Relevant bibliographies by topics / Basal ganglia model

Academic literature on the topic 'Basal ganglia model'

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Published: 10 March 2023

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Journal articles on the topic "Basal ganglia model"

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Barker, Roger. "Model for basal ganglia disorders." Trends in Neurosciences 13, no. 3 (March 1990): 93. http://dx.doi.org/10.1016/0166-2236(90)90181-9.

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Gonzalo, N. "The parafascicular thalamic complex and basal ganglia circuitry: further complexity to the basal ganglia model." Thalamus & Related Systems 1, no. 4 (June 2002): 341–48. http://dx.doi.org/10.1016/s1472-9288(02)00007-9.

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Gonzalo, N., J. L. Lanciego, M. Castle, A. Vázquez, E. Erro, and J. A. Obeso. "The parafascicular thalamic complex and basal ganglia circuitry: further complexity to the basal ganglia model." Thalamus and Related Systems 1, no. 04 (June 2002): 341. http://dx.doi.org/10.1017/s1472928802000079.

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Hallett, Mark. "Physiology of Basal Ganglia Disorders: An Overview." Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques 20, no. 3 (August 1993): 177–83. http://dx.doi.org/10.1017/s0317167100047909.

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ABSTRACT:The pathophysiology of the movement disorders arising from basal ganglia disorders has been uncertain, in part because of a lack of a good theory of how the basal ganglia contribute to normal voluntary movement. An hypothesis for basal ganglia function is proposed here based on recent advances in anatomy and physiology. Briefly, the model proposes that the purpose of the basal ganglia circuits is to select and inhibit specific motor synergies to carry out a desired action. The direct pathway is to select and the indirect pathway is to inhibit these synergies. The clinical and physiological features of Parkinson's disease, L-DOPA dyskinesias, Huntington's disease, dystonia and tic are reviewed. An explanation of these features is put forward based upon the model.
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Yin, Henry H. "How Basal Ganglia Outputs Generate Behavior." Advances in Neuroscience 2014 (November 18, 2014): 1–28. http://dx.doi.org/10.1155/2014/768313.

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The basal ganglia (BG) are a collection of subcortical nuclei critical for voluntary behavior. According to the standard model, the output projections from the BG tonically inhibit downstream motor centers and prevent behavior. A pause in the BG output opens the gate for behavior, allowing the initiation of actions. Hypokinetic neurological symptoms, such as inability to initiate actions in Parkinson’s disease, are explained by excessively high firing rates of the BG output neurons. This model, widely taught in textbooks, is contradicted by recent electrophysiological results, which are reviewed here. In addition, I also introduce a new model, based on the insight that behavior is a product of closed loop negative feedback control using internal reference signals rather than sensorimotor transformations. The nervous system is shown to be a functional hierarchy comprising independent controllers occupying different levels, each level controlling specific variables derived from its perceptual inputs. The BG represent the level of transition control in this hierarchy, sending reference signals specifying the succession of body orientations and configurations. This new model not only explains the major symptoms in movement disorders but also generates a number of testable predictions.
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Féger, J. "Updating the functional model of the basal ganglia." Trends in Neurosciences 20, no. 4 (May 13, 1997): 152–53. http://dx.doi.org/10.1016/s0166-2236(96)01016-8.

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Suri, R. E., C. Albani, and A. H. Glattfelder. "A dynamic model of motor basal ganglia functions." Biological Cybernetics 76, no. 6 (July 22, 1997): 451–58. http://dx.doi.org/10.1007/s004220050358.

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Lepora, Nathan F., and Kevin N. Gurney. "The Basal Ganglia Optimize Decision Making over General Perceptual Hypotheses." Neural Computation 24, no. 11 (November 2012): 2924–45. http://dx.doi.org/10.1162/neco_a_00360.

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The basal ganglia are a subcortical group of interconnected nuclei involved in mediating action selection within cortex. A recent proposal is that this selection leads to optimal decision making over multiple alternatives because the basal ganglia anatomy maps onto a network implementation of an optimal statistical method for hypothesis testing, assuming that cortical activity encodes evidence for constrained gaussian-distributed alternatives. This letter demonstrates that this model of the basal ganglia extends naturally to encompass general Bayesian sequential analysis over arbitrary probability distributions, which raises the proposal to a practically realizable theory over generic perceptual hypotheses. We also show that the evidence in this model can represent either log likelihoods, log-likelihood ratios, or log odds, all leading proposals for the cortical processing of sensory data. For these reasons, we claim that the basal ganglia optimize decision making over general perceptual hypotheses represented in cortex. The relation of this theory to cortical encoding, cortico-basal ganglia anatomy, and reinforcement learning is discussed.
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Plotkin, Joshua L., and Joshua A. Goldberg. "Thinking Outside the Box (and Arrow): Current Themes in Striatal Dysfunction in Movement Disorders." Neuroscientist 25, no. 4 (October 31, 2018): 359–79. http://dx.doi.org/10.1177/1073858418807887.

