Determinants of synaptic integration and heterogeneity in rebound firing explored with data-driven models of deep cerebellar nucleus cells

Significant inroads have been made to understand cerebellar cortical processing but neural coding at the output stage of the cerebellum in the deep cerebellar nuclei (DCN) remains poorly understood. The DCN are unlikely to just present a relay nucleus because Purkinje cell inhibition has to be turned into an excitatory output signal, and DCN neurons exhibit complex intrinsic properties. In particular, DCN neurons exhibit a range of rebound spiking properties following hyperpolarizing current injection, raising the question how this could contribute to signal processing in behaving animals. Computer modeling presents an ideal tool to investigate how intrinsic voltage-gated conductances in DCN neurons could generate the heterogeneous firing behavior observed, and what input conditions could result in rebound responses. To enable such an investigation we built a compartmental DCN neuron model with a full dendritic morphology and appropriate active conductances. We generated a good match of our simulations with DCN current clamp data we recorded in acute slices, including the heterogeneity in the rebound responses. We then examined how inhibitory and excitatory synaptic input interacted with these intrinsic conductances to control DCN firing. We found that the output spiking of the model reflected the ongoing balance of excitatory and inhibitory input rates and that changing the level of inhibition performed an additive operation. Rebound firing following strong Purkinje cell input bursts was also possible, but only if the chloride reversal potential was more negative than −70 mV to allow de-inactivation of rebound currents. Fast rebound bursts due to T-type calcium current and slow rebounds due to persistent sodium current could be differentially regulated by synaptic input, and the pattern of these rebounds was further influenced by HCN current. Our findings suggest that active properties of DCN neurons could play a crucial role for signal processing in the cerebellum

The Effect of Input from the Cerebellar Nuclei on Activity in Thalamocortical Networks

The cerebellum is a prominent brain structure that contains more than half of all neurons, in the brain, which are densely packed and make up 15% of the total brain mass (Andersen et al., 1992). It is well known for its contribution to the control of motor functions, but it also plays a pivotal role in non-motor behaviours. The cerebellum is also involved in numerous pathological conditions. This thesis contributes to the understanding of the pathophysiology of the cerebello-thalamo-cortical pathways. I concentrate on two cerebellar diseases, namely: absence epilepsy (Noebels, 2005) and downbeat nystagmus (DBN) (Strupp et al., 2007). In this thesis the missing link in explaining the alleviating mechanism of a potassium channel blocker on downbeat nystagmus was found. A simulated single biologically detailed floccular target neuron (FTN) model was stimulated by input from cerebellar Purkinje cells (PCs). It was demonstrated that for both synchronised and unsynchronised input, irregular PC spike trains (which resembles the DBN condition) resulted in elevated FTN firing rates, in comparison with regular (4-AP treated) ones. This increase or decrease of the FTN firing rates during DBN, or after 4-AP treatment, respectively depended on short term depression (STD) at the PC - FTN synapses exclusively in the cases when the PC input was unsynchronised. In contrast, results of previous modelling studies (Glasauer et al, 2011; Glasauer and Rossert, 2008) were not in-line with the corresponding experimental findings (Alvina and Khodakhah, 2010) because they did not take into account the STD on the FTN-PC synapses. It was also demonstrated here that the cerebellar output contributes to the control of absence epilepsy that originates in the thalamocortical network. Moreover, the cerebellar input was most effective when it arrived at the peak of the GSWD burst, with the least effective input arriving during the inter-ictal interval, showing clear phase-dependency. I have also shown that a three-fold increase in the inhibitory time constant, drives the asynchronous-irregular network into an ictal state. This increase reflects the GABAA block. A change to GABAB dominated inhibition results in GSWDs, in which the “wave” component is related to the slow GABAB-mediated K+ currents (Destexhe, 1998). Therefore, in this thesis two important contributions are made to the understanding of cerebellar pathological states: absence epilepsy and DBN, which might in turn be useful in the potential treatment of these conditions.

