modulated neural synchronization and reaching direction. A simple model explained the reorganization of spiking during reaching as reflecting a discrete bifurcation in olivary network dynamics. These findings argue that to prepare learned movements, olivo-cerebellar circuits enter a self-regulated, synchronized state promoting motor coordination. State changes facilitating behavioral transitions may generalize across neural systems.

Cerebellar Purkinje cells can differentially modulate coherence between sensory and motor cortex depending on region and behavior

Activity of sensory and motor cortices is essential for sensorimotor integration. In particular, coherence between these areas may indicate binding of critical functions like perception, motor planning, action, or sleep. Evidence is accumulating that cerebellar output modulates cortical activity and coherence, but how, when, and where it does so is unclear. We studied activity in and coherence between S1 and M1 cortices during whisker stimulation in the absence and presence of optogenetic Purkinje cell stimulation in crus 1 and 2 of awake mice, eliciting strong simple spike rate modulation. Without Purkinje cell stimulation, whisker stimulation triggers fast responses in S1 and M1 involving transient coherence in a broad spectrum. Simultaneous stimulation of Purkinje cells and whiskers affects amplitude and kinetics of sensory responses in S1 and M1 and alters the estimated S1-M1 coherence in theta and gamma bands, allowing bidirectional control dependent on behavioral context. These effects are absent when Purkinje cell activation is delayed by 20 ms. Focal stimulation of Purkinje cells revealed site specificity, with cells in medial crus 2 showing the most prominent and selective impact on estimated coherence, i.e., a strong suppression in the gamma but not the theta band. Granger causality analyses and computational modeling of the involved networks suggest that Purkinje cells control S1-M1 phase consistency predominantly via ventrolateral thalamus and M1. Our results indicate that activity of sensorimotor cortices can be dynamically and functionally modulated by specific cerebellar inputs, highlighting a widespread role of the cerebellum in coordinating sensorimotor behavior.

Functional Convergence of Autonomic and Sensorimotor Processing in the Lateral Cerebellum

The cerebellum is involved in the control of voluntary and autonomic rhythmic behaviors, yet it is unclear to what extent it coordinates these in concert. We studied Purkinje cell activity during unperturbed and perturbed respiration in lobules simplex, crus 1, and crus 2. During unperturbed (eupneic) respiration, complex spike and simple spike activity encode the phase of ongoing sensorimotor processing. In contrast, when the respiratory cycle is perturbed by whisker stimulation, mice concomitantly protract their whiskers and advance their inspiration in a phase-dependent manner, preceded by increased simple spike activity. This phase advancement of respiration in response to whisker stimulation can be mimicked by optogenetic stimulation of Purkinje cells and prevented by cell-specific genetic modification of their AMPA receptors, hampering increased simple spike firing. Thus, the impact of Purkinje cell activity on respiratory control is context and phase dependent, highlighting a coordinating role for the cerebellar hemispheres in aligning autonomic and sensorimotor behaviors.

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During unperturbed respiration, Purkinje cells signal ongoing sensorimotor processing

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After perturbation, mice advance their simple spike activity, whisking, and inspiration

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Altering simple spike activity affects the impact of whisker stimulation on respiration

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Cerebellar coordination of autonomic and sensorimotor behaviors is context dependent

Imaging Subthreshold Voltage Oscillation With Cellular Resolution in the Inferior Olive in vitro

Voltage imaging with cellular resolution in mammalian brain slices is still a challenging task. Here, we describe and validate a method for delivery of the voltage-sensitive dye ANNINE-6plus (A6+) into tissue for voltage imaging that results in higher signal-to-noise ratio (SNR) than conventional bath application methods. The not fully dissolved dye was injected into the inferior olive (IO) 0, 1, or 7 days prior to acute slice preparation using stereotactic surgery. We find that the voltage imaging improves after an extended incubation period in vivo in terms of labeled volume, homogeneous neuropil labeling with saliently labeled somata, and SNR. Preparing acute slices 7 days after the dye injection, the SNR is high enough to allow single-trial recording of IO subthreshold oscillations using wide-field (network-level) as well as high-magnification (single-cell level) voltage imaging with a CMOS camera. This method is easily adaptable to other brain regions where genetically-encoded voltage sensors are prohibitively difficult to use and where an ultrafast, pure electrochromic sensor, like A6+, is required. Due to the long-lasting staining demonstrated here, the method can be combined, for example, with deep-brain imaging using implantable GRIN lenses.