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The basal ganglia are an intricately connected assembly of subcortical nuclei, forming the core of an adaptive network connecting cortical and thalamic circuits. For nearly three decades, researchers and medical practitioners have conceptualized how the basal ganglia circuit works, and how its pathology underlies motor disorders such as Parkinson’s and Huntington’s diseases, using what is often referred to as the “box-and-arrow model”: a circuit diagram showing the broad strokes of basal ganglia connectivity and the pathological increases and decreases in the weights of specific connections that occur in disease. While this model still has great utility and has led to groundbreaking strategies to treat motor disorders, our evolving knowledge of basal ganglia function has made it clear that this classic model has several shortcomings that severely limit its predictive and descriptive abilities. In this review, we will focus on the striatum, the main input nucleus of the basal ganglia. We describe recent advances in our understanding of the rich microcircuitry and plastic capabilities of the striatum, factors not captured by the original box-and-arrow model, and provide examples of how such advances inform our current understanding of the circuit pathologies underlying motor disorders.
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Yin, Henry H. "The Basal Ganglia in Action." Neuroscientist 23, no. 3 (June 15, 2016): 299–313. http://dx.doi.org/10.1177/1073858416654115.

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The basal ganglia (BG) are the major subcortical nuclei in the brain. Disorders implicating the BG are characterized by diverse symptoms, but it remains unclear what these symptoms have in common or how they can be explained by changes in the BG circuits. This review summarizes recent findings that not only question traditional assumptions about the role of the BG in movement but also elucidate general computations performed by these circuits. To explain these findings, a new conceptual framework is introduced for understanding the role of the BG in behavior. According to this framework, the cortico-BG networks implement transition control in an extended hierarchy of closed loop negative feedback control systems. The transition control model provides a solution to the posture/movement problem, by postulating that BG outputs send descending signals to alter the reference states of downstream position control systems for orientation and body configuration. It also explains major neurological symptoms associated with BG pathology as a result of changes in system parameters such as multiplicative gain and damping.
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Dissertations / Theses on the topic "Basal ganglia model"

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Søiland, Stian. "Sequence learning in a model of the basal ganglia." Thesis, Norwegian University of Science and Technology, Department of Computer and Information Science, 2006. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-9312.

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This thesis presents a computational model of the basal ganglia that is able to learn sequences and perform action selection. The basal ganglia is a set of structures in the human brain involved in everything from action selection to reinforcement learning, inspiring research in psychology, neuroscience and computer science. Two temporal difference models of the basal ganglia based on previous work have been reimplemented. Several experiments and analyses help understand and describe the original works. This uncovered flaws and problems that is addressed.

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Senatore, Rosa. "The role of basal ganglia and cerebellum in motor learning. A computational model." Doctoral thesis, Universita degli studi di Salerno, 2012. http://hdl.handle.net/10556/373.

