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  • 1.
    Kamali Sarvestani, Iman
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Subsystems of the basal ganglia and motor infrastructure2013Doctoral 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.

  • 2.
    Kamali Sarvestani, Iman
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Ekeberg, Örjan
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Internal Connectivity of the GlobusPallidus and the Arbitration System2013Manuscript (preprint) (Other academic)
    Abstract [en]

    The rodent globus pallidus (homologue of primate external globus pallidus) has been shown to be composed of two types neuronal groups based on their location and local axon collaterals. The rostral outer layer near the striatopallidal border (GPr) has shorter but more dense local axon collaterals while the caudal inner layer (GPc) has wider and less dense axon collaterals. Moreover, the connection between the two segments is unidirectional with outer layer neurons sending inhibitory projections to the inner layer. Both segments inhibit the substantia nigra and the entopeduncular nucleus (homologue of primate internal globus pallidus). We have created a model of the basal ganglia arbitration subsystem composed of the subthalamic nucleus, the two segments of the pallidus as well as the entopeduncular nucleus and the substantia nigra in order to assess functional roles of the two pallidal segments. The simulations reveal that both segments of the pallidum are involved in winner-take-all structure of the arbitration system but the type of information competing is different in the two subsystems. In the STN-GPr network, strong lateral inhibition between pallidal neurons representing muscles leads to selection of a muscle which has been (due to noise or other reasons) randomly overactivated. In contrast, in STN-GPc network actions (each utilizing many muscles) compete. Our simulations suggest that both networks are active during selection and execution of movements. If overactivation of a muscle is accompanied with dopamine flow, the GPr-GPc connection together with local axonal network of GPc suppress other muscles and reinforce the muscle whose overactivity has caused the dopaminergic flow. Simulated lesions of these neuronal groups also show different results. Lesioning GPr results in synchronous activity in GPc and SNr but the mean firing rate of these nuclei remains untouched. Lesioning GPc on the other hand lifts the activity in the SNr drastically but does not create synchrony in any of the nuclei. The results suggest that STN-GPc and STN-GPr can be considered as two different subsystems working both in synergy and in competition.

  • 3.
    Kamali Sarvestani, Iman
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Kozlov, Alexander
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Harischandra, Nalin
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Grillner, Sten
    Karolinska Institutet.
    Ekeberg, Örjan
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    A computational model of visually guided locomotion in lamprey2013In: Biological Cybernetics, ISSN 0340-1200, E-ISSN 1432-0770, Vol. 107, no 5, p. 497-512Article in journal (Refereed)
    Abstract [en]

    This study addresses mechanisms for the generation and selection of visual behaviors in anamniotes. To demonstrate the function of these mechanisms, we have constructed an experimental platform where a simulated animal swims around in a virtual environment containing visually detectable objects. The simulated animal moves as a result of simulated mechanical forces between the water and its body. The undulations of the body are generated by contraction of simulated muscles attached to realistic body components. Muscles are driven by simulated motoneurons within networks of central pattern generators. Reticulospinal neurons, which drive the spinal pattern generators, are in turn driven directly and indirectly by visuomotor centers in the brainstem. The neural networks representing visuomotor centers receive sensory input from a simplified retina. The model also includes major components of the basal ganglia, as these are hypothesized to be key components in behavior selection. We have hypothesized that sensorimotor transformation in tectum and pretectum transforms the place-coded retinal information into rate-coded turning commands in the reticulospinal neurons via a recruitment network mimicking the layered structure of tectal areas. Via engagement of the basal ganglia, the system proves to be capable of selecting among several possible responses, even if exposed to conflicting stimuli. The anatomically based structure of the control system makes it possible to disconnect different neural components, yielding concrete predictions of how animals with corresponding lesions would behave. The model confirms that the neural networks identified in the lamprey are capable of responding appropriately to simple, multiple, and conflicting stimuli.

  • 4.
    Kamali Sarvestani, Iman
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Lindahl, Mikael
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Hellgren Kotaleski, Jeanette
    Ekeberg, Örjan
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    The arbitration-extension hypothesis: A hierarchical interpretation of the functional organization of the basal ganglia2011In: Frontiers in Systems Neuroscience, ISSN 1662-5137, E-ISSN 1662-5137, Vol. 5, p. 13-Article in journal (Refereed)
    Abstract [en]

    Based on known anatomy and physiology, we present a hypothesis where the basal ganglia motor loop is hierarchically organized in two main subsystems: the arbitration system and the extension system. The arbitration system, comprised of the subthalamic nucleus, globus pallidus, and pedunculopontine nucleus, serves the role of selecting one out of several candidate actions as they are ascending from various brain stem motor regions and aggregated in the centromedian thalamus or descending from the extension system or from the cerebral cortex. This system is an action-input/action-output system whose winner-take-all mechanism finds the strongest response among several candidates to execute. This decision is communicated back to the brain stem by facilitating the desired action via cholinergic/glutamatergic projections and suppressing conflicting alternatives via GABAergic connections. The extension system, comprised of the striatum and, again, globus pallidus, can extend the repertoire of responses by learning to associate novel complex states to certain actions. This system is a state-input/action-output system, whose organization enables it to encode arbitrarily complex Boolean logic rules using striatal neurons that only fire given specific constellations of inputs (Boolean AND) and pallidal neurons that are silenced by any striatal input (Boolean OR). We demonstrate the capabilities of this hierarchical system by a computational model where a simulated generic "animal" interacts with an environment by selecting direction of movement based on combinations of sensory stimuli, some being appetitive, others aversive or neutral. While the arbitration system can autonomously handle conflicting actions proposed by brain stem motor nuclei, the extension system is required to execute learned actions not suggested by external motor centers. Being precise in the functional role of each component of the system, this hypothesis generates several readily testable predictions.

  • 5.
    Lindahl, Mikael
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Sarvestani, Iman Kamali
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Ekeberg, Örjan
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Hällgren Kotaleski, Jeanette
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Signal enhancement in the output stage of the basal ganglia by synaptic short-term plasticity in the direct, indirect, and hyperdirect pathways2013In: Frontiers in Computational Neuroscience, ISSN 1662-5188, E-ISSN 1662-5188, Vol. 7, p. UNSP 76-Article in journal (Refereed)
    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.

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