Cerebellar Conditioning and Learning

1. Auflage 2014
ISBN: 978-0-12-800791-4
Verlag: Elsevier Science & Techn.
Format: EPUB
Kopierschutz: 6 - ePub Watermark

E-Book, Englisch, Band 114, 126 Seiten

Reihe: International Review of Neurobiology

This volume of International Review of Neurobiology is on Cerebellar Conditioning and Learning. It reviews current knowledge and understanding, provides a starting point for researchers and practitioners entering the field.

Autoren/Hrsg.

Inhaltsverzeichnis

1;Front Cover;1
2;Cerebellar Conditioning and Learning;4
3;Copyright;5
4;Contents;6
5;Contributors;8
6;Chapter One: Learning-Induced Structural Plasticity in the Cerebellum;10
6.1;1. Introduction;11
6.2;2. Learning-Induced Structural Plasticity at Parallel Fiber-Purkinje Cell Synapses;12
6.2.1;2.1. Acrobatic motor skill learning;13
6.2.2;2.2. Eyelid conditioning;16
6.2.3;2.3. Acrobatic motor skill learning versus eyelid conditioning and functional plasticity versus structural plasticity;17
6.3;3. Structural Plasticity Other than Parallel Fiber-Purkinje Cell Synapses;19
6.3.1;3.1. Mossy fiber-Golgi cell synapses;19
6.3.2;3.2. Mossy fiber-deep cerebellar nuclei;20
6.3.3;3.3. Purkinje fiber, Mossy fiber, and olivary inputs to the deep cerebellar nuclei;21
6.4;4. Conclusion;22
6.5;Acknowledgments;23
6.6;References;23
7;Chapter Two: Cerebellar Mechanisms of Learning and Plasticity Revealed by Delay Eyelid Conditioning;30
7.1;1. Introduction;30
7.2;2. Eyelid Conditioning as a Tool to Study the Cerebellum;32
7.3;3. Sites and Rules for Learning-Related Plasticity;34
7.4;4. The Role of Timing in Cerebellar Learning;35
7.5;5. Feedback Control of Climbing Fiber Equilibrium Activity;38
7.6;6. The Role of Feedback Inhibition of Climbing Fibers in Response Timing;40
7.7;7. Implications for Cerebellar Learning from the Phenomenon of Savings;41
7.8;8. Summary;43
7.9;References;44
8;Chapter Three: Cerebellar Long-Term Potentiation: Cellular Mechanisms and Role in Learning;48
8.1;1. The Search for the Memory Engram in Cerebellar Learning;48
8.2;2. LTP: Cellular Mechanisms;49
8.3;3. Conflicting Data on the Role of Calcineurin in LTD Induction;54
8.4;4. LTP and Cerebellar Learning;55
8.5;Acknowledgments;57
8.6;References;57
9;Chapter Four: The Ontogeny of Associative Cerebellar Learning;62
9.1;1. Introduction;63
9.2;2. Behavioral Ontogeny of Eyeblink Conditioning;63
9.3;3. Neural Mechanisms Underlying Eyeblink Conditioning;65
9.3.1;3.1. CS pathway;68
9.3.2;3.2. US pathway;68
9.4;4. Neural Mechanisms Underlying the Ontogeny of Eyeblink Conditioning;69
9.4.1;4.1. Development of the CS pathway;69
9.4.2;4.2. Development of the US pathway;72
9.4.3;4.3. Development of forebrain modulation of cerebellar learning;73
9.5;5. Conclusions;74
9.6;References;76
10;Index;82
11;Contents of Recent Volumes;84