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2010 - 2011
Our research activity investigates the computational processes underlying the execution of complex sequences of movements and aims at understanding how different levels of the nervous system interact and contribute to the gradual improvement of motor performance during learning. Many research areas, from neuroscience to engineering, investigate, from different perspectives and for diverse purposes, the processes that allow humans to efficiently perform skilled movements. From a biological point of view, the execution of voluntary movements requires the interaction between nervous and musculoskeletal systems, involving several areas, from the higher cortical centers to motor circuits in the spinal cord. Understanding these interactions could provide important insights for many research fields, from machine learning to medicine, from the design of robotic limbs to the development of new treatments for movement disorders, such as Parkinson’s disease. This goal could be achieved by finding an answer to the following questions: · How does the central nervous system control and coordinate natural voluntary movements? · Which brain areas are involved in learning a new motor skill? What are the changes that happen in these neural structures? What are the aspects of the movement memorized? · Which is the process that allows people to perform a skilled task, such as playing an instrument, being apparently unaware of the movements they are performing? · What happen when a neurodegenerative disease affects the brain areas involved in executing movements? These questions have been addressed from different perspectives and levels of analysis, from the exploration of the anatomical structure of the neural systems thought to be involved in motor learning (such as the basal ganglia, cerebellum and hippocampus) to the investigation of their neural interaction; from the analysis of the activation of these systems in executing a motor task to the specific activation of a single or a small group of neurons within them. In seeking to understand all the breadth and facets of motor learning, many researchers have used different approaches and methods, such as genetic analysis, neuroimaging techniques (such as fMRI, PET and EEG), animal models and clinical treatments (e.g. drugs administration and brain stimulation). These studies have provided a large body of knowledge that has led to several theories related to the role of the central nervous system in controlling and learning simple and complex movements. These theories envisage the interaction among multiple brain regions, whose cooperation leads to the execution of skilled movements. How can we test these interactions for the purpose of evaluating a theory? Our answer to this question is investigating these interactions through computational models, which provide a valuable complement to the experimental brain research, especially in evaluating the interactions within and among multiple neural systems. Based on these concepts arises our research, which addresses the questions previously pointed out and aims at understanding the computational processes performed by two neural circuits, the Basal Ganglia and Cerebellum, in motor learning. We propose a new hypothesis about the neural processes occurring during acquisition and retention of novel motor skills. According to our hypothesis, a sequence of movements is stored in the nervous system in the form of a spatial sequence of points (composing the trajectory plan associated to the motor sequence) and a sequence of motor commands. We propose that learning novel motor skills requires two phases, in which two different processes take place. Early in learning, when movements are slower, less accurate, and attention demanding, the motor sequence is performed by converting the sequence of target points into the appropriate sequence of motor commands. During this phase, the trajectory plan is acquired and the movements rely on the information provided by the visuo-proprioceptive feedback, which allows to correct the sequence of movements so that the actual trajectory plan corresponds to the desired one and the lowest energy is spent by the muscular subsystem involved. During the late learning phase, when the sequence of movements is performed faster and automatically, with little or no cognitive resources needed to complete it, and is characterized by anticipatory movements, the sequence of motor commands is acquired and thus, the sequence of movements comes to be executed as a single behavior. We suggest that the Basal Ganglia and Cerebellum are involved in learning novel motor sequences, although their role is crucial in different stages of learning. Accordingly, we propose a neural scheme for procedural motor learning, comprising the basal ganglia, cerebellum and cortex, which envisages that the basal ganglia, interacting with the cortex, select the sequence of target points to reach (composing the trajectory plan), whereas the cerebellum, interacting with the cortex, is responsible for converting the trajectory plan into the appropriate sequence of motor commands. Consequently, we suggest that early in learning, task performance is more dependent on the procedural knowledge maintained by the cortex-basal ganglia system, while after a long-term practice, when the sequence of motor commands is acquired within the cerebellum, task performance is more dependent on the motor command sequence maintained by the cortexcerebellar system. We tested the neural scheme (and the hypothesis behind it) through a computational model that incorporates the key anatomical, physiological and biological features of these brain areas in an integrated functional network. Analyzing the behavior of the network in learning novel motor tasks and executing well-known motor tasks, both in terms of the neural activations and motor response provided, we found that the results obtained fit those reported by many neuroimaging and experimental studies presented in the literature. We also carried out further experiments, simulating neurodegenerative disorders (Parkinson's and Huntington disease, which affect the basal ganglia) and cerebellar damages. Results obtained by these experiments validates the proposed hypothesis, showing that the basal ganglia play a key role during the early stage of learning, whereas the cerebellum is crucial for motor skill retention. Our model provides some insights about the learning mechanisms occurring within the cerebellum and gains further understanding of the functional dynamics of information processing within the basal ganglia and cerebellum in normal as well as in diseased brains. Therefore the model provides novel predictions about the role of basal ganglia and cerebellum in motor learning, motivating further investigations of their interactions. [edited by author]
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Silva, Miranda B. A. "The role of prefrontal cortex and basal ganglia in model-based and model-free reinforcement learning." Thesis, University College London (University of London), 2016. http://discovery.ucl.ac.uk/1475076/.

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Contemporary reinforcement learning (RL) theory suggests that choices can be evaluated either by the model-free (MF) strategy of learning their past worth or the model-based (MB) strategy of predicting their likely consequences based on learning how decision states eventually transition to outcomes. Statistical and computational considerations argue that these strategies should ideally be combined. This thesis aimed to investigate the neural implementation of these two RL strategies and the mechanisms of their interactions. Two non-human primates performed a two-stage decision task designed to elicit and discriminate the use of both MF and MB-RL, while single-neuron activity was recorded from the prefrontal cortex (frontal pole, FP; anterior cingulate cortex, ACC; dorsolateral prefrontal cortex) and striatum (caudate and putamen). Logistic regression analysis revealed that the structure of the task (of MB importance) and the reward history (of MF and MB importance) significantly influenced choice. A trial-by-trial computational analysis also confirmed that choices were made according to a weighted combination of MF and MB- RL, with the influence of the latter approaching 90%. Furthermore, the valuations of both learning methods also influenced response vigour and pupil response. Neural correlates of key elements for MF and MB learning were observed across all brain areas, but functional segregation was also in evidence. Neurons in ACC encoded features of both MF and MB, suggesting a possible role in the arbitration between both strategies. Striatal activity was consistent with a role in value updating by encoding reward prediction errors. Finally, novel neurophysiological evidence was found in favour of the role of the FP in counterfactual processing. In conclusion, this thesis provides insight into the neural implementation of MF and MB-RL computations and their various effects on diverse aspects of behaviour. It supports the parallel operation and integration of the two approaches, while revealing unexpected intricacies.
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Kumbhare, Deepak. "ELECTROPHYSIOLOGY OF BASAL GANGLIA (BG) CIRCUITRY AND DYSTONIA AS A MODEL OF MOTOR CONTROL DYSFUNCTION." VCU Scholars Compass, 2016. http://scholarscompass.vcu.edu/etd/4305.

