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  • 1.
    Djurfeldt, Mikael
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Hjorth, Johannes
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Eppler, Jochen
    Honda Research Institute.
    Dudani, Niraj
    Helias, Moritz
    University of Freiburg, Germany.
    Potjans, Tobias
    Bhalla, Upinder
    Diesmann, Markus
    Hellgren Kotaleski, Jeanette
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Ekeberg, Örjan
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Run-Time Interoperability Between Neuronal Network Simulators Based on the MUSIC Framework2010In: Neuroinformatics, ISSN 1539-2791, E-ISSN 1559-0089, Vol. 8, no 1, p. 43-60Article in journal (Refereed)
    Abstract [en]

    MUSIC is an API allowing large scale neuron simulators using MPI internally to exchange data during runtime. We provide experiences from the adaptation of two neuronal network simulators of different kinds, NEST and MOOSE, to this API. A multi-simulation of a cortico-striatal network model involving both simulators is performed, demonstrating how MUSIC can promote inter-operability between models written for different simulators and how these can be re-used to build a larger model system. We conclude that MUSIC fulfills the design goals of being portable and simple to adapt to existing simulators. In addition, since the MUSIC API enforces independence between the applications, the multi-simulationcould be built from pluggable component modules without adaptation of the components to each other in terms of simulation time-step or topology of connections between the modules.

  • 2.
    Hjorth, Johannes
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Computer Modelling of Neuronal Interactions in the Striatum2009Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Large parts of the cortex and the thalamus project into the striatum,which serves as the input stage of the basal ganglia. Information isintegrated in the striatal neural network and then passed on, via themedium spiny (MS) projection neurons, to the output stages of thebasal ganglia. In addition to the MS neurons there are also severaltypes of interneurons in the striatum, such as the fast spiking (FS)interneurons. I focused my research on the FS neurons, which formstrong inhibitory synapses onto the MS neurons. These striatal FSneurons are sparsely connected by electrical synapses (gap junctions),which are commonly presumed to synchronise their activity.Computational modelling with the GENESIS simulator was used toinvestigate the effect of gap junctions on a network of synapticallydriven striatal FS neurons. The simulations predicted a reduction infiring frequency dependent on the correlation between synaptic inputsto the neighbouring neurons, but only a slight synchronisation. Thegap junction effects on modelled FS neurons showing sub-thresholdoscillations and stuttering behaviour confirm these results andfurther indicate that hyperpolarising inputs might regulate the onsetof stuttering.The interactions between MS and FS neurons were investigated byincluding a computer model of the MS neuron. The hypothesis was thatdistal GABAergic input would lower the amplitude of back propagatingaction potentials, thereby reducing the calcium influx in thedendrites. The model verified this and further predicted that proximalGABAergic input controls spike timing, but not the amplitude ofdendritic calcium influx after initiation.Connecting models of neurons written in different simulators intonetworks raised technical problems which were resolved by integratingthe simulators within the MUSIC framework. This thesis discusses theissues encountered by using this implementation and gives instructionsfor modifying MOOSE scripts to use MUSIC and provides guidelines forachieving compatibility between MUSIC and other simulators.This work sheds light on the interactions between striatal FS and MSneurons. The quantitative results presented could be used to developa large scale striatal network model in the future, which would beapplicable to both the healthy and pathological striatum.

  • 3.
    Hjorth, Johannes
    KTH, School of Computer Science and Communication (CSC), Numerical Analysis and Computer Science, NADA.
    Information processing in the Striatum: a computational study2006Licentiate thesis, comprehensive summary (Other scientific)
    Abstract [en]

    The basal ganglia form an important structure centrally placed in the brain. They receive input from motor, associative and limbic areas, and produce output mainly to the thalamus and the brain stem. The basal ganglia have been implied in cognitive and motor functions. One way to understand the basal ganglia is to take a look at the diseases that affect them. Both Parkinson's disease and Huntington's disease with their motor problems are results of malfunctioning basal ganglia. There are also indications that these diseases affect cognitive functions. Drug addiction is another example that involves this structure, which is also important for motivation and selection of behaviour.