Leseproben

Chapter One Learning-Induced Structural Plasticity in the Cerebellum
Hiroshi Nishiyama1    Center for Learning and Memory, Department of Neuroscience, University of Texas at Austin, Austin, Texas, USA
1 Corresponding author: email address: [e-mail] Abstract
Activity-dependent changes in synaptic properties are considered key neural mechanisms of learning and memory. Most studies focus on changes in synaptic function such as long-term potentiation (LTP) and long-term depression (LTD), while changes in synaptic structure have been largely ignored. However, structural synaptic changes are also important. In fact, LTP and LTD are often associated with structural alterations of dendritic spines. Furthermore, experimental evidence indicates that behavioral learning often induces structural rewiring of synaptic circuitry through the formation of new synapses and elimination of existing ones. To discuss the role of structural plasticity in cerebellar learning, this chapter mainly focuses on parallel fiber–Purkinje cell synapses in the cerebellar cortex and reviews their structural changes under several different forms of motor learning. Keywords Structural plasticity Cerebellum Parallel fibers Purkinje cells Rewiring Motor learning Live imaging Electron microscopy 1 Introduction
Can experience change brain structures? Fifty years ago, Bennett, Diamond, Krech, and Rosenzweig (1964) addressed this century-old question with carefully designed animal experiments. They housed littermates of rats (same sex and age) in two different conditions, enriched environment versus restricted environment, and found a highly consistent and reproducible increase in the cortical weight in the enriched environment group (Bennett et al., 1964). This finding, along with other observations, allowed them to speculate that synaptic connections were substantially increased by experience. After this initial discovery, a number of studies convincingly showed that various aspects of experience and biological rhythms can alter synaptic connectivity through structural rearrangement of neurons in the adult brain (Black, Isaacs, Anderson, Alcantara, & Greenough, 1990; Chang & Greenough, 1982; Geinisman, Berry, Disterhoft, Power, & Van der Zee, 2001; Greenough, Larson, & Withers, 1985; Jones, Klintsova, Kilman, Sirevaag, & Greenough, 1997; Knott, Quairiaux, Genoud, & Welker, 2002; Popov & Bocharova, 1992; Popov, Bocharova, & Bragin, 1992; Woolley, Gould, Frankfurt, & McEwen, 1990). Despite these findings, the roles of neuronal structural plasticity in learning and memory have been largely unclear. First, behavioral treatments used in earlier studies involved a large amount of nonlearning components, such as difference in social interaction, substantial repetition of movements, and change in overall locomotion activity (Bennett et al., 1964; Chang & Greenough, 1982; Greenough et al., 1985). Second, in most previous studies, a between-subject comparison was used to infer what occurred in the brain during given behavioral tasks. In other words, the observed differences between control and experimental groups did not directly represent how the same neurons sequentially changed their structure over time. Because of these shortcomings, our understandings of neuronal structural plasticity in learning advanced, until recently, at a relatively slow pace. Meanwhile, the discovery of hippocampal long-term potentiation (LTP) in 1973 opened the era of functional plasticity research (Bliss & Lomo, 1973). The roles of LTP and long-term depression (LTD) in learning and memory have been intensively studied ever since. In most of those studies, long-term functional plasticity of synapses is the central theme, whereas structural plasticity received less attention. This situation has changed during the last decade. Owing largely to advances in high-resolution live imaging and three-dimensional reconstruction of serial electron micrographs, it has been clearly shown that LTP and LTD are accompanied by structural plasticity of dendritic spines in the hippocampus (Engert & Bonhoeffer, 1999; Matsuzaki, Honkura, Ellis-Davies, & Kasai, 2004; Nagerl, Eberhorn, Cambridge, & Bonhoeffer, 2004; Okamoto, Nagai, Miyawaki, & Hayashi, 2004; Ostroff, Fiala, Allwardt, & Harris, 2002; Zhou, Homma, & Poo, 2004). Furthermore, the advent of two-photon in vivo time-lapse microscopy enabled imaging of the same neurons repeatedly over a long period of time in