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The basal ganglia (BG) is a complex set of heavily interconnected nuclei located in the central part of the brain that receives inputs from the several areas of the cortex and projects via the thalamus back to the prefrontal and motor cortical areas. Despite playing a significant part in multiple brain functions, the physiology of the BG and associated disorders like dystonia remain poorly understood. Dystonia is a devastating condition characterized by ineffective, twisting movements, prolonged co-contractions and contorted postures. Evidences suggest that it occurs due to abnormal discharge patterning in BG-thalamocortocal (BGTC) circuitry. The central purpose of this study was to understand the electrophysiology of BGTC circuitry and its role in motor control and dystonia. Toward this goal, an advanced multi-target multi-unit recording and analysis system was utilized, which allows simultaneous collection and analysis of multiple neuronal units from multiple brain nuclei. Over the cause of this work, neuronal data from the globus pallidus (GP), subthalamic nucleus (STN), entopenduncular nucleus (EP), pallidal receiving thalamus (VL) and motor cortex (MC) was collected from normal, lesioned and dystonic rats under awake, head restrained conditions. The results have shown that the neuronal population in BG nuclei (GP, STN and EP) were characterized by a dichotomy of firing patterns in normal rats which remains preserved in dystonic rats. Unlike normals, neurons in dystonic rat exhibit reduced mean firing rate, increased irregularity and burstiness at resting state. The chaotic changes that occurs in BG leads to inadequate hyperpolarization levels within the VL thalamic neurons resulting in a shift from the normal bursting mode to an abnormal tonic firing pattern. During movement, the dystonic EP generates abnormally synchronized and elongated burst duration which further corrupts the VL motor signals. It was finally concluded that the loss of specificity and temporal misalignment between motor neurons leads to corrupted signaling to the muscles resulting in dystonic behavior. Furthermore, this study reveals the importance of EP output in controlling firing modes occurring in the VL thalamus.
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Du, Zhuowei. "Caractérisation of GABAergic neurotransmission within basal ganglia circuit in R6/1 Huntington's disease mouse model." Thesis, Bordeaux, 2014. http://www.theses.fr/2014BORD0046/document.

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Nous avons étudié les récepteurs GABAA dans un modèle de la maladie de Huntington. En combinant des approches biochimiques, moléculaires, électrophysiologiques et de l’imagerie haute résolution, nous avons montré une modification de la neurotransmission GABAergique chez des animaux à des stades pre- et post-symptomatiques. Nos études montrent une diminution de de la neurotransmission GABAergique dans le globus pallidus des souris Huntington qui pourrait conduire à une modification des noyaux de sortie des ganglions de la base et de l’activité motrice. L’ensemble de nos résultats permet de définir le rôle de différents types de récepteurs GABAA dans le cerveau dans des conditions physiologiques et pathologiques
We explored GABAergic neurotransmission in a mouse model of Huntington's disease. Combining molecular, imaging and electrophysiologicaltechniques, we showed changes of GABAergic neurotransmission in presymptomatic and symptomatic R6/1 mice. Our data demonstrated a decreased GABAergic inhibition in the globus pallidus of R6/1 mice, which could result in an alteration of basal ganglia output nuclei and motor activity. Taken together, our results will help to define the contribution of receptor subtypes to inhibitory transmission throughout the brain in physiological and pathophysiological states
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Slewa, Barbara Lidia [Verfasser]. "Electrophysiological activity of basal ganglia under deep brain stimulation in the rat model / Barbara Lidia Slewa." Tübingen : Universitätsbibliothek Tübingen, 2020. http://d-nb.info/1223451445/34.

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Haynes, William. "When anatomy drives physiology : expanding the actor-critic model of the basal ganglia to new subthalamus connections." Thesis, Paris 6, 2014. http://www.theses.fr/2014PA066662/document.

