Dans de nombreuses espèces doiseaux chanteurs (ou Passériformes), dont fait partie le canari domestique (Serinus canaria), le comportement de chant est produit à la fois pour défendre un territoire ou attirer un partenaire. Le Système de Conntrôle du Chant est un réseau nerveux central spécialisé, principalement localisé au niveau du télencéphale et associé au contrôle de lapprentissage, la perception et la production du chant. Ce comportement a été décrit subir la modulation de facteurs externes, tels que la testostérone, la photopériode et les interactions sociales. En parallèle avec le comportement de chant, certains des noyaux appartenant au Système de Contrôle du Chant (HVC, RA et Area X) présentent un phénomène de plasticité saisonnière nerveuse fascinante. Le volume de ces noyaux augmente notamment par espacement des cellules, agrandissement de la taille du neuropile et de larborisation dendritique et, dans le cas particulier dHVC, par incorporation de neurones nouveaux-nés. Nous proposons ici une synthèse de la littérature concernant ce phénomène tout à fait particulier ; en effet, la régénération des neurones du système nerveux central est considérée comme inexistante -ou uniquement limitée à la production de quelques interneurones- chez les mammifères. Létude de la neuroplasticité chez loiseau chanteur constitue dès lors un modèle tout à fait remarquable et offrant des perspectives nouvelles dans létude du cerveau des vertébrés.

This thesis consists of four chapters. In this thesis we explored associative plasticity of human motor cortex with the use of noninvasive transcranial magnetic stimulation (TMS). Paired Associative Stimulation (PAS) has grown in popularity because of its potential clinical applications. We used TMS techniques in combination with electromyographic (EMG) measurements to study cortical excitability and kinematic features of arm movement. This work has focused in a cohesive approach to answer certain fundamental questions about a) the rules of cortical plasticity and mechanism of PAS, b) the interaction between the state of neuronal excitability at the targeted cortical network and the effects of PAS, and c) translation of these effects into obvious measurable kinematic changes starting from network level changes and ending up with the behavioral modulation of arm movement. First we explored the role of GABAergic intracortical networks and intracortical facilitation on modulation of cortical excitability by showing for the first time that PAS can be conditioned by these inhibitory and facilitatory intracortical networks. Next, using standard indirect approaches utilizing peripheral EMG measures, we showed a graded excitability response for the PAS technique and showed that interactions of PAS with motor learning depends on the degree as well as the state of cortical excitability. Rules governing the interactions of brain stimulation techniques and motor learning are important because brain stimulation techniques can be used to modify, improve or disrupt motor adaptation and skill learning with great potential for clinical applications such as facilitation of recovery after stroke. TMS provide us with a unique opportunity to study the rules of plasticity at a systems level, which is a combination of synaptic and nonsynaptic (metaplastic) changes. These changes can occur either in the direction to limit the physiological range of neuronal functioning (homeostatic) or against the direction established state of neurons.

Recreational drug use peaks in the developmental stage of adolescence in humans. In this dissertation, we used a rodent model of adolescence and behavioral assessments of intravenous (i.v.) cocaine self-administration and reinstatement of cocaine-seeking to explore age differences in these cocaine-related behaviors, and then tested for the influence of environmental enrichment and for correlations between behavior and expression of plasticity genes. Although taking similar amount of cocaine, male rats trained to self-administer cocaine during adolescence (adolescent-onset) showed attenuated cue-induced reinstatement of cocaine seeking compared with adults. This attenuated cue-induced reinstatement did not generalize to a natural reward, sucrose pellets. Then we asked whether the attenuated reinstatement may be due to rapid developmental re-organization of reinforcement circuits (high plasticity) in adolescent-onset groups. To stimulate or inhibit neuroplasticity, subjects experienced environmental enrichment or impoverishment during abstinence. Environmental manipulations had no effect in adolescent-onset groups, whereas the enriched environment attenuated cue-induced reinstatement in adults compared with their impoverished counterparts. Thus, we turned to internal factors that may contribute to age-differences in reinstatement of cocaine seeking. Using in situ hybridization to quantify the mRNA for two neuroplasticity-related genes, activity-regulated cytoskeletal-associated gene (arc) and brain-derived neurotrophic factor (bdnf), we identified that overall, arc expression in the nucleus accumbens (NAc) and bdnf expression in the medial prefrontal cortex (mPFC) was higher in adolescent-onset than in adult groups. Together our data suggest that adolescence in rodents may be a period of relative biological resistance to some long-term drug effects.