    In this licentiate thesis I am laying the groundwork for a detailed model of the striatum, which is the input stage of the basal ganglia. The striatum receives glutamatergic input from the cortex and thalamus, as well as dopaminergic input from substantia nigra. The majority of the neurons in the striatum are medium spiny (MS) projection neurons that project mainly to globus pallidus but also to other neurons in the striatum and to both dopamine producing and GABAergic neurons in substantia nigra. In addition to the MS neurons there are fast spiking (FS) interneurons that are in a position to regulate the firing of the MS neurons. These FS neurons are few, but connected into large networks through electrical synapses that could synchronise their effect. By forming strong inhibitory synapses on the MS neurons the FS neurons have a powerful influence on the striatal output. The inhibitory output of the basal ganglia on the thalamus is believed to keep prepared motor commands on hold, but once one of them is disinhibited, then the selected motor command is executed. This disinhibition is initiated in the striatum by the MS neurons.

    Both MS and FS neurons are active during so called up-states, which are periods of elevated cortical input to striatum. Here I have studied the FS neurons and their ability to detect such up-states. This is important because FS neurons can delay spikes in MS neurons and the time between up-state onset and the first spike in the MS neurons is correlated with the amount of calcium entering the MS neuron, which in turn might have implications for plasticity and learning of new behaviours. The effect of different combinations of electrical couplings between two FS neurons has been tested, where the location, number and strength of these gap junctions have been varied. I studied both the ability of the FS neurons to fire action potentials during the up-state, and the synchronisation between neighbouring FS neurons due to electrical coupling. I found that both proximal and distal gap junctions synchronised the firing, but the distal gap junctions did not have the same temporal precision. The ability of the FS neurons to detect an up-state was affected by whether the neighbouring FS neuron also received up-state input or not. This effect was more pronounced for distal gap junctions than proximal ones, due to a stronger shunting effect of distal gap junctions when the dendrites were synaptically activated.

    We have also performed initial stochastic simulations of the Ca2+-calmodulin-dependent protein kinase II (CaMKII). The purpose here is to build the knowledge as well as the tools necessary for biochemical simulations of intracellular processes that are important for plasticity in the MS neurons. The simulated biochemical pathways will then be integrated into an existing model of a full MS neuron. Another venue to explore is to build striatal network models consisting of MS and FS neurons and using experimental data of the striatal microcircuitry. With these different approaches we will improve our understanding of striatal information processing.

  • 4.
    Hjorth, Johannes
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Blackwell, Kim T
    Hellgren Kotaleski, Jeanette
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Gap Junctions between Striatal Fast-Spiking Interneurons Regulate Spiking Activity and Synchronization as a Function of Cortical Activity2009In: Journal of Neuroscience, ISSN 0270-6474, E-ISSN 1529-2401, Vol. 29, no 16, p. 5276-5286Article in journal (Refereed)
    Abstract [en]

    Striatal fast-spiking (FS) interneurons are interconnected by gap junctions into sparsely connected networks. As demonstrated for cortical FS interneurons, these gap junctions in the striatum may cause synchronized spiking, which would increase the influence that FS neurons have on spiking by the striatal medium spiny (MS) neurons. Dysfunction of the basal ganglia is characterized by changes in synchrony or periodicity, thus gap junctions between FS interneurons may modulate synchrony and thereby influence behavior such as reward learning and motor control. To explore the roles of gap junctions on activity and spike synchronization in a striatal FS population, we built a network model of FS interneurons. Each FS connects to 30-40% of its neighbors, as found experimentally, and each FS interneuron in the network is activated by simulated corticostriatal synaptic inputs. Our simulations show that the proportion of synchronous spikes in FS networks with gap junctions increases with increased conductance of the electrical synapse; however, the synchronization effects are moderate for experimentally estimated conductances. Instead, the main tendency is that the presence of gap junctions reduces the total number of spikes generated in response to synaptic inputs in the network. The reduction in spike firing is due to shunting through the gap junctions; which is minimized or absent when the neurons receive coincident inputs. Together these findings suggest that a population of electrically coupled FS interneurons may function collectively as input detectors that are especially sensitive to synchronized synaptic inputs received from the cortex.