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Les noyaux gris centraux (ganglions de la base en anglais) sont un réseau de structures sous-corticales dont la persistance dans l'ensemble des vertébrés plaide en faveur d'une fonction clef au cours de l'évolution. Comme ce fut remarqué dès le 18ème siècle, ils ont l'unique particularité de concentrer des afférences de l'entièreté de la surface corticale. Cette position centrale et l'analyse de l'anatomie du réseau leur ont valu le rôle d'arbitre central du cerveau, réglant les conflits entre processus neuronaux concomitants bien qu'incompatibles. Au sein du réseau, le noyau subthalamique jouit d'une notoriété particulière. Ce noyau, sur la base de ses afférences corticales, et en vertu de ses projections sur le soma des neurones pallidaux, aurait pour fonction de filtrer les programmes comportementaux codés par le striatum et concourant pour leur expression. Rapporté aux théories de la prise de décision, le noyau subthalamique fixerait le seuil décisionnel, ou la quantité d'information à accumuler en faveur d'une option comportementale afin qu'elle soit exprimée. Mais si ce petit noyau est devenu si célèbre, c'est surtout qu'il est la cible d'une procédure chirurgicale spectaculaire: la stimulation cérébrale profonde. Cette opération du cerveau est le dernier recours pour les patients souffrant d'une maladie de Parkinson ou d'un trouble obsessionnel compulsif sévère. Elle parvient même parfois à faire disparaître leurs symptômes. Malgré cette efficacité remarquable, les mécanismes de la stimulation cérébrale profonde restent inconnus. Il faut, entre autres, blâmer l'obscurité qui règne encore sur le noyau subthalamique, car les fonctions mentionnées ci-dessus restent des conjectures théoriques en manque de validation expérimentale. La première étape de ce travail a été d'en valider les bases anatomiques. En effet, l'existence d'une voie fronto-subthalamique - nécessaire au modèle - n'était connue que sur la base d'études menées chez le rat. Nous avons démontré, par des méthodes de traçage axonal, l'existence de cette connexion chez le primate. En sus, cette connexion aura permis de redéfinir les frontières médiales du noyau subthalamique avec les conséquences cliniques qui peuvent en être tirées. Le deuxième objectif global de cette thèse était de tester la validité fonctionnelle du modèle, la stimulation cérébrale profonde offrant un accès rare aux activités du noyau subthalamique. Cependant, il était d'abord nécessaire de caractériser la population étudiée, à savoir des patients souffrants d'un trouble obsessionnel compulsif. Grâce à l'imagerie de diffusion nous démontrons une diminution ainsi qu'une désorganisation des connexions cortico-sous corticales, se traduisant probablement par un défaut de contrôle conscient sur le processus de sélection. Une étude de magnétoencéphalographie est en cours pour approfondir les changements d'activité corticale. Pour tester le rôle du noyau subthalamique dans l'établissement du seuil décisionnel nous avons enregistré son activité électrophysiologique pendant que les patients effectuaient une tâche de prise de décision perceptuelle. Nous démontrons que les neurones du noyau subthalamique ont une réponse multimodale, concordant en cela avec nos données anatomiques qui montrent une convergence d'informations au niveau du noyau subthalamique. De plus, une augmentation de l'activité est retrouvée dans les conditions attendues
The basal ganglia are a network of subcortical structures of which the invariant architecture throughout vertebrate evolution suggests a key function in evolution. As was noted as early as the 18th century, they have the unique characteristic of concentrating afferences from the entire cortical surgace. Given this central position and the internal architecture of the network, they could provide a centralised selection mechanism in the brain, arbitrating between any two conflicting processes. Among the basal ganglia, the subthalamic nucleus has become of particular interest as it is the target of deep brain stimulation, a neurosurgical procedure used to treat severe Parkinson’s disease and obsessive-compulsive disorder. It would have for function to integrate contextual information from its cortical inputs to filter behavioural programs encoded by the striatum. Within the framework of decision-making models, this filtering function is akin to setting the decision threshold, or the amount of evidence required before selecting a program. However, this considerations remain hypothetical as they are lacking experimental support. The first objective of this work was to validate the anatomical basis of these assumptions. Indeed, the existence of a prefrontal-subthalamic pathway, necessary to expand the decision models to every type of decision, had only been demonstrated in rodents. We demonstrated its existence in the primate using anterograde axonal tracing. In addition, this projection will have allowed us to redefine the medial border of the subthalamic nucleus with the clinical consequences that that may have. The second objective of this thesis was to test the functional validity of the models, and specifically the role of the subthalamic nucleus in setting decision thresholds. Deep brain stimulation offers a rare access to the electrophysiology of this structure; however, it is a patient population, here obsessive-compulsive disorder patients. A first step was, therefore, to characterise this population, anatomically and behaviourally, to understand how it might be of use as a model of decision-making in the basal ganglia. We demonstrated a reduction in the strength of cortico-subcortical anatomical connections. We suggest that this prevents accurate conscious control over decision mechanisms. Behaviourally, patients displayed a pathologically low confidence levels in their decisions and we hypothesised that this would lead to an increase of the decision threshold and matching subthalamic activity. To test this, we recorded the activity of the subthalamic nucleus during a decision-making task. We demonstrate that subthalamic neurons have a multimodal activity, consistent with our demonstration of convergent cortical inputs. However, we were unable to demonstrate a link between subthalamic activity and decision threshold, although this may be due to technical considerations…
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Zachrisson, Love. "HIGH-FREQUENCY OSCILLATIONS IN A MOUSE MODEL OF PARKINSON’S DISEASE." Thesis, Umeå universitet, Institutionen för psykologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-172265.

