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Signal enhancement in the output stage of the basal ganglia by synaptic short-term plasticity in the direct, indirect, and hyperdirect pathways
KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.ORCID iD: 0000-0002-2792-1622
KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.ORCID iD: 0000-0002-0550-0739
2013 (English)In: Frontiers in Computational Neuroscience, ISSN 1662-5188, E-ISSN 1662-5188, Vol. 7, UNSP 76- p.Article in journal (Refereed) Published
Abstract [en]

Many of the synapses in the basal ganglia display short-term plasticity. Still, computational models have not yet been used to investigate how this affects signaling. Here we use a model of the basal ganglia network, constrained by available data, to quantitatively investigate how synaptic short-term plasticity affects the substantia nigra reticulata (SNr), the basal ganglia output nucleus. We find that SNr becomes particularly responsive to the characteristic burst-like activity seen in both direct and indirect pathway striatal medium spiny neurons (MSN). As expected by the standard model, direct pathway MSNs are responsible for decreasing the activity in SNr. In particular, our simulations indicate that bursting in only a few percent of the direct pathway MSNs is sufficient for completely inhibiting SNr neuron activity. The standard model also suggests that SNr activity in the indirect pathway is controlled by MSNs disinhibiting the subthalamic nucleus (STN) via the globus pallidus externa (GPe). Our model rather indicates that SNr activity is controlled by the direct GPe-SNr projections. This is partly because GPe strongly inhibits SNr but also due to depressing STN-SNr synapses. Furthermore, depressing GPe-SNr synapses allow the system to become sensitive to irregularly firing GPe subpopulations, as seen in dopamine depleted conditions, even when the GPe mean firing rate does not change. Similar to the direct pathway, simulations indicate that only a few percent of bursting indirect pathway MSNs can significantly increase the activity in SNr. Finally, the model predicts depressing STN-SNr synapses, since such an assumption explains experiments showing that a brief transient activation of the hyperdirect pathway generates a tri-phasic response in SNr, while a sustained STN activation has minor effects. This can be explained if STN-SNr synapses are depressing such that their effects are counteracted by the (known) depressing GPe-SNr inputs.

Place, publisher, year, edition, pages
Frontiers Research Foundation , 2013. Vol. 7, UNSP 76- p.
Keyword [en]
substantia nigra pars reticulata, short-term plasticity, basal ganglia, network model, subthalamic nucleus, globus pallidus, facilitation, depression
National Category
Neurosciences Bioinformatics (Computational Biology)
Identifiers
URN: urn:nbn:se:kth:diva-125577DOI: 10.3389/fncom.2013.00076ISI: 000320851300001Scopus ID: 2-s2.0-84879713273OAI: oai:DiVA.org:kth-125577DiVA: diva2:639804
Funder
Swedish Research Council
Note

QC 20130809

Available from: 2013-08-09 Created: 2013-08-09 Last updated: 2017-12-06Bibliographically approved
In thesis
1. Subsystems of the basal ganglia and motor infrastructure
Open this publication in new window or tab >>Subsystems of the basal ganglia and motor infrastructure
2013 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The motor nervous system is one of the main systems of the body and is our principle means ofbehavior. Some of the most debilitating and wide spread disorders are motor systempathologies. In particular the basal ganglia are complex networks of the brain that control someaspects of movement in all vertebrates. Although these networks have been extensively studied,lack of proper methods to study them on a system level has hindered the process ofunderstanding what they do and how they do it. In order to facilitate this process I have usedcomputational models as an approach that can faithfully take into account many aspects of ahigh dimensional multi faceted system.In order to minimize the complexity of the system, I first took agnathan fish and amphibians asmodeling animals. These animals have rather simple neuronal networks and have been wellstudied so that developing their biologically plausible models is more feasible. I developedmodels of sensory motor transformation centers that are capable of generating basic behaviorsof approach, avoidance and escape. The networks in these models used a similar layeredstructure having a sensory map in one layer and a motor map on other layers. The visualinformation was received as place coded information, but was converted into population codedand ultimately into rate coded signals usable for muscle contractions.In parallel to developing models of visuomotor centers, I developed a novel model of the basalganglia. The model suggests that a subsystem of the basal ganglia is in charge of resolvingconflicts between motor programs suggested by different motor centers in the nervous system.This subsystem that is composed of the subthalamic nucleus and pallidum is called thearbitration system. Another subsystem of the basal ganglia called the extension system which iscomposed of the striatum and pallidum can bias decisions made by an animal towards theactions leading to lower cost and higher outcome by learning to associate proper actions todifferent states. Such states are generally complex states and the novel hypothesis I developedsuggests that the extension system is capable of learning such complex states and linking themto appropriate actions. In this framework, striatal neurons play the role of conjunction (BooleanAND) neurons while pallidal neurons can be envisioned as disjunction (Boolean OR) neurons.In the next set of experiments I tried to take the idea of basal ganglia subsystems to a new levelby dividing the rodent arbitration system into two functional subunits. A rostral group of ratpallidal neurons form dense local inhibition among themselves and even send inhibitoryprojections to the caudal segment. The caudal segment does not project back to its rostralcounterpart, but both segments send inhibitory projections to the output nuclei of the rat basalganglia i.e. the entopeduncular nucleus and substantia nigra. The rostral subsystems is capableof precisely detecting one (or several) components of a rudimentary action and suppress othercomponents. The components that are reinforced are those which lead to rewarding stateswhereas those that are suppressed are those which do not. The hypothesis explains neuronalmechanisms involved in this process and suggests that this subsystem is a means of generatingsimple but precise movements (such as using a single digit) from innate crude actions that theanimal can perform even at birth (such as general movement of the whole limb). In this way, therostral subsystem may play important role in exploration based learning.In an attempt to more precisely describe the relation between the arbitration and extensionsystems, we investigated the effect of dynamic synapses between subthalamic, pallidal andstriatal neurons and output neurons of the basal ganglia. The results imply that output neuronsare sensitive to striatal bursts and pallidal irregular firing. They also suggest that few striatalneurons are enough to fully suppress output neurons. Finally the results show that the globuspallidus exerts its effect on output neurons by direct inhibition rather than indirect influence viathe subthalamic nucleus.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2013. vii, 76 p.
Series
TRITA-CSC-A, ISSN 1653-5723 ; 2913:14
Keyword
Basal Ganglia, Action Selection, Motor Learning, Tectum, Superior Colliculus, Mesencephalic Locomotor Region, Reticulospinal Neurons, Computational Models
National Category
Neurosciences
Identifiers
urn:nbn:se:kth:diva-136745 (URN)978-91-7501-968-0 (ISBN)
Public defence
2013-12-19, Kollegiesalen, Brinellvägen 8, KTH, Stockholm, 10:00 (English)
Opponent
Supervisors
Note