  • 5.
    Hjorth, Johannes
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Blackwell, Kim T.
    Hellgren Kotaleski, Jeanette
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Gap junctions on striatal fast spiking interneurons reduce firing for non-correlated inputs2008Conference paper (Refereed)
  • 6.
    Hjorth, Johannes
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Elias, Alex Hanna
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Hellgren Kotaleski, Jeanette
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    The significance of gap junction location in striatal fast spiking interneurons2007In: Neurocomputing, ISSN 0925-2312, E-ISSN 1872-8286, Vol. 70, no 10-12, p. 1887-1891Article in journal (Refereed)
    Abstract [en]

    Fast spiking (FS) interneurons in the striatunt are hypothesised to control spike timing in the numerous medium spiny (MS) projection neurons by inhibiting or delaying firing in the MS neurons. The FS neurons are connected to each other through electrical gap junctions. This might synchronise the FS neurons, leading to increased influence on target neurons. Here, we explore the possible difference between proximal and distal gap junction locations. Somatic and distal dendritic gap junctions with equal effective coupling coefficient, as defined for steady-state somatic inputs, showed significantly different effective coupling coefficient with transient inputs. However, the ability to synchronise spiking in pairwise coupled FS neurons, which received synaptic inputs as during striatal up-state periods, was as effective with distal gap junctions as with proximal ones. Proximal gap junctions, however, caused synchronisation within a more precise time window.

  • 7.
    Hjorth, Johannes
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Hedlund, Lennart
    KTH, School of Computer Science and Communication (CSC).
    Blackwell, Kim T
    Hellgren Kotaleski, Jeanette
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Synchronization Effects in Networks of Striatal Fast Spiking Interneurons - Role of Gap Junctions2008In: ADVANCES IN COGNITIVE NEURODYNAMICS, PROCEEDINGS / [ed] Wang R, Gu F, Shen E, TOTOWA: HUMANA PRESS INC , 2008, p. 63-66Conference paper (Refereed)
    Abstract [en]

    Recent studies have found gap junctions between striatal fast spiking interneurons (FSN). Gap junctions between neocortical FSNs cause increased synchrony of firing in response to current injection, but the effect of gap junctions in response to synaptic input is unknown. To explore this issue, we built a network model of FSNs. Each FSN connects to 30-40% of its neighbours, as found experimentally, and each FSN in the network is activated by simulated up-state synaptic inputs. Simulation experiments show that the proportion of synchronous spikes in coupled FSNs increases with gap junction conductance. Proximal gap junctions increase the synchronization more than distal gap junctions. During up-states the synchronization effects in FSNs coupled pairwise with proximal gap junctions are small for experimentally estimated gap junction conductances; however, higher order correlations are significantly increased in larger FSN networks.

  • 8.
    Hjorth, Johannes
    et al.
    KTH, School of Computer Science and Communication (CSC), Numerical Analysis and Computer Science, NADA.
    Hellgren Kotaleski, Jeanette
    KTH, School of Computer Science and Communication (CSC), Numerical Analysis and Computer Science, NADA.
    Up-State signaling and Coincidence Detection in Striatal Fast Spiking Interneurons Coupled through Gap JunctionsManuscript (Other academic)
  • 9.
    Hjorth, Johannes
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Klaus, Andreas
    Hellgren Kotaleski, Jeanette
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    The influence of stuttering properties for firing activity in pairs of electrically coupled striatal fast-spiking interneurons2009In: Neuroinformatics 2009. Pilsen, Czech Republic, September 06 - 08,  2009, 2009Conference paper (Other academic)
    Abstract [en]