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Dopamine replacement therapy is the main method of treating Parkinson’s Disease (PD), however over time this treatment causes increasingly abnormal, involuntary movements. This symptom, known as Levodopa-Induced-Dyskinesia (LID) is associated with aberrant, high frequency oscillations (HFOs) in the motor cortex and basal ganglia, as demonstrated with implanted electrodes in human Parkinson’s patients as well as in a rat model of Parkinson’s Disease. However, despite efforts to determine if the same high frequency oscillations are also present during dyskinesia in the widespread 6-OHDA mouse model of Parkinson’s Disease, studies have been unable to do so. By building and implanting a 64-channel multi-electrode array into a unilateral 6-OHDA lesioned mouse, we were able to record HFOs at 80Hz and >100Hz in the motor cortex, basal ganglia and thalamus in the lesioned hemisphere during LID. We also recorded bilateral HFOs at >100Hz in the intact hemisphere. With this work we show that the same HFOs that are present in the motor cortex and basal ganglia of rats and humans are also present in mice during dyskinesia. This work will act to further validate the 6-OHDA PD-model in mice and provide opportunities to investigate new treatments for Parkinson’s Disease, dyskinesia and other neurological conditions. It will also serve as a model to study a purposed mechanism underlying the information processing in populations of neurons.
Dopaminbehandling är den mest förekommande metoden för att behandla Parkinsons sjukdom men detta orsakar dessvärre en bieffekt i form av gradvis förvärrande ofrivilliga rörelser. Detta beteendemönster kallas för Levodopa-Inducerad-Dyskinesi (LID) och med hjälp av elektrodimplantat i hjärnan, på parkinsonpatienter och djurmodeller av parkinsons, har man kunnat se att beteendet är förknippat med högfrekventa oscilleringar (HFO) av hjärnaktivitet i motorcortex och basala ganglierna. Trots försök att kartlägga om dessa högfrekventa oscilleringar också är närvarande i den populära 6-OHDA musmodellen av Parkinsons sjukdom, så har man hittills inte lyckats demonstrera detta. Genom att bygga och implantera ett elektrodimplantat med 64 kanaler i en ensidigt-leisonerad 6-OHDA musmodell av Parkinsons sjukdom så kunde vi åskådliggöra HFO i motor cortex, basala ganglierna och thalamus i den lesionerade hjärnhalvan under LID. Vi kunde också påvisa HFO som sträckte sig över till den intakta hjärnhalvan, med frekvenser över 100 Hz. Denna forskning ger stöd att 6-OHDA modellen för Parkinsons i möss är valid och ger möjlighet till nya metoder att utforska och behandla Parkinsons, dyskinesi och andra neurologiska åkommor. Studien lägger också grunden för framtida studier som ämnar att undersöka föreslagna mekanismer bakom sättet populationer av neuroner bearbetar information.
ingår i ett projekt finansierat av Vetenskapsrådet #2018-02717
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Canudas, Teixidó Anna-Maria. "Estudi de la degeneració transneuronal en models de malalties que afecten als ganglis basals." Doctoral thesis, Universitat de Barcelona, 2001. http://hdl.handle.net/10803/672867.

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L'objectiu general d'aquest treball és aprofundir en l'estudi de la fisiopatologia de les malalties degeneratives, un dels objectius principals per a la farmacologia actual degut a l'increment de la seva incidència en les últimes dècades. Mitjançant la utilització de models experimentals d 'aquestes patologies s'han plantejat diferents objectius més concrets: A.- Induir la malaltia de Parkinson experimental a través de la injecció de l'MPP+ en la substància negra de rata. A1.-Estudiar la resposta glial en el nucli estriat després de La degeneració anterograda de les neurones dopaminèrgiques causada per la injecció de l'MPP+ en la substància negra. Implicació en el mecanisme de mort neuronal i de regeneració. B.- Induir la malaltia de Huntington experimental a través de la injecció d'aminoàcids excitadors en el nucli estriat de rata. B1.-Caracteritzar la resposta endògena tròfica a l'excitotoxicitat, valorant els canvis en l 'expressió del BDNF i l'NT-3 així com la dels seus receptors, TrkB i TrkC, en l'escorça cerebral després de la injecció de diferents agonistes del receptor del glutamat en el nucli estriat. B2.-Estudiar la regulació endògena dels nivells de BDNF en Ja substància negra de rata després de la lesió estriatal induïda per l'àcid kaínic, així com la possible implicació d'aquesta neurotrofina en la supervivència de les neurones de la substància negra front la lesió excitotòxica en el nucli estriat. 3. Estudiar el possible efecte neuroprotector de les neurotrofines BDNF, NT-3 iNT-4/5, sobre les diferents poblacions neuronals de projecció del nucli estriat, en el model excitotòxic de l'àcid quinolínic. Es van implantar en l 'estriat de rata adulta línies cel·lulars establertes que secreten alts nivells de BDNF, NT-3 i NT-4/5 recombinant abans de la injecció de l'aminoàcid excitador.
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Thurnham, A. J. "Computational modelling of the neural systems involved in schizophrenia." Thesis, University of Hertfordshire, 2008. http://hdl.handle.net/2299/1842.

