Beyond parallel fiber LTD: the diversity of synaptic and non-synaptic plasticity in the cerebellum

Nasal obstruction during development leads to defective synapse elimination, hypersynchrony, and impaired cerebellar function

Nasal respiratory disorders are linked to craniofacial anomalies and systemic dysfunctions. However, the implications of nasal respiratory disorders on brain development and their subsequent impact on brain functionalization remain largely unknown. Here, we describe that nasal obstruction from postnatal developmental stages in mice precipitates deficits in cerebellum-associated behaviors and compromised refinement and maturation of neural circuits in the cerebellum. We show that mice with nasal obstruction during developmental phases exhibit marked impairments in motor function and exhibit increased immobility time in forced swimming test. Additionally, we identified critical periods during which nasal respiration is essential for optimizing motor function and preserving mental health. Our study also reveals that nasal obstruction in mice disrupts the typical developmental process of synapse elimination in the cerebellum and hinders the normal transition of activity patterns in cerebellar Purkinje cell populations during development. Through comparing activity patterns in mouse models subjected to nasal obstruction at various stages, we suggest that the maturation of specific activity pattern among Purkinje cell populations is fundamental to the functional integrity of the cerebellum. Our findings highlight the indispensable role of adequate nasal respiration during development for the establishment and functional integrity of neural circuits, thereby significantly affecting brain function.

Inaugural Review Prize 2023: The exercise hyperpnoea dilemma: A 21st-century perspective

During mild or moderate exercise, alveolar ventilation increases in direct proportion to metabolic rate, regulating arterial CO2 pressure near resting levels. Mechanisms giving rise to the hyperpnoea of exercise are unsettled despite over a century of investigation. In the past three decades, neuroscience has advanced tremendously, raising optimism that the 'exercise hyperpnoea dilemma' can finally be solved. In this review, new perspectives are offered in the hope of stimulating original ideas based on modern neuroscience methods and current understanding. We first describe the ventilatory control system and the challenge exercise places upon blood-gas regulation. We highlight relevant system properties, including feedforward, feedback and adaptive (i.e., plasticity) control of breathing. We then elaborate a seldom explored hypothesis that the exercise ventilatory response continuously adapts (learns and relearns) throughout life and ponder if the memory 'engram' encoding the feedforward exercise ventilatory stimulus could reside within the cerebellum. Our hypotheses are based on accumulating evidence supporting the cerebellum's role in motor learning and the numerous direct and indirect projections from deep cerebellar nuclei to brainstem respiratory neurons. We propose that cerebellar learning may be obligatory for the accurate and adjustable exercise hyperpnoea capable of tracking changes in life conditions/experiences, and that learning arises from specific cerebellar microcircuits that can be interrogated using powerful techniques such as optogenetics and chemogenetics. Although this review is speculative, we consider it essential to reframe our perspective if we are to solve the till-now intractable exercise hyperpnoea dilemma.

Neural ensembles: role of intrinsic excitability and its plasticity

Synaptic connectivity defines groups of neurons that engage in correlated activity during specific functional tasks. These co-active groups of neurons form ensembles, the operational units involved in, for example, sensory perception, motor coordination and memory (then called an engram). Traditionally, ensemble formation has been thought to occur via strengthening of synaptic connections via long-term potentiation (LTP) as a plasticity mechanism. This synaptic theory of memory arises from the learning rules formulated by Hebb and is consistent with many experimental observations. Here, we propose, as an alternative, that the intrinsic excitability of neurons and its plasticity constitute a second, non-synaptic mechanism that could be important for the initial formation of ensembles. Indeed, enhanced neural excitability is widely observed in multiple brain areas subsequent to behavioral learning. In cortical structures and the amygdala, excitability changes are often reported as transient, even though they can last tens of minutes to a few days. Perhaps it is for this reason that they have been traditionally considered as modulatory, merely supporting ensemble formation by facilitating LTP induction, without further involvement in memory function (memory allocation hypothesis). We here suggest-based on two lines of evidence-that beyond modulating LTP allocation, enhanced excitability plays a more fundamental role in learning. First, enhanced excitability constitutes a signature of active ensembles and, due to it, subthreshold synaptic connections become suprathreshold in the absence of synaptic plasticity (iceberg model). Second, enhanced excitability promotes the propagation of dendritic potentials toward the soma and allows for enhanced coupling of EPSP amplitude (LTP) to the spike output (and thus ensemble participation). This permissive gate model describes a need for permanently increased excitability, which seems at odds with its traditional consideration as a short-lived mechanism. We propose that longer modifications in excitability are made possible by a low threshold for intrinsic plasticity induction, suggesting that excitability might be on/off-modulated at short intervals. Consistent with this, in cerebellar Purkinje cells, excitability lasts days to weeks, which shows that in some circuits the duration of the phenomenon is not a limiting factor in the first place. In our model, synaptic plasticity defines the information content received by neurons through the connectivity network that they are embedded in. However, the plasticity of cell-autonomous excitability could dynamically regulate the ensemble participation of individual neurons as well as the overall activity state of an ensemble.

Plasticity mechanisms of genetically distinct Purkinje cells

Despite its uniform appearance, the cerebellar cortex is highly heterogeneous in terms of structure, genetics and physiology. Purkinje cells (PCs), the principal and sole output neurons of the cerebellar cortex, can be categorized into multiple populations that differentially express molecular markers and display distinctive physiological features. Such features include action potential rate, but also their propensity for synaptic and intrinsic plasticity. However, the precise molecular and genetic factors that correlate with the differential physiological properties of PCs remain elusive. In this article, we provide a detailed overview of the cellular mechanisms that regulate PC activity and plasticity. We further perform a pathway analysis to highlight how molecular characteristics of specific PC populations may influence their physiology and plasticity mechanisms.

Understanding Cerebellar Input Stage through Computational and Plasticity Rules

A central hypothesis concerning brain functioning is that plasticity regulates the signal transfer function by modifying the efficacy of synaptic transmission. In the cerebellum, the granular layer has been shown to control the gain of signals transmitted through the mossy fiber pathway. Until now, the impact of plasticity on incoming activity patterns has been analyzed by combining electrophysiological recordings in acute cerebellar slices and computational modeling, unraveling a broad spectrum of different forms of synaptic plasticity in the granular layer, often accompanied by forms of intrinsic excitability changes. Here, we attempt to provide a brief overview of the most prominent forms of plasticity at the excitatory synapses formed by mossy fibers onto primary neuronal components (granule cells, Golgi cells and unipolar brush cells) in the granular layer. Specifically, we highlight the current understanding of the mechanisms and their functional implications for synaptic and intrinsic plasticity, providing valuable insights into how inputs are processed and reconfigured at the cerebellar input stage.

New clues for the role of cerebellum in schizophrenia and the associated cognitive impairment

Schizophrenia (SZ) is a complex neuropsychiatric disorder associated with severe cognitive dysfunction. Although research has mainly focused on forebrain abnormalities, emerging results support the involvement of the cerebellum in SZ physiopathology, particularly in Cognitive Impairment Associated with SZ (CIAS). Besides its role in motor learning and control, the cerebellum is implicated in cognition and emotion. Recent research suggests that structural and functional changes in the cerebellum are linked to deficits in various cognitive domains including attention, working memory, and decision-making. Moreover, cerebellar dysfunction is related to altered cerebellar circuit activities and connectivity with brain regions associated with cognitive processing. This review delves into the role of the cerebellum in CIAS. We initially consider the major forebrain alterations in CIAS, addressing impairments in neurotransmitter systems, synaptic plasticity, and connectivity. We then focus on recent findings showing that several mechanisms are also altered in the cerebellum and that cerebellar communication with the forebrain is impaired. This evidence implicates the cerebellum as a key component of circuits underpinning CIAS physiopathology. Further studies addressing cerebellar involvement in SZ and CIAS are warranted and might open new perspectives toward understanding the physiopathology and effective treatment of these disorders.