    The striatum is the main input stage of the basal ganglia system, which is involved in executive functions of the forebrain – such as the planning and the selection of motor behavior. Feedforward inhibition of medium-sized spiny projection neurons in the striatum by fast-spiking interneurons is supposed to be an important determinant of controlling striatal output to later stages of the basal ganglia [1]. Striatal fast-spiking interneurons, which constitute approximately 1-2 % of all striatal neurons, show many similarities to cortical fast-spiking cells. In response to somatic current injection, for example, some of these neurons exhibit spike bursts with a variable number of action potentials (so called stuttering) [2-4]. Interestingly, the membrane potential between such stuttering episodes oscillates in the range of 20-100 Hz [3,5]. The first spike of each stuttering episode invariably occurs at a peak of the underlying subthreshold oscillation. In both cortex and striatum, fast-spiking cells have been shown to be inter-connected by gap junctions [6,7]. In vitro measurements as well as theoretical studies indicate that electrical coupling via gap junctions might be able to promote synchronous activity among these neurons [6,8].Here we use computational modeling to investigate how the presence of subthreshold oscillations and stuttering properties influence the synchronization of activity in pairs of electrically coupled fast-spiking neurons. We use the model of Golomb et al. [3], which we have extended with a dendritic tree in order to be able to simulate distal synaptic input. We show that gap junctions are able to synchronize both subthreshold membrane potential fluctuations as well as the stuttering periods in response to somatic current injection. In response to synaptic input, however, our model neuron rarely shows subthreshold oscillations, and the stuttering behavior changes to a firing pattern with single spikes or spike doublets. We furthermore investigate the effect of GABAergic (i.e. inhibitory) input to the model of the fast-spiking neuron and predict that inhibitory input is able to induce overlapping stuttering episodes in these cells. We finally discuss our results in the context of the feedforward inhibitory network which is likely to play an important role in striatal and basal ganglia function.

  • 10. Hjorth, Johannes
    et al.
    Krieger, P
    Oliveira, R.F.
    Blackwell, Kim T
    Hellgren Kotaleski, Jeanette
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    GABAergic control of dendritic calcium dynamics in striatal medium spiny neurons2008Conference paper (Refereed)
    Abstract [en]

    Experiments have demonstrated the ability of action potentials to actively backpropagate in striatal medium spiny (MS) neurons, affecting the calcium levels in the dendrites [1, 2, 3]. Increased calcium levels trigger changes in plasticity [4, 5], which is important for learning and other functions [6]. Studies in the hippocampus have shown that GABAergic input can modulate the backpropagation of action potentials from the soma to the distal dendrites [7]. The MS neurons receive both proximal feedforward GABAergic inhibition from fast spiking interneurons (FS), and distal feedback inhibition from other neighbouring MS neurons. In the present study the effect of GABAergic inputs on the dendritic calcium dynamics is investigated.

  • 11.
    Hjorth, Johannes
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Zilberter, M
    Oliveria, R.F.
    Blackwell, Kim T.
    Hellgren Kotaleski, Jeanette
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    GABAergic control of backpropagating action potentials in striatal medium spiny neurons2008Conference paper (Refereed)
    Abstract [en]

    Experiments have demonstrated the ability of action potentials to actively backpropagate in striatal medium spiny (MS) neurons, affecting the calcium levels in the dendrites[1-3]. Increased calcium levels trigger changes in plasticity[4,5], which is important for learning and other functions[6]. Studies in the hippocampus have shown that GABAergic input can modulate the backpropagation of action potentials from the soma to the distal dendrites[7]. The MS neurons receive both proximal feedforward GABAergic inhibition from fast spiking interneurons (FS), and distal feedback inhibition from other neighbouring MS neurons. In the present study the effect of these GABAergic inputs on the dendritic calcium dynamics is investigated.

  • 12. Klaus, A.
    et al.
    Hjorth, Johannes
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Hellgren Kotaleski, Jeanette
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    The influence of subthreshold membrane potential oscillations and GABAergic input on firing activity in striatal fast-spiking neurons2009In: BMC Neuroscience, ISSN 1471-2202, Vol. 10, no Suppl.1, p. P244-Article in journal (Refereed)
    Abstract [en]