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The aim of this thesis is to improve our understanding of the neural systems involved in schizophrenia by suggesting possible avenues for future computational modelling in an attempt to make sense of the vast number of studies relating to the symptoms and cognitive deficits relating to the disorder. This multidisciplinary research has covered three different levels of analysis: abnormalities in the microscopic brain structure, dopamine dysfunction at a neurochemical level, and interactions between cortical and subcortical brain areas, connected by cortico-basal ganglia circuit loops; and has culminated in the production of five models that provide useful clarification in this difficult field. My thesis comprises three major relevant modelling themes. Firstly, in Chapter 3 I looked at an existing neural network model addressing the Neurodevelopmental Hypothesis of Schizophrenia by Hoffman and McGlashan (1997). However, it soon became clear that such models were overly simplistic and brittle when it came to replication. While they focused on hallucinations and connectivity in the frontal lobes they ignored other symptoms and the evidence of reductions in volume of the temporal lobes in schizophrenia. No mention was made of the considerable evidence of dysfunction of the dopamine system and associated areas, such as the basal ganglia. This led to my second line of reasoning: dopamine dysfunction. Initially I helped create a novel model of dopamine neuron firing based on the Computational Substrate for Incentive Salience by McClure, Daw and Montague (2003), incorporating temporal difference (TD) reward prediction errors (Chapter 5). I adapted this model in Chapter 6 to address the ongoing debate as to whether or not dopamine encodes uncertainty in the delay period between presentation of a conditioned stimulus and receipt of a reward, as demonstrated by sustained activation seen in single dopamine neuron recordings (Fiorillo, Tobler & Schultz 2003). An answer to this question could result in a better understanding of the nature of dopamine signaling, with implications for the psychopathology of cognitive disorders, like schizophrenia, for which dopamine is commonly regarded as having a primary role. Computational modelling enabled me to suggest that while sustained activation is common in single trials, there is the possibility that it increases with increasing probability, in which case dopamine may not be encoding uncertainty in this manner. Importantly, these predictions can be tested and verified by experimental data. My third modelling theme arose as a result of the limitations to using TD alone to account for a reinforcement learning account of action control in the brain. In Chapter 8 I introduce a dual weighted artificial neural network, originally designed by Hinton and Plaut (1987) to address the problem of catastrophic forgetting in multilayer artificial neural networks. I suggest an alternative use for a model with fast and slow weights to address the problem of arbitration between two systems of control. This novel approach is capable of combining the benefits of model free and model based learning in one simple model, without need for a homunculus and may have important implications in addressing how both goal directed and stimulus response learning may coexist. Modelling cortical-subcortical loops offers the potential of incorporating both the symptoms and cognitive deficits associated with schizophrenia by taking into account the interactions between midbrain/striatum and cortical areas.
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Books on the topic "Basal ganglia model"

1

Pieter, Voorn, Berendse Henk W, Mulder Antonius B, Cools Alexander Rudolf 1941-, and SpringerLink (Online service), eds. The Basal Ganglia IX. New York, NY: Springer-Verlag New York, 2009.

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Chakravarthy, V. Srinivasa, and Ahmed A. Moustafa. Computational Neuroscience Models of the Basal Ganglia. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8494-2.

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C, Houk James, Davis Joel L. 1942-, and Beiser David G, eds. Models of information processing in the basal ganglia. Cambridge, Mass: MIT Press, 1994.

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C, Houk James, Davis Joel L. 1942-, and Beiser David G, eds. Models of information processing in the basal ganglia. Cambridge, Mass: MIT Press, 1995.

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5

International Basal Ganglia Society. Symposium. The basal ganglia II: Structure and function : current concepts. New York: Plenum Press, 1987.

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P, Riederer, and Wesemann W, eds. Parkinson's disease: Experimental models and therapy. Wien: Springer-Verlag, 1995.

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7

Ely, Budding Deborah, ed. Subcortical structures and cognition: Implications for neuropsychological assessment. New York: Springer, 2009.

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Subcortical functions in language and memory. New York: Guilford Press, 1992.

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9

Steele, Vaughn R., Vani Pariyadath, Rita Z. Goldstein, and Elliot A. Stein. Reward Circuitry and Drug Addiction. Edited by Dennis S. Charney, Eric J. Nestler, Pamela Sklar, and Joseph D. Buxbaum. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190681425.003.0044.

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Addiction is a complex neuropsychiatric syndrome related to dysregulation of brain systems including the mesocorticolimbic dopamine reward circuit. Dysregulation of reward circuitry is related to each of the three cyclical stages in the disease model of addiction: maintenance, abstinence, and relapse. Parsing reward circuitry is confounded due to the anatomical complexity of cortico-basal ganglia-thalamocortical loops, forward and backward projections within the circuit, and interactions between neurotransmitter systems. We begin by introducing the neurobiology of the reward system, specifically highlighting nodes of the circuit beyond the basal ganglia, followed by a review of the current literature on reward circuitry dysregulation in addiction. Finally, we discuss biomarkers of addiction identified with neuroimaging that could help guide neuroprediction models and development of targets for effective new interventions, such as noninvasive brain stimulation. The neurocircuitry of reward, especially non-prototypical nodes, may hold essential keys to understanding and treating addiction.
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Symposium, International Basal Ganglia Society. The basal ganglia II. Plenum, 1987.

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Book chapters on the topic "Basal ganglia model"

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Koziol, Leonard F., Deborah Ely Budding, and Dana Chidekel. "The Basal Ganglia." In ADHD as a Model of Brain-Behavior Relationships, 35–39. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-8382-3_14.

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Koziol, Leonard F., Deborah Ely Budding, and Dana Chidekel. "The Basal Ganglia and Intention Programs." In ADHD as a Model of Brain-Behavior Relationships, 41–42. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-8382-3_15.

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Koziol, Leonard F., Deborah Ely Budding, and Dana Chidekel. "Reward Circuitry and the Basal Ganglia." In ADHD as a Model of Brain-Behavior Relationships, 45–49. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-8382-3_17.