Data-driven multiscale computational models of cortical and subcortical regions

Data-driven computational models of neurons, synapses, microcircuits, and mesocircuits have become essential tools in modern brain research. The goal of these multiscale models is to integrate and synthesize information from different levels of brain organization, from cellular properties, dendritic excitability, and synaptic dynamics to microcircuits, mesocircuits, and ultimately behavior. This article surveys recent advances in the genesis of data-driven computational models of mammalian neural networks in cortical and subcortical areas. I discuss the challenges and opportunities in developing data-driven multiscale models, including the need for interdisciplinary collaborations, the importance of model validation and comparison, and the potential impact on basic and translational neuroscience research. Finally, I highlight future directions and emerging technologies that will enable more comprehensive and predictive data-driven models of brain function and dysfunction.

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.

Etomidate enhances cerebellar CF-PC synaptic plasticity through CB1 receptor/PKA cascade in vitro in mice

Etomidate (ET) is a widely used intravenous imidazole general anesthetic, which depresses the cerebellar neuronal activity by modulating various receptors activity and synaptic transmission. In this study, we investigated the effects of ET on the cerebellar climbing fiber-Purkinje cells (CF-PC) plasticity in vitro in mice using whole-cell recording technique and pharmacological methods. Our results demonstrated that CF tetanic stimulation produced a mGluR1-dependent long-term depression (LTD) of CF-PC excitatory postsynaptic currents (EPSCs), which was enhanced by bath application of ET (10 µM). Blockade of mGluR1 receptor with JNJ16259685, ET triggered the tetanic stimulation to induce a CF-PC LTD accompanied with an increase in paired-pulse ratio (PPR). The ET-triggered CF-PC LTD was abolished by extracellular administration of an N-methyl-(D)-aspartate (NMDA) receptor antagonist, D-APV, as well as by intracellular blockade of NMDA receptors activity with MK801. Furthermore, blocking cannabinoids 1 (CB1) receptor with AM251 or chelating intracellular Ca2+ with BAPTA, ET failed to trigger the CF-PC LTD. Moreover, the ET-triggered CF-PC LTD was abolished by inhibition of protein kinase A (PKA), but not by inhibition of protein kinase C inhibiter. The present results suggest that ET acts on postsynaptic NMDA receptor resulting in an enhancement of the cerebellar CF-PC LTD through CB1 receptor/PKA cascade in vitro in mice. These results provide new evidence and possible mechanism for ET anesthesia to affect motor learning and motor coordination by regulating cerebellar CF-PC LTD.

Biology of cognitive aging across species

Aging is associated with cognitive decline, which can critically affect quality of life. Examining the biology of cognitive aging across species will lead to a better understanding of the fundamental mechanisms involved in this process, and identify potential interventions that could help to improve cognitive function in aging individuals. This minireview aimed to explore the mechanisms and processes involved in cognitive aging across a range of species, from flies to rodents, and covers topics, such as the role of reactive oxygen species and autophagy/mitophagy in cognitive aging. Overall, this literature provides a comprehensive overview of the biology of cognitive aging across species, highlighting the latest research findings and identifying potential avenues for future research. Geriatr Gerontol Int 2024; 24: 15-24.

Intrinsic plasticity of Purkinje cell serves homeostatic regulation of fear memory

Two forms of plasticity, synaptic and intrinsic, are neural substrates for learning and memory. Abnormalities in homeostatic plasticity cause severe neuropsychiatric diseases, such as schizophrenia and autism. This suggests that the balance between synaptic transmission and intrinsic excitability is important for physiological function in the brain. Despite the established role of synaptic plasticity between parallel fiber (PF) and Purkinje cell (PC) in fear memory, its relationship with intrinsic plasticity is not well understood. Here, patch clamp recording revealed depression of intrinsic excitability in PC following auditory fear conditioning (AFC). Depressed excitability balanced long-term potentiation of PF-PC synapse to serve homeostatic regulation of PF-evoked PC firing. We then optogenetically manipulated PC excitability during the early consolidation period resulting in bidirectional regulation of fear memory. Fear conditioning-induced synaptic plasticity was also regulated following optogenetic manipulation. These results propose intrinsic plasticity in PC as a novel mechanism of fear memory and elucidate that decreased intrinsic excitability in PC counterbalances PF-PC synaptic potentiation to maintain fear memory in a normal range.

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.

Randomized clinical trial on the effects of a computerized cognitive training for pediatric patients with acquired brain injury or congenital malformation

Both acquired injuries and congenital malformations often cause lifelong disabilities in children, with a significant impact on cognitive abilities. Remote computerized cognitive training (CCT) may be delivered in ecological settings to favour rehabilitation continuity. This randomized clinical trial (RCT) evaluated the efficacy of an 8-week multi-domain, home-based CCT in a sample of patients aged 11-16 years with non-progressive acquired brain injury (ABI), brain tumor (BT) and congenital brain malformation (CBM). Following a stepped-wedge research design, patients were randomized into two groups: Training-first group, which started the CCT immediately after baseline assessment and Waiting-first group, which started the CCT after a period of time comparable to that required by the training (8 weeks). Post-training and long-term (6 months) changes were assessed. Both groups improved on visual-spatial working memory after the CCT, with benefits maintained after 6 months, while no other changes in cognitive or psychological measures were found. These findings suggest that a multi-domain CCT can generate benefits in visual-spatial working memory, in accordance with data from extant literature reporting that computer games heavily engage visuo-spatial abilities. We speculate that is tapping on the same cognitive ability with a prolonged training that may generate the greatest change after a CCT.

Synaptic variance and action potential firing of cerebellar output neurons during motor learning in larval zebrafish

The cerebellum regulates both reflexive and acquired movements. Here, by recording voltage-clamped synaptic currents and spiking in cerebellar output (eurydendroid) neurons in immobilized larval zebrafish, we investigated synaptic integration during reflexive movements and throughout associative motor learning. Spiking coincides with the onset of reflexive fictive swimming but precedes learned swimming, suggesting that eurydendroid signals may facilitate the initiation of acquired movements. Although firing rates increase during swimming, mean synaptic inhibition greatly exceeds mean excitation, indicating that learned responses cannot result solely from changes in synaptic weight or upstream excitability that favor excitation. Estimates of spike threshold crossings based on measurements of intrinsic properties and the time course of synaptic currents demonstrate that noisy excitation can transiently outweigh noisy inhibition enough to increase firing rates at swimming onset. Thus, the millisecond-scale variance of synaptic currents can regulate cerebellar output, and the emergence of learned cerebellar behaviors may involve a time-based code.