Modeling the Cerebellar Microcircuit: New Strategies for a Long-Standing Issue

The cerebellar microcircuit has been the work bench for theoretical and computational modeling since the beginning of neuroscientific research. The regular neural architecture of the cerebellum inspired different solutions to the long-standing issue of how its circuitry could control motor learning and coordination. Originally, the cerebellar network was modeled using a statistical-topological approach that was later extended by considering the geometrical organization of local microcircuits. However, with the advancement in anatomical and physiological investigations, new discoveries have revealed an unexpected richness of connections, neuronal dynamics and plasticity, calling for a change in modeling strategies, so as to include the multitude of elementary aspects of the network into an integrated and easily updatable computational framework. Recently, biophysically accurate realistic models using a bottom-up strategy accounted for both detailed connectivity and neuronal non-linear membrane dynamics. In this perspective review, we will consider the state of the art and discuss how these initial efforts could be further improved. Moreover, we will consider how embodied neurorobotic models including spiking cerebellar networks could help explaining the role and interplay of distributed forms of plasticity. We envisage that realistic modeling, combined with closed-loop simulations, will help to capture the essence of cerebellar computations and could eventually be applied to neurological diseases and neurorobotic control systems

Excitation and Excitability of Unipolar Brush Cells

__Abstract__ The cerebellum is a distinct brain structure that ensures the spatial accuracy and temporal coordination of movements. It is located superimposed on the brainstem and has an appearance and organization unlike that of the cerebral cortex: its surface has a highly regular foliation pattern, and its neural circuitry is organized in repeated structured modules. Neural activity enters the cerebellum via two excitatory pathways, the mossy ber system and the climbing ber system. Climbing bers originate from the inferior olivary nucleus in the brainstem, and assert a powerful in uence on cerebellar output and long-term adaptation processes. Mossy bers originate from a large number of sources, and carry contextual information on sensory inputs, aspects of motor planning and commands, and proprioceptive feedback. In the cerebellum this information is evaluated and integrated, to produce neural output that in uences ongoing movement directly. Mossy ber signals are processed in the cerebellum in three stages. In the granular layer, the input stage of the cerebellum, mossy ber signals undergo a recoding step where they are combined and expanded by granule cells. Next, in the molecular layer, granule cell signals are integrated with climbing ber signals in Purkinje cells. Together, the granular la

Computational Properties of Cerebellar Nucleus Neurons: Effects of Stochastic Ion Channel Gating and Input Location

The function of the nervous system is shaped by the refined integration of synaptic inputs taking place at the single neuron level. Gain modulation is a computational principle that is widely used across the brain, in which the response of a neuronal unit to a set of inputs is affected in a multiplicative fashion by a second set of inputs, but without any effect on its selectivity. The arithmetic operations performed by pyramidal cells in cortical brain areas have been well characterised, along with the underlying mechanisms at the level of networks and cells, for instance background synaptic noise and dendritic saturation. However, in spite of the vast amount of research on the cerebellum and its function, little is known about neuronal computations carried out by its cellular components. A particular area of interest are the cerebellar nuclei, the main output gate of the cerebellum to the brain stem and cortical areas. The aim of this thesis is to contribute to an understanding of the arithmetic operations performed by neurons in the cerebellar nuclei. Focus is placed on two putative determinants, the location of the synaptic input and the presence of channel noise. To analyse the effect of channel noise, the known voltage-gated ion channels of a cerebellar nucleus neuron model are translated to stochastic Markov formalisms and their electrophysiologial behaviour is compared to their deterministic Hodgkin-Huxley counterparts. The findings demonstrate that in most cases, the behaviour of stochastic channels matches the reference deterministic models, with the notable exception of voltage-gated channels with fast kinetics. Two potential explanations are suggested for this discrepancy. Firstly, channels with fast kinetics are strongly affected by the artefactual loss of gating events in the simulation that is caused by the use of a finite-length time step. While this effect can be mitigated, in part, by using very small time steps, the second source of simulation artefacts is the rectification of the distribution of open channels, when channel kinetics characteristics allow the generation of a window current, with an temporal-averaged equilibrium close to zero. Further, stochastic gating is implemented in a realistic cerebellar nucleus neuronal model. The resulting stochastic model exhibits probabilistic spiking and a similar output rate as the corresponding deterministic cerebellar nucleus neuronal model. However, the outcomes of this thesis indicate the computational properties of the cerebellar nucleus neuronal model are independent of the presence of ion channel noise. The main result of this thesis is that the synaptic input location determines the single neuron computational properties, both in the cerebellar nucleus and layer Vb pyramidal neuronal models. The extent of multiplication increases systematically with the distance from the soma, for the cerebellar nucleus, but not for the layer Vb pyramidal neuron, where it is smaller than it would be expected for the distance from the soma. For both neurons, the underlying mechanism is related to the combined effect of nonlinearities introduced by dendritic saturation and the synaptic input noise. However, while excitatory inputs in the perisomatic areas in the cerebellar nucleus undergo additive operations and the distal areas multiplicative, in the layer Vb pyramidal neuron the integration of the excitatory driving input is always multiplicative. In addition, the change in gain is sensitive to the synchronicity of the excitatory synaptic input in the layer Vb pyramidal neuron, but not in the cerebellar nucleus neuron. These observations indicate that the same gain control mechanism might be utilized in distinct ways, in different computational contexts and across different areas, based on the neuronal type and its function