Population coding in the cerebellum: a machine learning perspective

The cere resembles a feedforward, three-layer network of neurons in which the "hidden layer" consists of Purkinje cells (P-cells) and the output layer consists of deep cerebellar nucleus (DCN) neurons. In this analogy, the output of each DCN neuron is a prediction that is compared with the actual observation, resulting in an error signal that originates in the inferior olive. Efficient learning requires that the error signal reach the DCN neurons, as well as the P-cells that project onto them. However, this basic rule of learning is violated in the cerebellum: the olivary projections to the DCN are weak, particularly in adulthood. Instead, an extraordinarily strong signal is sent from the olive to the P-cells, producing complex spikes. Curiously, P-cells are grouped into small populations that converge onto single DCN neurons. Why are the P-cells organized in this way, and what is the membership criterion of each population? Here, I apply elementary mathematics from machine learning and consider the fact that P-cells that form a population exhibit a special property: they can synchronize their complex spikes, which in turn suppress activity of DCN neuron they project to. Thus complex spikes cannot only act as a teaching signal for a P-cell, but through complex spike synchrony, a P-cell population may act as a surrogate teacher for the DCN neuron that produced the erroneous output. It appears that grouping of P-cells into small populations that share a preference for error satisfies a critical requirement of efficient learning: providing error information to the output layer neuron (DCN) that was responsible for the error, as well as the hidden layer neurons (P-cells) that contributed to it. This population coding may account for several remarkable features of behavior during learning, including multiple timescales, protection from erasure, and spontaneous recovery of memory.

Electrical coupling controls dimensionality and chaotic firing of inferior olive neurons

We previously proposed, on theoretical grounds, that the cerebellum must regulate the dimensionality of its neuronal activity during motor learning and control to cope with the low firing frequency of inferior olive neurons, which form one of two major inputs to the cerebellar cortex. Such dimensionality regulation is possible via modulation of electrical coupling through the gap junctions between inferior olive neurons by inhibitory GABAergic synapses. In addition, we previously showed in simulations that intermediate coupling strengths induce chaotic firing of inferior olive neurons and increase their information carrying capacity. However, there is no in vivo experimental data supporting these two theoretical predictions. Here, we computed the levels of synchrony, dimensionality, and chaos of the inferior olive code by analyzing in vivo recordings of Purkinje cell complex spike activity in three different coupling conditions: carbenoxolone (gap junctions blocker), control, and picrotoxin (GABA-A receptor antagonist). To examine the effect of electrical coupling on dimensionality and chaotic dynamics, we first determined the physiological range of effective coupling strengths between inferior olive neurons in the three conditions using a combination of a biophysical network model of the inferior olive and a novel Bayesian model averaging approach. We found that effective coupling co-varied with synchrony and was inversely related to the dimensionality of inferior olive firing dynamics, as measured via a principal component analysis of the spike trains in each condition. Furthermore, for both the model and the data, we found an inverted U-shaped relationship between coupling strengths and complexity entropy, a measure of chaos for spiking neural data. These results are consistent with our hypothesis according to which electrical coupling regulates the dimensionality and the complexity in the inferior olive neurons in order to optimize both motor learning and control of high dimensional motor systems by the cerebellum.

