Neuroscience
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Mouse models of Autism Spectrum Disorder (ASD) have been interrogated using a variety of behavioral tests in order to understand the symptoms of ASD. However, the hallmark behaviors that are classically affected in ASD - deficits in social interaction and communication as well as the occurrence of repetitive behaviors - do not have direct murine equivalents. Thus, it is critical to identify the caveats that come with modeling a human disorder in mice. ⋯ LAY Mouse models of Autism Spectrum Disorder help us gain insight about ASD symptoms in human patients. However, there are many differences between mice and humans, which makes interpreting behaviors challenging. Here, we discuss a battery of behavioral tests for specific mouse behaviors to explore whether each test does indeed evaluate the intended measure, and whether these tests are useful in learning about ASD.
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The cerebellum forms regular neural network structures consisting of a few major types of neurons, such as Purkinje cells, granule cells, and molecular layer interneurons, and receives two major inputs from climbing fibers and mossy fibers. Its regular structures consist of three well-defined layers, with each type of neuron designated to a specific location and forming specific synaptic connections. During the first few weeks of postnatal development in rodents, the cerebellum goes through dynamic changes via proliferation, migration, differentiation, synaptogenesis, and maturation, to create such a network structure. ⋯ Therefore, it is reasonable that extracellular signaling via synaptic transmission, secreted molecules, and cell adhesion molecules, plays important roles in cerebellar network development. Although it is not yet clear as to how overall cerebellar development is orchestrated, there is indeed accumulating lines of evidence that extracellular signaling acts toward the development of individual elements in the cerebellar networks. In this article, we introduce what we have learned from many studies regarding the extracellular signaling required for cerebellar network development, including our recent study suggesting the importance of unbiased synaptic inputs from parallel fibers.
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Cerebellar development has a remarkably protracted morphogenetic timeline that is coordinated by multiple cell types. Here, we discuss the intriguing cellular consequences of interactions between inhibitory Purkinje cells and excitatory granule cells during embryonic and postnatal development. Purkinje cells are central to all cerebellar circuits, they are the first cerebellar cortical neurons to be born, and based on their cellular and molecular signaling, they are considered the master regulators of cerebellar development. ⋯ They provide a combination of mechanical, molecular and activity-based cues that shape the maturation of Purkinje cell structure, connectivity and function. We propose that the wiring of Purkinje cells for function falls into two developmental phases: an initial phase that is guided by intrinsic mechanisms and a later phase that is guided by dynamically-acting cues, some of which are provided by granule cells. In this review, we highlight the mechanisms that granule cells use to help establish the unique properties of Purkinje cell firing.
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This article is dedicated to the memory of Masao Ito. Masao Ito made numerous important contributions revealing the function of the cerebellum in motor control. His pioneering contributions to cerebellar physiology began with his discovery of inhibition and disinhibition of target neurons by cerebellar Purkinje cells, and his discovery of the presence of long-term depression in parallel fiber-Purkinje cell synapses. ⋯ These discoveries became the basis for his ideas regarding the flocculus hypothesis, the adaptive motor control system, and motor learning by the cerebellum, inspiring many new experiments to test his hypotheses. This article will trace the achievements of Ito and colleagues in analyzing the neural circuits of the input-output organization of the cerebellar cortex and nuclei, particularly with respect to motor control. The article will discuss some of the important issues that have been solved and also those that remain to be solved for our understanding of motor control by the cerebellum.
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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.