the brains of live animals (Grutzendler, Kasthuri, & Gan, 2002; Lendvai, Stern, Chen, & Svoboda, 2000; Trachtenberg et al., 2002). Such in vivo time-lapse imaging experiments have revealed that (i) a small fraction of presynaptic axonal boutons, dendritic spines, and even dendritic arbors remain dynamic in adulthood even without any sensory or behavioral manipulation (De Paola et al., 2006; Grutzendler et al., 2002; Holtmaat et al., 2005; Lee et al., 2006; Lendvai et al., 2000; Majewska, Newton, & Sur, 2006; Nishiyama, Fukaya, Watanabe, & Linden, 2007; Stettler, Yamahachi, Li, Denk, & Gilbert, 2006; Trachtenberg et al., 2002; Zuo, Lin, Chang, & Gan, 2005) and (ii) novel sensory experience and motor learning promote the formation and elimination of dendritic spines in the adult neocortex (Holtmaat, Wilbrecht, Knott, Welker, & Svoboda, 2006; Xu et al., 2009; Yang, Pan, & Gan, 2009; Zuo, Yang, Kwon, & Gan, 2005). All these technical advances provide us with tools to examine the roles of structural plasticity in learning and memory with unprecedented levels of details. Cellular mechanisms of learning and memory have been extensively studied in the cerebellum. Different cerebellar neurons show a wide variety of functional plasticities, including LTP and LTD, and changes in intrinsic excitability (Carey, 2011; Gao, van Beugen, & De Zeeuw, 2012; Hansel, Linden, & D'Angelo, 2001). However, the prominence of functional plasticity research appears to overshadow the contribution of structural plasticity to learning in the cerebellum. This chapter therefore focuses on structural plasticity and discusses its potential roles in learning. 2 Learning-Induced Structural Plasticity at Parallel Fiber–Purkinje Cell Synapses
Purkinje cells are inhibitory projection neurons in the cerebellar cortex. They provide the sole cortical output to the deep cerebellar nuclei that send motor commands to various motor centers located outside of the cerebellum. Therefore, the activity of Purkinje cells is considered the final outcome of neural computation in the cerebellar cortex, which eventually influences motor behavior of animals (Eccles, Ito, & Szentagothai, 1967). Each Purkinje cell receives excitatory synaptic inputs from two different sources, climbing fibers and parallel fibers, and the parallel fiber–Purkinje cell synapses show postsynaptically expressed, bidirectional (i.e., potentiation and depression), long-term synaptic plasticity (Jorntell & Hansel, 2006). LTD is induced when parallel fibers and climbing fibers are concurrently activated (Ito & Kano, 1982; Ito, Sakurai, & Tongroach, 1982), whereas LTP is induced when only parallel fibers are activated (Coesmans, Weber, De Zeeuw, & Hansel, 2004; Lev-Ram, Wong, Storm, & Tsien, 2002). Since the roles of LTD and LTP (mostly LTD) in motor learning are intensively investigated (Ito, 2001; Mauk, Garcia, Medina, & Steele, 1998), it is reasonable to ask whether (i) this synapse also shows structural plasticity and, if so, (ii) if it is associated with learning. 2.1 Acrobatic motor skill learning
As described above, a conventional experimental approach for studying experience-dependent structural plasticity relies upon analysis of fixed tissue, which necessitates between-animal comparisons. Since analysis of this type is not sensitive enough to detect subtle changes, behavioral tasks were often designed such that massive and widespread structural changes were expected. In such behavioral tasks, dissociating learning and nonlearning components of behavior are not always straightforward. But in 1990, Greenough and coworkers addressed this problem by devising an acrobatic motor skill learning task (Black et al., 1990). In this task, animals are required to sequentially traverse various elevated obstacles leading to their home cage. During the training over several weeks, animals show substantial improvement in their performance (measured by the latency to traverse individual obstacles), indicating an improvement in their sensory–motor integration and whole-body and limb coordination. Importantly, this acrobatic motor skill learning does not substantially increase overall motor activity. Furthermore, several different control tasks were carefully designed to dissociate motor learning from the increase in overall motor activity. Their data showed that acrobatic motor skill learning induced synaptogenesis in the cerebellar cortex; the number of synapses per Purkinje...