Intrinsic threshold plasticity: cholinergic activation and role in the neuronal recognition of incomplete input patterns

Activity-dependent changes in membrane excitability are observed in neurons across brain areas and represent a cell-autonomous form of plasticity (intrinsic plasticity; IP) that in itself does not involve alterations in synaptic strength (synaptic plasticity; SP). Non-homeostatic IP may play an essential role in learning, e.g. by changing the action potential threshold near the soma. A computational problem, however, arises from the implication that such amplification does not discriminate between synaptic inputs and therefore may reduce the resolution of input representation. Here, we investigate consequences of IP for the performance of an artificial neural network in (a) the discrimination of unknown input patterns and (b) the recognition of known/learned patterns. While negative changes in threshold potentials in the output layer indeed reduce its ability to discriminate patterns, they benefit the recognition of known but incompletely presented patterns. An analysis of thresholds and IP-induced threshold changes in published sets of physiological data obtained from whole-cell patch-clamp recordings from L2/3 pyramidal neurons in (a) the primary visual cortex (V1) of awake macaques and (b) the primary somatosensory cortex (S1) of mice in vitro, respectively, reveals a difference between resting and threshold potentials of ∼15 mV for V1 and ∼25 mV for S1, and a total plasticity range of ∼10 mV (S1). The most efficient activity pattern to lower threshold is paired cholinergic and electric activation. Our findings show that threshold reduction promotes a shift in neural coding strategies from accurate faithful representation to interpretative assignment of input patterns to learned object categories. KEY POINTS: Intrinsic plasticity may change the action potential threshold near the soma of neurons (threshold plasticity), thus altering the input-output function for all synaptic inputs 'upstream' of the plasticity location. A potential problem arising from this shared amplification is that it may reduce the ability to discriminate between different input patterns. Here, we assess the performance of an artificial neural network in the discrimination of unknown input patterns as well as the recognition of known patterns subsequent to changes in the spike threshold. We observe that negative changes in threshold potentials do reduce discrimination performance, but at the same time improve performance in an object recognition task, in particular when patterns are incompletely presented. Analysis of whole-cell patch-clamp recordings from pyramidal neurons in the primary somatosensory cortex (S1) of mice reveals that negative threshold changes preferentially result from electric stimulation of neurons paired with the activation of muscarinic acetylcholine receptors.

Pre-ataxic loss of intrinsic plasticity and motor learning in a mouse model of SCA1

Spinocerebellar ataxias are neurodegenerative diseases, the hallmark symptom of which is the development of ataxia due to cerebellar dysfunction. Purkinje cells, the principal neurons of the cerebellar cortex, are the main cells affected in these disorders, but the sequence of pathological events leading to their dysfunction is poorly understood. Understanding the origins of Purkinje cells dysfunction before it manifests is imperative to interpret the functional and behavioural consequences of cerebellar-related disorders, providing an optimal timeline for therapeutic interventions. Here, we report the cascade of events leading to Purkinje cells dysfunction before the onset of ataxia in a mouse model of spinocerebellar ataxia 1 (SCA1). Spatiotemporal characterization of the ATXN1[82Q] SCA1 mouse model revealed high levels of the mutant ATXN1[82Q] weeks before the onset of ataxia. The expression of the toxic protein first caused a reduction of Purkinje cells intrinsic excitability, which was followed by atrophy of Purkinje cells dendrite arborization and aberrant glutamatergic signalling, finally leading to disruption of Purkinje cells innervation of climbing fibres and loss of intrinsic plasticity of Purkinje cells. Functionally, we found that deficits in eyeblink conditioning, a form of cerebellum-dependent motor learning, precede the onset of ataxia, matching the timeline of climbing fibre degeneration and reduced intrinsic plasticity. Together, our results suggest that abnormal synaptic signalling and intrinsic plasticity during the pre-ataxia stage of spinocerebellar ataxias underlie an aberrant cerebellar circuitry that anticipates the full extent of the disease severity. Furthermore, our work indicates the potential for eyeblink conditioning to be used as a sensitive tool to detect early cerebellar dysfunction as a sign of future disease.

Effect of a differential training paradigm with varying frequencies and amplitudes on adaptation of vestibulo-ocular reflex in mice

The vestibulo-ocular reflex (VOR) functions to maintain eye stability during head movement, and VOR gain can be dynamically increased or decreased by gain-up or gain-down adaptation. In this study, we investigated the impact of a differential training paradigm with varying frequencies and amplitudes on the level of VOR adaptation in mice. Training for gain-up (out of phase) or gain-down (in phase) VOR adaptation was applied for 60 min using two protocols: (1) oscillation of a drum and turntable with fixed frequency and differing amplitudes (0.5 Hz/2.5°, 0.5 Hz/5° and 0.5 Hz/10°). (2) Oscillation of a drum and turntable with fixed amplitude and a differing frequency (0.25 Hz/5°, 0.5 Hz/5° and 1 Hz/5°). VOR adaptation occurred distinctively in gain-up and gain-down learning. In gain-up VOR adaptation, the learned increase in VOR gain was greatest when trained with the same frequency and amplitude as the test stimulation, and VOR gain decreased after gain-up training with too high a frequency or amplitude. In gain-down VOR adaptation, the decrease in VOR gain increased as the training frequency or amplitude increased. These results suggest that different mechanisms are, at least in part, involved in gain-up and gain-down VOR adaptation.

Discrimination and learning of temporal input sequences in a cerebellar Purkinje cell model

Introduction

Temporal information processing is essential for sequential contraction of various muscles with the appropriate timing and amplitude for fast and smooth motor control. These functions depend on dynamics of neural circuits, which consist of simple neurons that accumulate incoming spikes and emit other spikes. However, recent studies indicate that individual neurons can perform complex information processing through the nonlinear dynamics of dendrites with complex shapes and ion channels. Although we have extensive evidence that cerebellar circuits play a vital role in motor control, studies investigating the computational ability of single Purkinje cells are few.

Methods

We found, through computer simulations, that a Purkinje cell can discriminate a series of pulses in two directions (from dendrite tip to soma, and from soma to dendrite), as cortical pyramidal cells do. Such direction sensitivity was observed in whatever compartment types of dendrites (spiny, smooth, and main), although they have dierent sets of ion channels.

Results

We found that the shortest and longest discriminable sequences lasted for 60 ms (6 pulses with 10 ms interval) and 4,000 ms (20 pulses with 200 ms interval), respectively. and that the ratio of discriminable sequences within the region of the interesting parameter space was, on average, 3.3% (spiny), 3.2% (smooth), and 1.0% (main). For the direction sensitivity, a T-type Ca²⁺ channel was necessary, in contrast with cortical pyramidal cells that have N-methyl-D-aspartate receptors (NMDARs). Furthermore, we tested whether the stimulus direction can be reversed by learning, specifically by simulated long-term depression, and obtained positive results.

Discussion

Our results show that individual Purkinje cells can perform more complex information processing than is conventionally assumed for a single neuron, and suggest that Purkinje cells act as sequence discriminators, a useful role in motor control and learning.

A computational passage-of-time model of the cerebellar Purkinje cell in eyeblink conditioning

The cerebellar Purkinje cell controlling eyeblinks can learn, remember, and reproduce the interstimulus interval in a classical conditioning paradigm. Given temporally separated inputs, the cerebellar Purkinje cell learns to pause its tonic inhibition of a motor pathway with high temporal precision so that an overt blink occurs at the right time. Most models place the passage-of-time representation in upstream network effects. Yet, bypassing the upstream network and directly stimulating the Purkinje cell's pre-synaptic fibers during conditioning still causes acquisition of a well-timed response. Additionally, while network models are sensitive to variance in the temporal structure of probe stimulation, in vivo findings suggest that the acquired Purkinje cell response is not. Such findings motivate alternative approaches to modeling neural function. Here, we present a proof-of-principle model of the passage-of-time which is internal to the Purkinje cell and is invariant to probe structure. The model is consistent with puzzling findings, accurately recapitulates Purkinje cell firing during classical conditioning and makes testable electrophysiological predictions.

