Thesis (doctoral)--University of Tartu, 2006. / Thesis is based on 3 papers.

No description available.

No description available.

The deep cerebellar nuclei (DCN) function as output gates for a large majority of the Purkinje cells of the cerebellar cortex and thereby determine how the cerebellum influences the rest of the brain and body. In my PhD programme I have investigated how the DCN process two kinds of input patterns received from Purkinje cells: irregularity of spike intervals and pauses in Purkinje cell activity resulting from the recognition of patterns received at the synapses with the upstream parallel fibres (PFs). To that objective I have created a network system of biophysically realistic Purkinje cell and DCN neuron models that enables the exploration of a wide range of network structure and cell physiology parameters. With this system I have performed simulations that show how the DCN neuron changes the information modality of its input, consisting of varying regularity in Purkinje cell spike intervals, to varying spike rates in its output to the nervous system outside of the cerebellum. This was confirmed in simulations where I exchanged the artificial Purkinje cell trains for those received from experimental collaborators. In pattern recognition simulations I have found that the morphological arrangement present in the cerebellum, where multiple Purkinje cells connect to each DCN neuron, has the effect of amplifying pattern recognition already performed in the Purkinje cells. Using the metric of signal-to-noise ratio I show that PF patterns previously encountered and stored in PF - Purkinje cell synapses are most clearly distinguished from those novel to the system by a 10-20 ms shortened burst firing of the DCN neuron. This result suggests that the effect on downstream targets of these excitatory projection neurons is a decreased excitation when a stored as opposed to novel pattern is received. My work has contributed to a better understanding of information processing in the cerebellum, with implications for human motor control as well as the increasingly recognised non-motor functions of the cerebellum.

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. / 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.

Absence epilepsy is a neurological disorder that commonly occurs in children. Some studies have shown that absence seizures predominantly originate either in the thalamus or the cerebral cortex. Some cerebellar nuclei (CN) neurons project to these brain areas, as explained further in Fig. 2.6 in Chapter 2. Also, some CN neurons have been observed to show modulation during the absence seizures. This indicates that they somehow participate in the seizure and hence are referred to as "participating neurons" in this thesis. In this research, I demonstrate how machine learning techniques and computer simulations can be applied to investigate the properties and the input conditions present in these participating neurons. My investigation found a sub-group of CN neurons, with similar interictal spiking activity, spiking activity between the seizures, that are most likely to participate in seizures. To investigate the input conditions present in the CN neurons that produce this type of interictal activity, I used a morphologically realistic conductance based model of an excitatory CN projection neuron [66] and optimised the input parameters to this model using an Evolutionary Algorithm (EA). The results of the EA revealed that these participating CN neurons receive a synchronous and bursting input from Purkinje cells and bursting input with long intervals(approx. 500ms) from mossy fibre. The same interictal activity can also be produced when the Purkinje cell input to the CN neuron is asynchronous. The excitatory input in this case also had long interburst intervals but there is a decrease in excitatory and inhibitory synaptic weight. Surprisingly, a slight change in these input parameters can change the interictal spiking pattern to an ictal spiking pattern, the spiking pattern observed during absence seizures. I also discovered that it is possible to prevent a participating CN neuron from taking part in the seizures by blocking the Purkinje cell input.

Computational methods in neuroscience have advanced our understanding of neuronal regulation of motor behavior and locomotion and have been applied to identify encoding of behavioral features in circuits. The cerebellum has an established role in sensorimotor processing during coordinated movements, referred to as the “head ganglion of the proprioceptive system” (Sherrington, 1906). Increasing evidence also highlights its role in the processing of behaviorally meaningful stimuli that have the potential of guiding adaptative movements relevant to the task and priming downstream targets for action. Yet the extent to which these diverse encodings of signals in complex motor tasks are present in the cerebellar nuclei and their influence on behavior remains unknown. To shed new light on the role of this subcortical region using computational approaches, this thesis begins with an introduction that reviews the circuity of the mammalian cerebellum, highlights its proposed functions in motor behavior, and explores our understanding of its role in locomotion. In the first chapter, I analyze electrophysiological recordings from cerebellar nuclei in a locomotor obstacle avoidance task in mice that involves a rich and diverse set of task relevant features. Given the complexity of and correlations between the behavioral features, statistical modeling is required to attribute the firing rates to the correct combinations. This model enables identifying the encoding of these signals and reporting on the prevalence and degree to which they are present across individual cells in the nuclei. Additionally, this model allows investigation into the encoding of groups of cells that are selective for specific features. Chapter 2 uses network modeling to generate hypotheses about population level activity in two cortical areas, the primary and supplementary motor areas, and differentiate their computations in monkeys performing a cycling task. Finally, in chapter 3 I concentrate on a specific class of recurrent network models in the balanced state and investigate the linkage between connectivity distribution and firing sparsity, which has the potential to further our understanding on the emergence of feature selectivity in excitatory/inhibitory circuits.

Le cervelet permet l’apprentissage moteur et la coordination des mouvements fins. Pour ce faire, il intègre les informations sensorielles provenant de l’ensemble du corps ainsi que les commandes motrices émises par d’autres structures du système nerveux central. Les noyaux cérébelleux profonds (DCN) constituent la sortie du cervelet et intègre les informations provenant des cellules de Purkinje (PC), des fibres moussues et des fibres grimpantes. Nous avons étudié les connexions fonctionnelles entres les PC et les DNC in vivo, grâce à une stimulation optogénétique des lobules IV/V du cortex cérébelleux et à l’enregistrement multi unitaire du noyau médian. Nous avons ainsi identifié deux groupes de cellules au sein des DCN, présentant des caractéristiques propres au niveau de leur fréquence de décharge et de la forme des potentiels d’action, en accord avec la dichotomie établie par une précédente étude in vitro permettant de séparer les neurones GABAergiques des autres neurones. Nos résultats suggèrent que les PC contrôlent la sotie du cervelet d’un point de vue temporel. De plus, la ciruiterie interne des DCN conforte ce résultat de part le fait que les cellules GABAergiques ne produisent pas d’effet temporel au travers de l’inhibition locale. / The cerebellum integrates motor commands with somatosensory, vestibular, visual and auditory information for motor learning and coordination functions. The deep cerebellar nuclei (DCN) generates the final output by processing inputs from Purkinje cells (PC), mossy and climbing fibers. We investigated the properties of PC connections to DCN cells using optogenetic stimulation in L7-ChR2 mice with in vivo multi electrode extracellular recordings in lobule IV/V of the cerebellar cortex and in the medial nuclei. DCN cells discharged phase locked to local field potentials in the beta, gamma and high frequency bands. We identified two groups of DCN cells with significant differences in action potential waveforms and firing rates, matching previously discriminated in vitro properties of GABAergic and non-GABAergic cells. PCs inhibited the two group of cells gradually (rate coding), however spike times were controlled for only non-GABAergic cells. Our results suggest that PC inputs temporally control the output of cerebellum and the internal DCN circuitry supports this phenomenon since GABAergic cells do not induce a temporal effect through local inhibition.