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Mandali, Alekhya, and V. Srinivasa Chakravarthy. "Synchronization and Exploration in Basal Ganglia—A Spiking Network Model." In Computational Neuroscience Models of the Basal Ganglia, 97–112. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8494-2_6.

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Muralidharan, Vignesh, Pragathi Priyadharsini Balasubramani, V. Srinivasa Chakravarthy, and Ahmed A. Moustafa. "A Basal Ganglia Model of Freezing of Gait in Parkinson’s Disease." In Computational Neuroscience Models of the Basal Ganglia, 113–29. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8494-2_7.

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Krishnan, Ravi, Shivakesavan Ratnadurai, Deepak Subramanian, and Srinivasa Chakravarthy. "A Model of Basal Ganglia in Saccade Generation." In Artificial Neural Networks – ICANN 2010, 282–90. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15819-3_37.

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Muralidharan, Vignesh, Alekhya Mandali, Pragathi Priyadharsini Balasubramani, Hima Mehta, V. Srinivasa Chakravarthy, and Marjan Jahanshahi. "A Cortico-Basal Ganglia Model to Understand the Neural Dynamics of Targeted Reaching in Normal and Parkinson’s Conditions." In Computational Neuroscience Models of the Basal Ganglia, 167–95. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8494-2_10.

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Mandali, Alekhya, and V. Srinivasa Chakravarthy. "Studying the Effect of Dopaminergic Medication and STN–DBS on Cognitive Function Using a Spiking Basal Ganglia Model." In Computational Neuroscience Models of the Basal Ganglia, 197–214. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8494-2_11.

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Maya, M., V. Srinivasa Chakravarthy, and B. Ravindran. "An Oscillatory Neural Network Model for Birdsong Learning and Generation: Implications for the Role of Dopamine in Song Learning." In Computational Neuroscience Models of the Basal Ganglia, 255–84. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8494-2_14.

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Bogacz, Rafal. "Optimal Decision Making in the Cortico-Basal-Ganglia Circuit." In An Introduction to Model-Based Cognitive Neuroscience, 291–302. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-2236-9_14.

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Conference papers on the topic "Basal ganglia model"

1

Mohagheghi-Nejad, Mohammad Reza, Fariba Bahrami, and Mahyar Janahmadi. "Conductance-based computational model of basal ganglia." In 2014 22nd Iranian Conference on Electrical Engineering (ICEE). IEEE, 2014. http://dx.doi.org/10.1109/iraniancee.2014.6999867.

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Elibol, Rahmi, and Neslihan Serap Sengor. "Modeling basal ganglia circuits with mass model equations." In 2016 Medical Technologies National Congress (TIPTEKNO). IEEE, 2016. http://dx.doi.org/10.1109/tiptekno.2016.7863131.

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Guiyeom Kang and M. M. Lowery. "Conductance-based model of the basal ganglia in Parkinson's Disease." In IET Irish Signals and Systems Conference (ISSC 2009). IET, 2009. http://dx.doi.org/10.1049/cp.2009.1692.

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Ozdemir, Mustafa Yasir, and Neslihan Serap Sengor. "A Computational Model of Basal Ganglia Circuit Established with Spiking Neural Network." In 2017 21st National Biomedical Engineering Meeting (BIYOMUT). IEEE, 2017. http://dx.doi.org/10.1109/biyomut.2017.8479123.

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Gao, Yuanyuan, and Hongjun Song. "A motor learning model based on the basal ganglia in operant conditioning." In 2014 26th Chinese Control And Decision Conference (CCDC). IEEE, 2014. http://dx.doi.org/10.1109/ccdc.2014.6853115.

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Baston, Chiara, and Mauro Ursino. "A computational model of Dopamine and Acetylcholine aberrant learning in Basal Ganglia." In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2015. http://dx.doi.org/10.1109/embc.2015.7319883.

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Liang, Yabin, Zikai Yan, Qi Zhang, Hongyu Liang, Xiyu Ji, Yin Liu, and Rong Liu. "A Decision-Making Model Based on Basal Ganglia Account of Action Prediction." In 2019 IEEE International Conference on Robotics and Biomimetics (ROBIO). IEEE, 2019. http://dx.doi.org/10.1109/robio49542.2019.8961538.

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Cabessa, Jeremie, and Alessandro E. P. Villa. "Attractor-based complexity of a Boolean model of the basal ganglia-thalamocortical network." In 2016 International Joint Conference on Neural Networks (IJCNN). IEEE, 2016. http://dx.doi.org/10.1109/ijcnn.2016.7727812.

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Kepce, Ayca, and N. Serap Sengor. "Bifurcation Analysis of A Mass Model Related to Cortex - Basal Ganglia - Thalamus Loop." In 2019 Medical Technologies Congress (TIPTEKNO). IEEE, 2019. http://dx.doi.org/10.1109/tiptekno.2019.8894916.

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Liu, Jianbo, Hassan K. Khalil, and Karim G. Oweiss. "Model-based spatiotemporal analysis and control of a network of spiking Basal Ganglia neurons." In 5th International IEEE/EMBS Conference on Neural Engineering (NER 2011). IEEE, 2011. http://dx.doi.org/10.1109/ner.2011.5910540.

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