QC 20131209

Available from: 2013-12-09 Created: 2013-12-09 Last updated: 2014-02-11Bibliographically approved
2. Computational Dissection of the Basal Ganglia: functions and dynamics in health and disease
Open this publication in new window or tab >>Computational Dissection of the Basal Ganglia: functions and dynamics in health and disease
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The basal ganglia (BG), a group of nuclei in the forebrain of all vertebrates, are important for behavioral selection. BG receive contextual input from most cortical areas as well as from parts of the thalamus, and provide output to brain systems that are involved in the generation of behavior, i.e. the thalamus and the brain stem. Many neurological disorders such as Parkinson’s disease and Huntington’s disease, and several neuropsychiatric disorders, are related to BG. Studying BG enhances the understanding as to how behaviors are learned and modified. These insights can be used to improve treatments for several BG disorders, and to develop brain-inspired algorithms for solving special information-processing tasks.

 

In this thesis modeling and simulations have been used to investigate function and dynamics of BG. In the first project a model was developed to explore a new hypothesis about how conflicts between competing actions are resolved in BG. It was proposed that a subsystem named the arbitration system, composed of the subthalamic nucleus (STN), pedunculopontine nucleus (PPN), the brain stem, central medial nucleus of thalamus (CM), globus pallidus interna (GPi) and globus pallidus externa (GPe), resolve basic conflicts between alternative motor programs. On top of the arbitration system there is a second subsystem named the extension systems, which involves the direct and indirect pathway of the striatum. This system can modify the output of the arbitration system to bias action selection towards outcomes dependent on contextual information.

 

In the second project a model framework was developed in two steps, with the aim to gain a deeper understanding of how synapse dynamics, connectivity and neural excitability in the BG relate to function and dynamics in health and disease. First a spiking model of STN, GPe and substantia nigra pars reticulata (SNr), with emulated inputs from striatal medium spiny neurons (MSNs) and the cortex, was built and used to study how synaptic short-term plasticity affected action selection signaling in the direct-, hyperdirect- and indirect pathways. It was found that the functional consequences of facilitatory synapses onto SNr neurons are substantial, and only a few presynaptic MSNs can suppress postsynaptic SNr neurons. The model also predicted that STN signaling in SNr is mainly effective in a transient manner. The model was later extended with a striatal network, containing MSNs and fast spiking interneurons (FSNs), and modified to represent GPe with two types of neurons: type I, which projects downstream in BG, and type A, which have a back-projection to striatum. Furthermore, dopamine depletion dependent modification of connectivity and neuron excitability were added to the model. Using this extended BG model, it was found that FSNs and GPe type A neurons controlled excitability of striatal neurons during low cortical drive, whereas MSN collaterals have a greater impact at higher cortical drive. The indirect pathway increased the dynamical range over which two possible action commands were competing, while removing intrastriatal inhibition deteriorated action selection capabilities. Dopamine-depletion induced effects on spike synchronization and oscillations in the BG were also investigated here.

 

For the final project, an abstract spiking BG model which included a hypothesized control of the reward signaling dopamine system was developed. This model incorporated dopamine-dependent synaptic plasticity, and used a plasticity rule based on probabilistic inference called Bayesian Confidence Propagation Neural Network (BCPNN). In this paradigm synaptic connections were controlled by gathering statistics about neural input and output activity. Synaptic weights were inferred using Bayes’ rule to estimate the confidence of future observations from the input. The model exhibits successful performance, measured as a moving average of correct selected actions, in a multiple-choice learning task with a changeable reward schedule. Furthermore, the model predicts a decreased performance upon dopamine lesioning, and suggests that removing the indirect pathway may disrupt learning in profound ways.

Place, publisher, year, edition, pages
Stockholm: Kungliga Tekniska högskolan, 2016. 47 p.
Series
TRITA-CSC-A, ISSN 1653-5723 ; 1653-5723
National Category
Neurosciences
Research subject
Computer Science
Identifiers
urn:nbn:se:kth:diva-185160 (URN)978-91-7595-925-2 (ISBN)
Public defence
2016-05-04, FS, Lindstedtsvägen 26, KTH Campus, Stockholm, 13:00
Opponent
Supervisors
Note

QC 20160412

Available from: 2016-04-12 Created: 2016-04-11 Last updated: 2016-04-12Bibliographically approved

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Ekeberg, ÖrjanHällgren Kotaleski, Jeanette

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