Computational models of neurotransmission at cerebellar synapses unveil the impact on network computation

The neuroscientific field benefits from the conjoint evolution of experimental and computational techniques, allowing for the reconstruction and simulation of complex models of neurons and synapses. Chemical synapses are characterized by presynaptic vesicle cycling, neurotransmitter diffusion, and postsynaptic receptor activation, which eventually lead to postsynaptic currents and subsequent membrane potential changes. These mechanisms have been accurately modeled for different synapses and receptor types (AMPA, NMDA, and GABA) of the cerebellar cortical network, allowing simulation of their impact on computation. Of special relevance is short-term synaptic plasticity, which generates spatiotemporal filtering in local microcircuits and controls burst transmission and information flow through the network. Here, we present how data-driven computational models recapitulate the properties of neurotransmission at cerebellar synapses. The simulation of microcircuit models is starting to reveal how diverse synaptic mechanisms shape the spatiotemporal profiles of circuit activity and computation.

Dual STDP processes at Purkinje cells contribute to distinct improvements in accuracy and speed of saccadic eye movements

Saccadic eye-movements play a crucial role in visuo-motor control by allowing rapid foveation onto new targets. However, the neural processes governing saccades adaptation are not fully understood. Saccades, due to the short-time of execution (20-100 ms) and the absence of sensory information for online feedback control, must be controlled in a ballistic manner. Incomplete measurements of the movement trajectory, such as the visual endpoint error, are supposedly used to form internal predictions about the movement kinematics resulting in predictive control. In order to characterize the synaptic and neural circuit mechanisms underlying predictive saccadic control, we have reconstructed the saccadic system in a digital controller embedding a spiking neural network of the cerebellum with spike timing-dependent plasticity (STDP) rules driving parallel fiber-Purkinje cell long-term potentiation and depression (LTP and LTD). This model implements a control policy based on a dual plasticity mechanism, resulting in the identification of the roles of LTP and LTD in regulating the overall quality of saccade kinematics: it turns out that LTD increases the accuracy by decreasing visual error and LTP increases the peak speed. The control policy also required cerebellar PCs to be divided into two subpopulations, characterized by burst or pause responses. To our knowledge, this is the first model that explains in mechanistic terms the visual error and peak speed regulation of ballistic eye movements in forward mode exploiting spike-timing to regulate firing in different populations of the neuronal network. This elementary model of saccades could be extended and applied to other more complex cases in which single jerks are concatenated to compose articulated and coordinated movements.

Sensory Over-responsivity and Aberrant Plasticity in Cerebellar Cortex in a Mouse Model of Syndromic Autism

Background

Patients with autism spectrum disorder often show altered responses to sensory stimuli as well as motor deficits, including an impairment of delay eyeblink conditioning, which involves integration of sensory signals in the cerebellum. Here, we identify abnormalities in parallel fiber (PF) and climbing fiber (CF) signaling in the mouse cerebellar cortex that may contribute to these pathologies.

Methods

We used a mouse model for the human 15q11-13 duplication (patDp/+) and studied responses to sensory stimuli in Purkinje cells from awake mice using two-photon imaging of GCaMP6f signals. Moreover, we examined synaptic transmission and plasticity using in vitro electrophysiological, immunohistochemical, and confocal microscopic techniques.

Results

We found that spontaneous and sensory-evoked CF-calcium transients are enhanced in patDp/+ Purkinje cells, and aversive movements are more severe across sensory modalities. We observed increased expression of the synaptic organizer NRXN1 at CF synapses and ectopic spread of these synapses to fine dendrites. CF-excitatory postsynaptic currents recorded from Purkinje cells are enlarged in patDp/+ mice, while responses to PF stimulation are reduced. Confocal measurements show reduced PF+CF-evoked spine calcium transients, a key trigger for PF long-term depression, one of several plasticity types required for eyeblink conditioning learning. Long-term depression is impaired in patDp/+ mice but is rescued on pharmacological enhancement of calcium signaling.

Conclusions

Our findings suggest that this genetic abnormality causes a pathological inflation of CF signaling, possibly resulting from enhanced NRXN1 expression, with consequences for the representation of sensory stimuli by the CF input and for PF synaptic organization and plasticity.

Cerebellum Involvement in Dystonia During Associative Motor Learning: Insights From a Data-Driven Spiking Network Model

Dystonia is a movement disorder characterized by sustained or intermittent muscle contractions causing abnormal, often repetitive movements, postures, or both. Although dystonia is traditionally associated with basal ganglia dysfunction, recent evidence has been pointing to a role of the cerebellum, a brain area involved in motor control and learning. Cerebellar abnormalities have been correlated with dystonia but their potential causative role remains elusive. Here, we simulated the cerebellar input-output relationship with high-resolution computational modeling. We used a data-driven cerebellar Spiking Neural Network and simulated a cerebellum-driven associative learning task, Eye-Blink Classical Conditioning (EBCC), which is characteristically altered in relation to cerebellar lesions in several pathologies. In control simulations, input stimuli entrained characteristic network dynamics and induced synaptic plasticity along task repetitions, causing a progressive spike suppression in Purkinje cells with consequent facilitation of deep cerebellar nuclei cells. These neuronal processes caused a progressive acquisition of eyelid Conditioned Responses (CRs). Then, we modified structural or functional local neural features in the network reproducing alterations reported in dystonic mice. Either reduced olivocerebellar input or aberrant Purkinje cell burst-firing resulted in abnormal learning curves imitating the dysfunctional EBCC motor responses (in terms of CR amount and timing) of dystonic mice. These behavioral deficits might be due to altered temporal processing of sensorimotor information and uncoordinated control of muscle contractions. Conversely, an imbalance of excitatory and inhibitory synaptic densities on Purkinje cells did not reflect into significant EBCC deficit. The present work suggests that only certain types of alterations, including reduced olivocerebellar input and aberrant PC burst-firing, are compatible with the EBCC changes observed in dystonia, indicating that some cerebellar lesions can have a causative role in the pathogenesis of symptoms.

Brain-Inspired Spiking Neural Network Controller for a Neurorobotic Whisker System

It is common for animals to use self-generated movements to actively sense the surrounding environment. For instance, rodents rhythmically move their whiskers to explore the space close to their body. The mouse whisker system has become a standard model for studying active sensing and sensorimotor integration through feedback loops. In this work, we developed a bioinspired spiking neural network model of the sensorimotor peripheral whisker system, modeling trigeminal ganglion, trigeminal nuclei, facial nuclei, and central pattern generator neuronal populations. This network was embedded in a virtual mouse robot, exploiting the Human Brain Project's Neurorobotics Platform, a simulation platform offering a virtual environment to develop and test robots driven by brain-inspired controllers. Eventually, the peripheral whisker system was adequately connected to an adaptive cerebellar network controller. The whole system was able to drive active whisking with learning capability, matching neural correlates of behavior experimentally recorded in mice.