Computational theory suggests that the cerebellum must decrease the dimensionality of its neuronal activity to learn and control high dimensional motor systems effectively, while being constrained by the low firing frequency of inferior olive neurons, one of the two major source of input signals to the cerebellum. We previously proposed that the cerebellum adaptively controls the dimensionality of inferior olive firing by adjusting the level of synchrony and that such control is made possible by modulating the electrical coupling strength between inferior olive neurons. Here, we developed a novel method that uses a biophysical model of the inferior olive to accurately estimate the effective coupling strengths between inferior olive neurons from in vivo recordings of spike activity in three different coupling conditions. We found that high coupling strengths induce synchronous firing and decrease the dimensionality of inferior olive firing dynamics. In contrast, intermediate coupling strengths lead to chaotic firing and increase the dimensionality of the firing dynamics. Thus, electrical coupling is a feasible mechanism to control dimensionality and chaotic firing of inferior olive neurons. In sum, our results provide insights into possible mechanisms underlying cerebellar function and, in general, a biologically plausible framework to control the dimensionality of neural coding.

Optimization of Efficient Neuron Models With Realistic Firing Dynamics. The Case of the Cerebellar Granule Cell

Biologically relevant large-scale computational models currently represent one of the main methods in neuroscience for studying information processing primitives of brain areas. However, biologically realistic neuron models tend to be computationally heavy and thus prevent these models from being part of brain-area models including thousands or even millions of neurons. The cerebellar input layer represents a canonical example of large scale networks. In particular, the cerebellar granule cells, the most numerous cells in the whole mammalian brain, have been proposed as playing a pivotal role in the creation of somato-sensorial information representations. Enhanced burst frequency (spiking resonance) in the granule cells has been proposed as facilitating the input signal transmission at the theta-frequency band (4–12 Hz), but the functional role of this cell feature in the operation of the granular layer remains largely unclear. This study aims to develop a methodological pipeline for creating neuron models that maintain biological realism and computational efficiency whilst capturing essential aspects of single-neuron processing. Therefore, we selected a light computational neuron model template (the adaptive-exponential integrate-and-fire model), whose parameters were progressively refined using an automatic parameter tuning with evolutionary algorithms (EAs). The resulting point-neuron models are suitable for reproducing the main firing properties of a realistic granule cell from electrophysiological measurements, including the spiking resonance at the theta-frequency band, repetitive firing according to a specified intensity-frequency (I-F) curve and delayed firing under current-pulse stimulation. Interestingly, the proposed model also reproduced some other emergent properties (namely, silent at rest, rheobase and negligible adaptation under depolarizing currents) even though these properties were not set in the EA as a target in the fitness function (FF), proving that these features are compatible even in computationally simple models. The proposed methodology represents a valuable tool for adjusting AdEx models according to a FF defined in the spiking regime and based on biological data. These models are appropriate for future research of the functional implication of bursting resonance at the theta band in large-scale granular layer network models.

Oculopalatal tremor following sequential medullary infarcts that did not cause hypertrophic olivary degeneration

Background

The syndrome of oculopalatal tremor is a known consequence of lesions in the dentate-olivary pathway. Hypertrophic degeneration of the inferior olive is a recognized pathological correlate of these lesions and hypothesized to cause tremorogenic olivary hypersynchrony. However, oculopalatal tremor also occurs in Alexander disease, which produces severe inferior olive degeneration without intervening hypertrophy.

Methods

Serial clinical, imaging, video-oculography and kinematic tremor recording of a patient with oculopalatal and limb tremor.

Case study

We report an unusual presentation of oculopalatal tremor and right upper extremity myorhythmia following sequential right dorsolateral and left anteromedial medullary infarcts directly involving both inferior olives. As in adult Alexander disease, our patient did not have hypertrophic olivary degeneration during 10 years of follow-up.

Conclusion

Contemporary theories have emphasized the role of cerebellar maladaptation in “shaping” oscillations generated elsewhere, the inferior olive in particular. Our patient and published Alexander disease cases illustrate that oculopalatal tremor can occur in the absence of hypertrophic olivary degeneration. Therefore, cerebellar maladaptation to any form of olivary damage may be the critical pathophysiology in producing oculopalatal tremor.