Neurones glycinergiques et transmission inhibitrice dans les noyaux cérébelleux

The cerebellum is composed of a three-layered cortex and of nuclei and is responsible for the learned fine control of posture and movements. I combined a genetic approach (based on the use of transgenic mouse lines) with anatomical tracings, immunohistochemical stainings, electrophysiological recordings and optogenetic stimulations to establish the distinctive characteristics of the inhibitory neurons of the cerebellar nuclei and to detail their connectivity and their role in the cerebellar circuitry.We showed that the glycinergic inhibitory neurons of the cerebellar nuclei constitute a distinct neuronal population and are characterized by their mixed inhibitory GABAergic/glycinergic phenotype. Those inhibitory neurons are also distinguished by their axonal plexus which includes a local arborization with the cerebellar nuclei where they contact principal output neurons and a projection to the granular layer of the cerebellar cortex where they end onto Golgi cells dendrites. Finally, the inhibitory neurons of the cerebellar nuclei receive inhibitory afferents from Purkinje cells and may be contacted by mossy fibers or climbing fibers.We provided the first evidence of functional mixed transmission in the cerebellar nuclei and the first demonstration of a mixed inhibitory nucleo-cortical projection. Overall, our data establish the inhibitory neurons as the third cellular component of the cerebellar nuclei. Their importance in the modular organization of the cerebellum and their impact on sensory-motor integration need to be confirmed by optogenetic experiments in vivo.Le cervelet, composé d'un cortex et de noyaux, est responsable du contrôle moteur fin des mouvements et de la posture. En combinant une approche génétique (basée sur l'utilisation de lignées de souris transgéniques) avec des traçages anatomiques, des marquages immunohistochimiques et des expériences d'électrophysiologie et d'optogénétique, nous établissons les caractères distinctifs des neurones inhibiteurs des noyaux cérébelleux et en détaillons la connectivité ainsi que les fonctions dans le circuit cérébelleux. Les neurones inhibiteurs glycinergiques des noyaux profonds constituent une population de neurones distincts des autres types cellulaires identifiables par leur phénotype inhibiteur mixte GABAergique/glycinergique. Ces neurones se distinguent également par leur plexus axonal qui comporte une arborisation locale dans les noyaux cérébelleux où ils contactent les neurones principaux et une projection vers le cortex cérébelleux où ils contactent les cellules de Golgi. Ces neurones inhibiteurs reçoivent également des afférences inhibitrices des cellules de Purkinje et pourraient être contactés par les fibres moussues ou les fibres grimpantes.Nous apportons ainsi la première étude d'une transmission mixte fonctionnelle par les neurones inhibiteurs des noyaux cérébelleux, projetant à la fois dans les noyaux et le cortex cérébelleux. L'ensemble de nos données établissent les neurones inhibiteurs mixtes des noyaux cérébelleux comme la troisième composante cellulaire des noyaux profonds. Leur importance dans l'organisation modulaire du cervelet, ainsi que leur impact sur l'intégration sensori-motrice, devront être confirmés par des études optogénétiques in vivo