Subcortical Cognition: The Fruit Below the Rind

Cognitive neuroscience has highlighted the cerebral cortex while often overlooking subcortical structures. This cortical proclivity is found in basic and translational research on many aspects of cognition, especially higher cognitive domains such as language, reading, music, and math. We suggest that, for both anatomical and evolutionary reasons, multiple subcortical structures play substantial roles across higher and lower cognition. We present a comprehensive review of existing evidence, which indeed reveals extensive subcortical contributions in multiple cognitive domains. We argue that the findings are overall both real and important. Next, we advance a theoretical framework to capture the nature of (sub)cortical contributions to cognition. Finally, we propose how new subcortical cognitive roles can be identified by leveraging anatomical and evolutionary principles, and we describe specific methods that can be used to reveal subcortical cognition. Altogether, this review aims to advance cognitive neuroscience by highlighting subcortical cognition and facilitating its future investigation. Expected final online publication date for the Annual Review of Neuroscience, Volume 45 is July 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Exploring the Genomic Patterns in Human and Mouse Cerebellums Via Single-Cell Sequencing and Machine Learning Method

In mammals, the cerebellum plays an important role in movement control. Cellular research reveals that the cerebellum involves a variety of sub-cell types, including Golgi, granule, interneuron, and unipolar brush cells. The functional characteristics of cerebellar cells exhibit considerable differences among diverse mammalian species, reflecting a potential development and evolution of nervous system. In this study, we aimed to recognize the transcriptional differences between human and mouse cerebellum in four cerebellar sub-cell types by using single-cell sequencing data and machine learning methods. A total of 321,387 single-cell sequencing data were used. The 321,387 cells included 4 cell types, i.e., Golgi (5,048, 1.57%), granule (250,307, 77.88%), interneuron (60,526, 18.83%), and unipolar brush (5,506, 1.72%) cells. Our results showed that by using gene expression profiles as features, the optimal classification model could achieve very high even perfect performance for Golgi, granule, interneuron, and unipolar brush cells, respectively, suggesting a remarkable difference between the genomic profiles of human and mouse. Furthermore, a group of related genes and rules contributing to the classification was identified, which might provide helpful information for deepening the understanding of cerebellar cell heterogeneity and evolution.

Metastable dynamics of neural circuits and networks

Cortical neurons emit seemingly erratic trains of action potentials or "spikes," and neural network dynamics emerge from the coordinated spiking activity within neural circuits. These rich dynamics manifest themselves in a variety of patterns, which emerge spontaneously or in response to incoming activity produced by sensory inputs. In this Review, we focus on neural dynamics that is best understood as a sequence of repeated activations of a number of discrete hidden states. These transiently occupied states are termed "metastable" and have been linked to important sensory and cognitive functions. In the rodent gustatory cortex, for instance, metastable dynamics have been associated with stimulus coding, with states of expectation, and with decision making. In frontal, parietal, and motor areas of macaques, metastable activity has been related to behavioral performance, choice behavior, task difficulty, and attention. In this article, we review the experimental evidence for neural metastable dynamics together with theoretical approaches to the study of metastable activity in neural circuits. These approaches include (i) a theoretical framework based on non-equilibrium statistical physics for network dynamics; (ii) statistical approaches to extract information about metastable states from a variety of neural signals; and (iii) recent neural network approaches, informed by experimental results, to model the emergence of metastable dynamics. By discussing these topics, we aim to provide a cohesive view of how transitions between different states of activity may provide the neural underpinnings for essential functions such as perception, memory, expectation, or decision making, and more generally, how the study of metastable neural activity may advance our understanding of neural circuit function in health and disease.

Ultrastructural effects of sleep and wake on the parallel fiber synapses of the cerebellum

Multiple evidence in rodents shows that the strength of excitatory synapses in the cerebral cortex and hippocampus is greater after wake than after sleep. The widespread synaptic weakening afforded by sleep is believed to keep the cost of synaptic activity under control, promote memory consolidation, and prevent synaptic saturation, thus preserving the brain's ability to learn day after day. The cerebellum is highly plastic and the Purkinje cells, the sole output neurons of the cerebellar cortex, are endowed with a staggering number of excitatory parallel fiber synapses. However, whether these synapses are affected by sleep and wake is unknown. Here, we used serial block face scanning electron microscopy to obtain the full 3D reconstruction of more than 7000 spines and their parallel fiber synapses in the mouse posterior vermis. This analysis was done in mice whose cortical and hippocampal synapses were previously measured, revealing that average synaptic size was lower after sleep compared to wake with no major changes in synapse number. Here, instead, we find that while the average size of parallel fiber synapses does not change, the number of branched synapses is reduced in half after sleep compared to after wake, corresponding to ~16% of all spines after wake and ~8% after sleep. Branched synapses are harbored by two or more spines sharing the same neck and, as also shown here, are almost always contacted by different parallel fibers. These findings suggest that during wake, coincidences of firing over parallel fibers may translate into the formation of synapses converging on the same branched spine, which may be especially effective in driving Purkinje cells to fire. By contrast, sleep may promote the off-line pruning of branched synapses that were formed due to spurious coincidences.

Influence of various temporal recoding on pavlovian eyeblink conditioning in the cerebellum

We consider the Pavlovian eyeblink conditioning (EBC) via repeated presentation of paired conditioned stimulus (tone) and unconditioned stimulus (US; airpuff). In an effective cerebellar ring network, we change the connection probability p c from Golgi to granule (GR) cells, and make a dynamical classification of various firing patterns of the GR cells. Individual GR cells are thus found to show various well- and ill-matched firing patterns relative to the US timing signal. Then, these variously-recoded signals are fed into the Purkinje cells (PCs) through the parallel-fibers (PFs). Based on such unique dynamical classification of various firing patterns, we make intensive investigations on the influence of various temporal recoding (i.e., firing patterns) of the GR cells on the synaptic plasticity of the PF-PC synapses and the subsequent learning process for the EBC. We first note that the variously-recoded PF signals are effectively depressed by the (error-teaching) instructor climbing-fiber (CF) signals from the inferior olive neuron. In the case of well-matched PF signals, they are strongly depressed through strong long-term depression (LTD) by the instructor CF signals due to good association between the in-phase PF and the instructor CF signals. On the other hand, practically no LTD occurs for the ill-matched PF signals because most of them have no association with the instructor CF signals. This kind of "effective" depression at the PF-PC synapses coordinates firings of PCs effectively, which then makes effective inhibitory coordination on the cerebellar nucleus neuron [which elicits conditioned response (CR; eyeblink)]. When the learning trial passes a threshold, acquisition of CR begins. In this case, the timing degree T d of CR becomes good due to presence of the ill-matched firing group which plays a role of protection barrier for the timing. With further increase in the number of trials, strength S of CR (corresponding to the amplitude of eyelid closure) increases due to strong LTD in the well-matched firing group, while its timing degree T d decreases. In this way, the well- and the ill-matched firing groups play their own roles for the strength and the timing of CR, respectively. Thus, with increasing the number of learning trials, the (overall) learning efficiency degree L e (taking into consideration both timing and strength of CR) for the CR is increased, and eventually it becomes saturated. Finally, we also discuss dependence of the variety degree for firing patterns of the GR cells and the saturated learning efficiency degree L e of the CR on p c and their relations.