Learning is central to the exploration of intelligence. Psychology and machine learning provide high-level explanations of how rational agents learn. Neuroscience provides low-level descriptions of how the brain changes as a result of learning. This thesis attempts to bridge the gap between these two levels of description by solving problems using machine learning ideas, implemented in biologically plausible spiking neural networks with experimentally supported learning rules. We present three novel neural models that contribute to the understanding of how the brain might solve the three main problems posed by machine learning: supervised learning, in which the rational agent has a fine-grained feedback signal, reinforcement learning, in which the agent gets sparse feedback, and unsupervised learning, in which the agents has no explicit environmental feedback. In supervised learning, we argue that previous models of supervised learning in spiking neural networks solve a problem that is less general than the supervised learning problem posed by machine learning. We use an existing learning rule to solve the general supervised learning problem with a spiking neural network. We show that the learning rule can be mapped onto the well-known backpropagation rule used in artificial neural networks. In reinforcement learning, we augment an existing model of the basal ganglia to implement a simple actor-critic model that has a direct mapping to brain areas. The model is used to recreate behavioural and neural results from an experimental study of rats performing a simple reinforcement learning task. In unsupervised learning, we show that the BCM rule, a common learning rule used in unsupervised learning with rate-based neurons, can be adapted to a spiking neural network. We recreate the effects of STDP, a learning rule with strict time dependencies, using BCM, which does not explicitly remember the times of previous spikes. The simulations suggest that BCM is a more general rule than STDP. Finally, we propose a novel learning rule that can be used in all three of these simulations. The existence of such a rule suggests that the three types of learning examined separately in machine learning may not be implemented with separate processes in the brain.

Thesis (M.S.)--West Virginia University, 2001. / Title from document title page. Document formatted into pages; contains vii, 79 p. : ill. (some col.). Vita. Includes abstract. Includes bibliographical references (p. 70-78).

published_or_final_version / Anatomy / Doctoral / Doctor of Philosophy

C-type Natriuretic Peptide (CNP), a relatively new member of the natriuretic peptide family, is found throughout the central nervous system. Circumstantial evidence associates CNP with learning and memory, as its expression is highest in brain regions known to be involved in memory and associated with hippocampal physiology. Here, the first study housed rats in an enriched environment, regarded as providing an 'informal' learning experience, for either 14 or 28 days of housing in enrichment in six regions of interest, which was attributed to changes in the degradation of CNP. The second study examined a group of rats trained on object -recognition task – the bow-tie maze. A difference was found in CNP production in the limbic medial prefrontal cortex over repeated exposures to novel objects relative to controls that received 'yoked learning' an exposure only to the test room. CNP concentrations also tended to be lower in rats with better levels of discrimination between familiar objects. Together, these studies provide some initial evidence that CNP influences learning –induced plasticity in the intact brain.

This thesis consists of four chapters. In this thesis we explored associative plasticity of human motor cortex with the use of noninvasive transcranial magnetic stimulation (TMS). Paired Associative Stimulation (PAS) has grown in popularity because of its potential clinical applications. We used TMS techniques in combination with electromyographic (EMG) measurements to study cortical excitability and kinematic features of arm movement. This work has focused in a cohesive approach to answer certain fundamental questions about a) the rules of cortical plasticity and mechanism of PAS, b) the interaction between the state of neuronal excitability at the targeted cortical network and the effects of PAS, and c) translation of these effects into obvious measurable kinematic changes starting from network level changes and ending up with the behavioral modulation of arm movement. First we explored the role of GABAergic intracortical networks and intracortical facilitation on modulation of cortical excitability by showing for the first time that PAS can be conditioned by these inhibitory and facilitatory intracortical networks. Next, using standard indirect approaches utilizing peripheral EMG measures, we showed a graded excitability response for the PAS technique and showed that interactions of PAS with motor learning depends on the degree as well as the state of cortical excitability. Rules governing the interactions of brain stimulation techniques and motor learning are important because brain stimulation techniques can be used to modify, improve or disrupt motor adaptation and skill learning with great potential for clinical applications such as facilitation of recovery after stroke. TMS provide us with a unique opportunity to study the rules of plasticity at a systems level, which is a combination of synaptic and nonsynaptic (metaplastic) changes. These changes can occur either in the direction to limit the physiological range of neuronal functioning (homeostatic) or against the direction established state of neurons.

