An agonist–antagonist cerebellar nuclear system controlling eyelid kinematics during motor learning

The presence of two antagonistic groups of deep cerebellar nuclei neurons has been reported as necessary for a proper dynamic control of learned motor responses. Most models of cerebellar function seem to ignore the biomechanical need for a double activation–deactivation system controlling eyelid kinematics, since most of them accept that, for closing the eyelid, only the activation of the orbicularis oculi (OO) muscle (via the red nucleus to the facial motor nucleus) is necessary, without a simultaneous deactivation of levator palpebrae motoneurons (via unknown pathways projecting to the perioculomotor area). We have analyzed the kinetic neural commands of two antagonistic types of cerebellar posterior interpositus neuron (IPn) (types A and B), the electromyographic (EMG) activity of the OO muscle, and eyelid kinematic variables in alert behaving cats during classical eyeblink conditioning, using a delay paradigm. We addressed the hypothesis that the interpositus nucleus can be considered an agonist–antagonist system controlling eyelid kinematics during motor learning. To carry out a comparative study of the kinetic–kinematic relationships, we applied timing and dispersion pattern analyses. We concluded that, in accordance with a dominant role of cerebellar circuits for the facilitation of flexor responses, type A neurons fire during active eyelid downward displacements—i.e., during the active contraction of the OO muscle. In contrast, type B neurons present a high tonic rate when the eyelids are wide open, and stop firing during any active downward displacement of the upper eyelid. From a functional point of view, it could be suggested that type B neurons play a facilitative role for the antagonistic action of the levator palpebrae muscle. From an anatomical point of view, the possibility that cerebellar nuclear type B neurons project to the perioculomotor area—i.e., more or less directly onto levator palpebrae motoneurons—is highly appealing

Eyeblink Conditioning in Schizophrenia: A Critical Review

There is accruing evidence of cerebellar abnormalities in schizophrenia. The theory of cognitive dysmetria considers cerebellar dysfunction a key component of schizophrenia. Delay eyeblink conditioning (EBC), a cerebellar-dependent translational probe, is a behavioral index of cerebellar integrity. The circuitry underlying EBC has been well characterized by non-human animal research, revealing the cerebellum as the essential circuitry for the associative learning instantiated by this task. However, there have been persistent inconsistencies in EBC findings in schizophrenia. This article thoroughly reviews published studies investigating EBC in schizophrenia, with an emphasis on possible effects of antipsychotic medication and stimulus and analysis parameters on reports of EBC performance in schizophrenia. Results indicate a consistent finding of impaired EBC performance in schizophrenia, as measured by decreased rates of conditioning, and that medication or study design confounds do not account for this impairment. Results are discussed within the context of theoretical and neurochemical models of schizophrenia

Trial-by-Trial Coding of Instructive Signals in the Cerebellum: Insights From Eyeblink Conditioning in Mice

The cerebellum is an area of the brain that plays a crucial role in the learning of motor skills. This process involves climbing fibers, which provide teaching signals to Purkinje cells in the cerebellar cortex when perturbations occur during a movement. However, controversy has arisen over climbing fibers contribution to cerebellar learning. This is because climbing-fiber signals are described as all-or-nothing : they fire a single burst of action potentials in response to all supra-threshold stimuli, regardless of their strength. On the contrary, motor learning is not all-or-nothing: the amount of learning is driven by the strength of perturbations. In this dissertation, I describe the experiments that I performed to unravel how climbing fibers may encode the strength of teaching signals. In Chapter 2, I present my behavioral studies in mice, which involved a simple cerebellar-dependent motor learning task, eyeblink conditioning. I show that mice take into account the strength of unexpected perturbations to adapt their movements trial-by-trial. In Chapter 3, I present a review of the previous literature and provide a hypothesis on how climbing fibers can encode the strength of teaching signals in a single trial. In Chapter 4, I present the findings of my in vivo two-photon calcium imaging experiments, which suggest climbing-fiber signals may not be all-or-nothing at the post-synaptic level. Finally, in Chapter 5 I describe the different mechanisms that we discovered for coding the intensity of teaching signals by Purkinje cells in the cerebellum of awake mice