Deficiency of TRIM32 Impairs Motor Function and Purkinje Cells in Mid-Aged Mice

Proper functioning of the cerebellum is crucial to motor balance and coordination in adult mammals. Purkinje cells (PCs), the sole output neurons of the cerebellar cortex, play essential roles in cerebellar motor function. Tripartite motif-containing protein 32 (TRIM32) is an E3 ubiquitin ligase that is involved in balance activities of neurogenesis in the subventricular zone of the mammalian brain and in the development of many nervous system diseases, such as Alzheimer's disease, autism spectrum disorder, attention deficit hyperactivity disorder. However, the role of TRIM32 in cerebellar motor function has never been examined. In this study we found that motor balance and coordination of mid-aged TRIM32 deficient mice were poorer than those of wild-type littermates. Immunohistochemical staining was performed to assess cerebella morphology and TRIM32 expression in PCs. Golgi staining showed that the extent of dendritic arborization and dendritic spine density of PCs were decreased in the absence of TRIM32. The loss of TRIM32 was also associated with a decrease in the number of synapses between parallel fibers and PCs, and in synapses between climbing fibers and PCs. In addition, deficiency of TRIM32 decreased Type I inositol 1,4,5-trisphosphate 5-phosphatase (INPP5A) levels in cerebellum. Overall, this study is the first to elucidate a role of TRIM32 in cerebellar motor function and a possible mechanism, thereby highlighting the importance of TRIM32 in the cerebellum.

W246G Mutant ELOVL4 Impairs Synaptic Plasticity in Parallel and Climbing Fibers and Causes Motor Defects in a Rat Model of SCA34

Spinocerebellar ataxia (SCA) is a neurodegenerative disorder characterized by ataxia and cerebellar atrophy. A number of different mutations gives rise to different types of SCA with characteristic ages of onset, symptomatology, and rates of progression. SCA type 34 (SCA34) is caused by mutations in ELOVL4 (ELOngation of Very Long-chain fatty acids 4), a fatty acid elongase essential for biosynthesis of Very Long Chain Saturated and Polyunsaturated Fatty Acids (VLC-SFA and VLC-PUFA, resp., ≥28 carbons), which have important functions in the brain, skin, retina, Meibomian glands, testes, and sperm. We generated a rat model of SCA34 by knock-in of the SCA34-causing 736T>G (p.W246G) ELOVL4 mutation. Rats carrying the mutation developed impaired motor deficits by 2 months of age. To understand the mechanism of these motor deficits, we performed electrophysiological studies using cerebellar slices from rats homozygous for W246G mutant ELOVL4 and found marked reduction of long-term potentiation at parallel fiber synapses and long-term depression at climbing fiber synapses onto Purkinje cells. Neuroanatomical analysis of the cerebellum showed normal cytoarchitectural organization with no evidence of degeneration out to 6 months of age. These results point to ELOVL4 as essential for motor function and cerebellar synaptic plasticity. The results further suggest that ataxia in SCA34 patients may arise from a primary impairment of synaptic plasticity and cerebellar network desynchronization before onset of neurodegeneration and progression of the disease at a later age.

The Rules of Cerebellar Learning: Around the Ito Hypothesis

As a tribute to Masao Ito, we propose a model of cerebellar learning that incorporates and extends his original model. We suggest four principles that align well with conclusions from multiple cerebellar learning systems. (1) Climbing fiber inputs to the cerebellum drive early, fast, poorly-retained learning in the parallel fiber to Purkinje cell synapse. (2) Learned Purkinje cell outputs drive late, slow, well-retained learning in non-Purkinje cell inputs to neurons in the cerebellar nucleus, transferring learning from the cortex to the nucleus. (3) Recurrent feedback from Purkinje cells to the inferior olive, through interneurons in the cerebellar nucleus, limits the magnitude of fast, early learning in the cerebellar cortex. (4) Functionally different inputs are subjected to plasticity in the cerebellar cortex versus the cerebellar nucleus. A computational neural circuit model that is based on these principles mimics a large amount of neural and behavioral data obtained from the smooth pursuit eye movements of monkeys.

Calcium Channel-Dependent Induction of Long-Term Synaptic Plasticity at Excitatory Golgi Cell Synapses of Cerebellum

Golgi cells, together with granule cells and mossy fibers, form a neuronal microcircuit regulating information transfer at the cerebellum input stage. Despite theoretical predictions, little was known about long-term synaptic plasticity at Golgi cell synapses. Here, we have used whole-cell patch-clamp recordings and calcium imaging to investigate long-term synaptic plasticity at excitatory synapses impinging on Golgi cells. In acute mouse cerebellar slices, mossy fiber theta-burst stimulation (TBS) could induce either long-term potentiation (LTP) or long-term depression (LTD) at mossy fiber-Golgi cell and granule cell-Golgi cell synapses. This synaptic plasticity showed a peculiar voltage dependence, with LTD or LTP being favored when TBS induction occurred at depolarized or hyperpolarized potentials, respectively. LTP required, in addition to NMDA channels, activation of T-type Ca²⁺ channels, while LTD required uniquely activation of L-type Ca²⁺ channels. Notably, the voltage dependence of plasticity at the mossy fiber-Golgi cell synapses was inverted with respect to pure NMDA receptor-dependent plasticity at the neighboring mossy fiber-granule cell synapse, implying that the mossy fiber presynaptic terminal can activate different induction mechanisms depending on the target cell. In aggregate, this result shows that Golgi cells show cell-specific forms of long-term plasticity at their excitatory synapses, that could play a crucial role in sculpting the response patterns of the cerebellar granular layer.SIGNIFICANCE STATEMENT This article shows for the first time a novel form of Ca²⁺ channel-dependent synaptic plasticity at the excitatory synapses impinging on cerebellar Golgi cells. This plasticity is bidirectional and inverted with respect to NMDA receptor-dependent paradigms, with long-term depression (LTD) and long-term potentiation (LTP) being favored at depolarized and hyperpolarized potentials, respectively. Furthermore, LTP and LTD induction requires differential involvement of T-type and L-type voltage-gated Ca²⁺ channels rather than the NMDA receptors alone. These results, along with recent computational predictions, support the idea that Golgi cell plasticity could play a crucial role in controlling information flow through the granular layer along with cerebellar learning and memory.

Accounting for uncertainty: inhibition for neural inference in the cerebellum

Sensorimotor coordination is thought to rely on cerebellar-based internal models for state estimation, but the underlying neural mechanisms and specific contribution of the cerebellar components is unknown. A central aspect of any inferential process is the representation of uncertainty or conversely precision characterizing the ensuing estimates. Here, we discuss the possible contribution of inhibition to the encoding of precision of neural representations in the granular layer of the cerebellar cortex. Within this layer, Golgi cells influence excitatory granule cells, and their action is critical in shaping information transmission downstream to Purkinje cells. In this review, we equate the ensuing excitation-inhibition balance in the granular layer with the outcome of a precision-weighted inferential process, and highlight the physiological characteristics of Golgi cell inhibition that are consistent with such computations.

Intrinsic plasticity and birdsong learning

Although information processing and storage in the brain is thought to be primarily orchestrated by synaptic plasticity, other neural mechanisms such as intrinsic plasticity are available. While a number of recent studies have described the plasticity of intrinsic excitability in several types of neurons, the significance of non-synaptic mechanisms in memory and learning remains elusive. After reviewing plasticity of intrinsic excitation in relation to learning and homeostatic mechanisms, we focus on the intrinsic properties of a class of basal-ganglia projecting song system neurons in zebra finch, how these related to each bird's unique learned song, how these properties change over development, and how they are maintained dynamically to rapidly change in response to auditory feedback perturbations. We place these results in the broader theme of learning and changes in intrinsic properties, emphasizing the computational implications of this form of plasticity, which are distinct from synaptic plasticity. The results suggest that exploring reciprocal interactions between intrinsic and network properties will be a fruitful avenue for understanding mechanisms of birdsong learning.