Learning is central to the exploration of intelligence. Psychology and machine learning provide high-level explanations of how rational agents learn. Neuroscience provides low-level descriptions of how the brain changes as a result of learning. This thesis attempts to bridge the gap between these two levels of description by solving problems using machine learning ideas, implemented in biologically plausible spiking neural networks with experimentally supported learning rules. We present three novel neural models that contribute to the understanding of how the brain might solve the three main problems posed by machine learning: supervised learning, in which the rational agent has a fine-grained feedback signal, reinforcement learning, in which the agent gets sparse feedback, and unsupervised learning, in which the agents has no explicit environmental feedback. In supervised learning, we argue that previous models of supervised learning in spiking neural networks solve a problem that is less general than the supervised learning problem posed by machine learning. We use an existing learning rule to solve the general supervised learning problem with a spiking neural network. We show that the learning rule can be mapped onto the well-known backpropagation rule used in artificial neural networks. In reinforcement learning, we augment an existing model of the basal ganglia to implement a simple actor-critic model that has a direct mapping to brain areas. The model is used to recreate behavioural and neural results from an experimental study of rats performing a simple reinforcement learning task. In unsupervised learning, we show that the BCM rule, a common learning rule used in unsupervised learning with rate-based neurons, can be adapted to a spiking neural network. We recreate the effects of STDP, a learning rule with strict time dependencies, using BCM, which does not explicitly remember the times of previous spikes. The simulations suggest that BCM is a more general rule than STDP. Finally, we propose a novel learning rule that can be used in all three of these simulations. The existence of such a rule suggests that the three types of learning examined separately in machine learning may not be implemented with separate processes in the brain.

The human primary motor cortex (M1) undergoes structural and functional change throughout life by a process known as neuroplasticity. Techniques which artificially induce neuroplastic changes are seen as potential adjunct therapies for neurological conditions reliant on neuroplasticity for recovery of function. Unfortunately, the reported improvements in function when these techniques have been used in combination with regular rehabilitation have so far been inconsistent. One reason attributed to this is the large variability in effectiveness of these techniques in inducing neuroplastic change. This thesis has investigated factors influencing the effectiveness and reproducibility of neuroplasticity induction in human M1 using several experimental paradigms. The effectiveness and reproducibility of inducing neuroplasticity in human M1 using two variants of a paired associative stimulation (PAS) protocol was investigated in the first set of experiments (Chapter 2). Both protocols repeatedly paired a peripheral electrical stimulus to the median nerve of the left wrist with single-pulse transcranial magnetic stimulation (TMS) delivered 25 ms later to the contralateral M1. Neuroplastic changes were quantified by comparing the amplitude of the muscle evoked potential (MEP) recorded in abductor pollicis brevis (APB) muscle by suprathreshold TMS prior to and following PAS. With both protocols, neuroplasticity induction was more effective, and the responses across sessions more reproducible, if the experiments were performed in the afternoon compared to the morning. Subsequent experiments confirmed the time of day modulation of PAS-induced neuroplasticity by repeatedly testing twenty-five subjects on two separate occasions, once in the morning (8 am), and once in the evening (8 pm) (Chapter 3). Time of day was also shown to modulate GABAergic inhibition in M1. In a further set of experiments, a double-blind, placebo-controlled study demonstrated that artificially elevated circulating cortisol levels (with a single oral dose of hydrocortisone) inhibits PAS-induced neuroplasticity in the evening (8 pm), indicating that the time of day modulation of neuroplasticity induction with PAS is due, at least in part, to differences in circulating cortisol levels (Chapter 3). The cortical circuits that are modulated by PAS have also been shown to be important in motor learning. Therefore, the final set of experiments, described in Chapter 4, investigated whether motor-training-related changes in motor performance (and cortical excitability) following a ballistic motor training task are also modulated by time of day. Twenty-two subjects repeatedly abducted their left thumb with maximal acceleration for thirty minutes during two experimental sessions (morning (8 am) and evening (8 pm)) on separate occasions. Motor training improved motor performance, and increased cortical excitability, however these changes were independent of time of day. It may be that the motor training task and/or outcome measures used were not sufficiently sensitive to detect a subtle time of day effect of motor training on motor performance. Alternatively, the normally functioning motor system may be able to compensate for changes in cortical excitability to maintain optimal motor performance. These findings have important implications for therapies reliant on neuroplasticity for recovery of function, and indicate that rehabilitation may be most effective when circulating cortisol levels are low. / Thesis (Ph.D.) - University of Adelaide, School of Molecular and Biomedical Science, 2009