Cerebellar mechanisms underlying adaptive motor responses

In order to understand the function of any brain structure, one must know what input/output transformation it performs. The term input/output transformation includes at least two stages. First, we must understand how inputs are processed. Second, we must know what the output activity encodes. Certain properties of the cerebellum make such an undertaking feasible. In this thesis I present the results of three main projects designed to study the input/output transformations of this major brain system from different angles. In the first project I investigated the relationship between spiking activity of cerebellar cortex principal neurons - Purkinje cells (PCs) - and eyelid conditioned response (CRs) profiles on a single trial basis. Systematically exploring a variety of encoding possibilities, I found that PCs do not directly encode a single kinematic variable of a CR. The best prediction was rather achieved via a dynamical model approach, where PCs provide a ‘drive’ to the eyelid plant, the dynamics of which are described by a differential equation. In the second project I addressed how the cerebellum deals with inherent uncertainty about the nature of sensory inputs. I found that under conditions of uncertainty, the cerebellum performed a probabilistic binary choice, scaling the probability of response with the similarity between current and trained stimuli. Importantly, if responses were made, their amplitude was close to the previously trained value, maintaining the adaptive nature of responses. Recordings from eyelid Purkinje cells localized this computation to cerebellar cortex. Results from large-scale computer simulation suggest that the efference copy signal is critical for the expression of target response amplitude. In the third project I studied cerebellar mechanisms of learning and expression of movement sequences. While the majority of movements we perform are composed of sequences, most of the knowledge about cerebellar learning and computation comes from tasks involving single, unitary movements. Hence, I designed a novel sequence training protocol to explicitly test the ability of the cerebellum to chain together a series of movements through associative learning processes. The results demonstrate a simple yet general framework for how the cerebellum can learn to produce a movement sequence.Neuroscienc

A Multiple-Plasticity Spiking Neural Network Embedded in a Closed-Loop Control System to Model Cerebellar Pathologies

The cerebellum plays a crucial role in sensorimotor control and cerebellar disorders compromise adaptation and learning of motor responses. However, the link between alterations at network level and cerebellar dysfunction is still unclear. In principle, this understanding would benefit of the development of an artificial system embedding the salient neuronal and plastic properties of the cerebellum and operating in closed-loop. To this aim, we have exploited a realistic spiking computational model of the cerebellum to analyze the network correlates of cerebellar impairment. The model was modified to reproduce three different damages of the cerebellar cortex: (i) a loss of the main output neurons (Purkinje Cells), (ii) a lesion to the main cerebellar afferents (Mossy Fibers), and (iii) a damage to a major mechanism of synaptic plasticity (Long Term Depression). The modified network models were challenged with an Eye-Blink Classical Conditioning test, a standard learning paradigm used to evaluate cerebellar impairment, in which the outcome was compared to reference results obtained in human or animal experiments. In all cases, the model reproduced the partial and delayed conditioning typical of the pathologies, indicating that an intact cerebellar cortex functionality is required to accelerate learning by transferring acquired information to the cerebellar nuclei. Interestingly, depending on the type of lesion, the redistribution of synaptic plasticity and response timing varied greatly generating specific adaptation patterns. Thus, not only the present work extends the generalization capabilities of the cerebellar spiking model to pathological cases, but also predicts how changes at the neuronal level are distributed across the network, making it usable to infer cerebellar circuit alterations occurring in cerebellar pathologies

Synaptic mechanisms for associative learning in the cerebellar nuclei

Associative learning during delay eyeblink conditioning (EBC) depends on an intact cerebellum. However, the relative contribution of changes in the cerebellar nuclei to learning remains a subject of ongoing debate. In particular, little is known about the changes in synaptic inputs to cerebellar nuclei neurons that take place during EBC and how they shape the membrane potential of these neurons. Here, we probed the ability of these inputs to support associative learning in mice, and investigated structural and cell-physiological changes within the cerebellar nuclei during learning. We find that optogenetic stimulation of mossy fiber afferents to the anterior interposed nucleus (AIP) can substitute for a conditioned stimulus and is sufficient to elicit conditioned responses (CRs) that are adaptively well-timed. Further, EBC induces structural changes in mossy fiber and inhibitory inputs, but not in climbing fiber inputs, and it leads to changes in subthreshold processing of AIP neurons that correlate with conditioned eyelid movements. The changes in synaptic and spiking activity that precede the CRs allow for a decoder to distinguish trials with a CR. Our data reveal how structural and physiological modifications of synaptic inputs to cerebellar nuclei neurons can facilitate learning.</p