How and Why the Cerebellum Recodes Input Signals: An Alternative to Machine Learning

Mossy fiber input to the cerebellum is received by granule cells where it is thought to be recoded into internal signals received by Purkinje cells, which alone carry the output of the cerebellar cortex. In any neural network, variables are contained in groups of signals as well as signals themselves—which cells are active and how many, for example, and statistical variables coded in rates, such as the mean and range, and which rates are strongly represented, in a defined population. We argue that the primary function of recoding is to confine translation to an effect of some variables and not others—both where input is recoded into internal signals and the translation downstream of internal signals into an effect on Purkinje cells. The cull of variables is harsh. Internal signaling is group coded. This allows coding to exploit statistics for a reliable and precise effect despite needing to work with high-dimensional input which is a highly unpredictably variable. An important effect is to normalize eclectic input signals, so that the basic, repeating cerebellar circuit, preserved across taxa, does not need to specialize (within regional variations). With this model, there is no need to slavishly conserve or compute data coded in single signals. If we are correct, a learning algorithm—for years, a mainstay of cerebellar modeling—would be redundant.

Diversity and dynamism in the cerebellum

The past several years have brought revelations and paradigm shifts in research on the cerebellum. Historically viewed as a simple sensorimotor controller with homogeneous architecture, the cerebellum is increasingly implicated in cognitive functions. It possesses an impressive diversity of molecular, cellular and circuit mechanisms, embedded in a dynamic, recurrent circuit architecture. Recent insights about the diversity and dynamism of the cerebellum provide a roadmap for the next decade of cerebellar research, challenging some old concepts, reinvigorating others and defining major new research directions.

Brain-inspired classical conditioning model

Classical conditioning plays a critical role in the learning process of biological brains, and many computational models have been built to reproduce the related classical experiments. However, these models can reproduce and explain only a limited range of typical phenomena in classical conditioning. Based on existing biological findings concerning classical conditioning, we build a brain-inspired classical conditioning (BICC) model. Compared with other computational models, our BICC model can reproduce as many as 15 classical experiments, explaining a broader set of findings than other models have, and offers better computational explainability for both the experimental phenomena and the biological mechanisms of classical conditioning. Finally, we validate our theoretical model on a humanoid robot in three classical conditioning experiments (acquisition, extinction, and reacquisition) and a speed generalization experiment, and the results show that our model is computationally feasible as a foundation for brain-inspired robot classical conditioning.

•

Classical conditioning (CC) is crucial in biological and embodied robot learning

•

A spiking neural network incorporates existing biological findings of CC in one model

•

BICC can explain a broader set of findings than other existing computational models

•

BICC ensures a robot gets similar biological behavior and speed generalization capability

Gating by Functionally Indivisible Cerebellar Circuits: a Hypothesis

The attempt to understand the cerebellum has been dominated for years by supervised learning models. The central idea is that a learning algorithm modifies transmission strength at repeatedly co-active synapses, creating memories stored as finely calibrated synaptic weights. As a result, Purkinje cells, usually the de facto output cells of these models, acquire a modified response to input in a remembered pattern. This paper proposes an alternative model of pattern memory in which the function of a match is permissive, allowing but not driving output, and accordingly controlling the timing of output but not the rate of firing by Purkinje cells. Learning does not result in graded synaptic weights. There is no supervised learning algorithm or memory of individual patterns, which, like graded weights, are unnecessary to explain the evidence. Instead, patterns are classed as simply either known or not, at the level of input to a functional population of 100s of Purkinje cells (a microzone). The standard is strict. If only a handful of Purkinje cells receive a mismatch output of the whole circuit is blocked. Only if there is a full and accurate match are projection neurons in deep nuclei, which carry the output of most circuits, released from default inhibitory restraint. Purkinje cell firing at those times is a linear function of input rates. There is no effect of modification of synaptic transmission except to either allow or block output.

NMDA receptor-independent LTP in mammalian nervous system

Long-term potentiation (LTP) of synaptic transmission is a form of activity-dependent synaptic plasticity that exists at most synapses in the nervous system. In the central nervous system (CNS), LTP has been recorded at numerous synapses and is a prime candidate mechanism associating activity-dependent plasticity with learning and memory. LTP involves long-lasting increase in synaptic strength with various underlying mechanisms. In the CNS, the predominant type of LTP is believed to be dependent on activation of the ionotropic glutamate N-methyl-D-aspartate receptor (NMDAR), which is highly calcium-permeable. However, various forms of NMDAR-independent LTP have been identified in diverse areas of the nervous system. The NMDAR-independent LTP may require activation of glutamate metabotropic receptors (mGluR) or ionotropic receptors other than NMDAR such as nicotinic acetylcholine receptor (α7-nAChR), serotonin 5-HT3 receptor or calcium-permeable AMPA receptor (CP-AMPAR). In this review, NMDAR-independent LTP of various areas of the central and peripheral nervous systems are discussed.

Human stem cell models of polyglutamine diseases: Sources for disease models and cell therapy

Polyglutamine (polyQ) diseases are a group of neurodegenerative disorders involving expanded CAG repeats in pathogenic genes that are translated into extended polyQ tracts and lead to progressive neuronal degeneration in the affected brain. To date, there is no effective therapy for these diseases. Due to the complex pathologic mechanisms of these diseases, intensive research on the pathogenesis of their progression and potential treatment strategies is being conducted. However, animal models cannot recapitulate all aspects of neuronal degeneration. Pluripotent stem cells (PSCs), such as induced pluripotent stem cells (iPSCs) and embryonic stem cells (ESCs), can be used to study the pathological mechanisms of polyQ diseases, and the ability of autologous stem cell transplantation to treat these diseases. Differentiated PSCs, neuronal precursor cells/neural progenitor cells (NPCs) and mesenchymal stem cells (MSCs) are valuable resources for preclinical and clinical cell transplantation therapies. Here, we discuss diverse stem cell models and their ability to generate neurons involved in polyQ diseases, such as medium spiny neurons (MSNs), cortical neurons, cerebellar Purkinje cells (PCs) and motor neurons. In addition, we discuss potential therapeutic approaches, including stem cell replacement therapy and gene therapy.

Effect of diverse recoding of granule cells on optokinetic response in a cerebellar ring network with synaptic plasticity

We consider a cerebellar ring network for the optokinetic response (OKR), and investigate the effect of diverse recoding of granule (GR) cells on OKR by varying the connection probability p_c from Golgi to GR cells. For an optimal value of p_c^∗(=0.06), individual GR cells exhibit diverse spiking patterns which are in-phase, anti-phase, or complex out-of-phase with respect to their population-averaged firing activity. Then, these diversely-recoded signals via parallel fibers (PFs) from GR cells are effectively depressed by the error-teaching signals via climbing fibers from the inferior olive which are also in-phase ones. Synaptic weights at in-phase PF-Purkinje cell (PC) synapses of active GR cells are strongly depressed via strong long-term depression (LTD), while those at anti-phase and complex out-of-phase PF-PC synapses are weakly depressed through weak LTD. This kind of "effective" depression (i.e., strong/weak LTD) at the PF-PC synapses causes a big modulation in firings of PCs, which then exert effective inhibitory coordination on the vestibular nucleus (VN) neuron (which evokes OKR). For the firing of the VN neuron, the learning gain degree L_g, corresponding to the modulation gain ratio, increases with increasing the learning cycle, and it saturates at about the 300th cycle. By varying p_c from p_c^∗, we find that a plot of saturated learning gain degree L_g^∗ versus p_c forms a bell-shaped curve with a peak at p_c^∗ (where the diversity degree in spiking patterns of GR cells is also maximum). Consequently, the more diverse in recoding of GR cells, the more effective in motor learning for the OKR adaptation.

Cerebellar developmental deficits underlie neurodegenerative disorder spinocerebellar ataxia type 23

Spinocerebellar ataxia type 23 (SCA23) is a late‐onset neurodegenerative disorder characterized by slowly progressive gait and limb ataxia, for which there is no therapy available. It is caused by pathogenic variants in PDYN, which encodes prodynorphin (PDYN). PDYN is processed into the opioid peptides α‐neoendorphin and dynorphins (Dyn) A and B; inhibitory neurotransmitters that function in pain signaling, stress‐induced responses and addiction. Variants causing SCA23 mostly affect Dyn A, leading to loss of secondary structure and increased peptide stability. PDYN^R212W mice express human PDYN containing the SCA23 variant p.R212W. These mice show progressive motor deficits from 3 months of age, climbing fiber (CF) deficits from 3 months of age, and Purkinje cell (PC) loss from 12 months of age. A mouse model for SCA1 showed similar CF deficits, and a recent study found additional developmental abnormalities, namely increased GABAergic interneuron connectivity and non‐cell autonomous disruption of PC function. As SCA23 mice show a similar pathology to SCA1 mice in adulthood, we hypothesized that SCA23 may also follow SCA1 pathology during development. Examining PDYN^R212W cerebella during development, we uncovered developmental deficits from 2 weeks of age, namely a reduced number of GABAergic synapses on PC soma, possibly leading to the observed delay in early phase CF elimination between 2 and 3 weeks of age. Furthermore, CFs did not reach terminal height, leaving proximal PC dendrites open to be occupied by parallel fibers (PFs). The observed increase in vGlut1 protein—a marker for PF‐PC synapses—indicates that PFs indeed take over CF territory and have increased connectivity with PCs. Additionally, we detected altered expression of several critical Ca²⁺ channel subunits, potentially contributing to altered Ca²⁺ transients in PDYN^R212W cerebella. These findings indicate that developmental abnormalities contribute to the SCA23 pathology and uncover a developmental role for PDYN in the cerebellum.

Rapid Estrogenic and Androgenic Neurosteroids Effects in the Induction of Long-Term Synaptic Changes: Implication for Early Memory Formation

Mounting experimental evidence demonstrate that sex neuroactive steroids (neurosteroids) are essential for memory formation. Neurosteroids have a profound impact on the function and structure of neural circuits and their local synthesis is necessary for the induction of both long-term potentiation (LTP) and long-term depression (LTD) of synaptic transmission and for neural spine formation in different areas of the central nervous system (CNS). Several studies demonstrated that in the hippocampus, 17β-estradiol (E2) is necessary for inducing LTP, while 5α-dihydrotestosterone (DHT) is necessary for inducing LTD. This contribution has been proven by administering sex neurosteroids in rodent models and by using blocking agents of their synthesis or of their specific receptors. The general opposite role of sex neurosteroids in synaptic plasticity appears to be dependent on their different local availability in response to low or high frequency of synaptic stimulation, allowing the induction of bidirectional synaptic plasticity. The relevant contribution of these neurosteroids to synaptic plasticity has also been described in other brain regions involved in memory processes such as motor learning, as in the case of the vestibular nuclei, the cerebellum, and the basal ganglia, or as the emotional circuit of the amygdala. The rapid effects of sex neurosteroids on neural synaptic plasticity need the maintenance of a tonic or phasic local steroid synthesis determined by neural activity but might also be influenced by circulating hormones, age, and gender. To disclose the exact mechanisms how sex neurosteroids participate in finely tuning long-term synaptic changes and spine remodeling, further investigation is required.

Cannabinoids modulate associative cerebellar learning via alterations in behavioral state

Cannabinoids are notorious and profound modulators of behavioral state. In the brain, endocannabinoids act via Type 1-cannabinoid receptors (CB1) to modulate synaptic transmission and mediate multiple forms of synaptic plasticity. CB1 knockout (CB1KO) mice display a range of behavioral phenotypes, in particular hypoactivity and various deficits in learning and memory, including cerebellum-dependent delay eyeblink conditioning. Here we find that the apparent effects of CB1 deletion on cerebellar learning are not due to direct effects on CB1-dependent plasticity, but rather, arise as a secondary consequence of altered behavioral state. Hypoactivity of CB1KO mice accounts for their impaired eyeblink conditioning across both animals and trials. Moreover, learning in these mutants is rescued by walking on a motorized treadmill during training. Finally, cerebellar granule-cell-specific CB1KOs exhibit normal eyeblink conditioning, and both global and granule-cell-specific CB1KOs display normal cerebellum-dependent locomotor coordination and learning. These findings highlight the modulation of behavioral state as a powerful independent means through which individual genes contribute to complex behaviors.

Microglia-Triggered Plasticity of Intrinsic Excitability Modulates Psychomotor Behaviors in Acute Cerebellar Inflammation

Cerebellar dysfunction relates to various psychiatric disorders, including autism spectrum and depressive disorders. However, the physiological aspect is less advanced. Here, we investigate the immune-triggered hyperexcitability in the cerebellum on a wider scope. Activated microglia via exposure to bacterial endotoxin lipopolysaccharide or heat-killed Gram-negative bacteria induce a potentiation of the intrinsic excitability in Purkinje neurons, which is suppressed by microglia-activity inhibitor and microglia depletion. An inflammatory cytokine, tumor necrosis factor alpha (TNF-α), released from microglia via toll-like receptor 4, triggers this plasticity. Our two-photon FRET ATP imaging shows an increase in ATP concentration following endotoxin exposure. Both TNF-α and ATP secretion facilitate synaptic transmission. Region-specific inflammation in the cerebellum in vivo shows depression- and autistic-like behaviors. Furthermore, both TNF-α inhibition and microglia depletion revert such behavioral abnormality. Resting-state functional MRI reveals overconnectivity between the inflamed cerebellum and the prefrontal neocortical regions. Thus, immune activity in the cerebellum induces neuronal hyperexcitability and disruption of psychomotor behaviors in animals.

Neuronal spike-rate adaptation supports working memory in language processing

Language processing involves the ability to store and integrate pieces of information in working memory over short periods of time. According to the dominant view, information is maintained through sustained, elevated neural activity. Other work has argued that short-term synaptic facilitation can serve as a substrate of memory. Here we propose an account where memory is supported by intrinsic plasticity that downregulates neuronal firing rates. Single neuron responses are dependent on experience, and we show through simulations that these adaptive changes in excitability provide memory on timescales ranging from milliseconds to seconds. On this account, spiking activity writes information into coupled dynamic variables that control adaptation and move at slower timescales than the membrane potential. From these variables, information is continuously read back into the active membrane state for processing. This neuronal memory mechanism does not rely on persistent activity, excitatory feedback, or synaptic plasticity for storage. Instead, information is maintained in adaptive conductances that reduce firing rates and can be accessed directly without cued retrieval. Memory span is systematically related to both the time constant of adaptation and baseline levels of neuronal excitability. Interference effects within memory arise when adaptation is long lasting. We demonstrate that this mechanism is sensitive to context and serial order which makes it suitable for temporal integration in sequence processing within the language domain. We also show that it enables the binding of linguistic features over time within dynamic memory registers. This work provides a step toward a computational neurobiology of language.