    The striatum is the main input stage of the basal ganglia system, which is involved in executive functions of the forebrain, such as the planning and the selection of motor behavior. Feedforward inhibition of medium-sized spiny projection neurons in the striatum by fast-spiking interneurons is supposed to be an important determinant of controlling striatal output to later stages of the basal ganglia[1]. Striatal fast-spiking interneurons, which constitute approximately 1–2% of all striatal neurons, show many similarities to cortical fast-spiking cells. In response to somatic current injection, for example, some of these neurons exhibit spike bursts with a variable number of action potentials (so called stuttering)[2-4]. Interestingly, the membrane potential between such stuttering episodes oscillates in the range of 20–100 Hz[3,5]. The first spike of each stuttering episode invariably occurs at a peak of the underlying subthreshold oscillation. In both cortex and striatum, fast-spiking cells are inter-connected by gap junctions[6,7]. In vitro measurements as well as theoretical studies indicate that electrical coupling via gap junctions might be able to promote synchronous activity among these neurons[6,8]. Here we investigate the possible role of subthreshold oscillations on the synchronization of sub- and suprathreshold activity in a model of electrically coupled fast-spiking neurons. We use the model of Golomb et al.[3], which we extended with a dendritic tree so as to be able to simulate distal synaptic input. We show that gap junctions are able to synchronize subthreshold membrane potential fluctuations in response to somatic current injection. However, the oscillations are only prevalent in the subthreshold range and therefore require enough membrane potential depolarization[5]. In response to synaptic input, our model neuron only enters the subthreshold oscillatory regime with AMPA and NMDA synapses located at distal dendrites. Proximal synaptic input leads to more random fluctuations of the membrane potential, reflecting a smaller extent of dendritic filtering of the Poisson-distributed postsynaptic potentials. We furthermore investigate the effect of GABAergic (i.e. inhibitory) input to the model of the fast-spiking neuron and predict that inhibitory input is able to induce a stuttering episode in these cells. We finally discuss our results in the context of the feedforward inhibitory network, which is likely to play an important role in striatal and basal ganglia function.

  • 13. Klaus, A.
    et al.
    Planert, H.
    Hjorth, Johannes
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Berke, J.D.
    Silberberg, G.
    Hellgren Kotaleski, Jeanette
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Striatal fast-spiking interneurons: from firing patterns to postsynaptic impact2011In: Frontiers in Systems Neuroscience, ISSN 1662-5137, E-ISSN 1662-5137, Vol. 5, no July, p. 57-Article in journal (Refereed)
    Abstract [en]

    In the striatal microcircuit, fast-spiking (FS) interneurons have an important role in mediating inhibition onto neighboring medium spiny (MS) projection neurons. In this study, we combined computational modeling with in vitro and in vivo electrophysiological measurements to investigate FS cells in terms of their discharge properties and their synaptic efficacies onto MS neurons. In vivo firing of striatal FS interneurons is characterized by a high firing variability. It is not known, however, if this variability results from the input that FS cells receive, or if it is promoted by the stuttering spike behavior of these neurons. Both our model and measurements in vitro show that FS neurons that exhibit random stuttering discharge in response to steady depolarization do not show the typical stuttering behavior when they receive fluctuating input. Importantly, our model predicts that electrically coupled FS cells show substantial spike synchronization only when they are in the stuttering regime. Therefore, together with the lack of synchronized firing of striatal FS interneurons that has been reported in vivo, these results suggest that neighboring FS neurons are not in the stuttering regime simultaneously and that in vivo FS firing variability is more likely determined by the input fluctuations. Furthermore, the variability in FS firing is translated to variability in the postsynaptic amplitudes in MS neurons due to the strong synaptic depression of the FS-to-MS synapse. Our results support the idea that these synapses operate over a wide range from strongly depressed to almost fully recovered. The strong inhibitory effects that FS cells can impose on their postsynaptic targets, and the fact that the FS-to-MS synapse model showed substantial depression over extended periods of time might indicate the importance of cooperative effects of multiple presynaptic FS interneurons and the precise orchestration of their activity.

  • 14. Nadadhur, A. G.
    et al.
    Melero, J. E.
    Meijer, M.
    Schut, D.
    Jacobs, G.
    Wan Li, K.
    Hjorth, Johannes
    KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST). KTH, Centres, Science for Life Laboratory, SciLifeLab. Vrije Universiteit, Netherlands.
    Meredith, R. M.
    Toonen, R. F.
    Van Kesteren, R. E.
    Smit, A. B.
    Verhage, M.
    Heine, V. M.
    Multi-level characterization of balanced inhibitory-excitatory cortical neuron network derived from human pluripotent stem cells2017In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 12, no 6, article id 0178533Article in journal (Refereed)
    Abstract [en]

    Generation of neuronal cultures from induced pluripotent stem cells (hiPSCs) serve the studies of human brain disorders. However we lack neuronal networks with balanced excitatory- inhibitory activities, which are suitable for single cell analysis. We generated low-density networks of hPSC-derived GABAergic and glutamatergic cortical neurons. We used two different co-culture models with astrocytes. We show that these cultures have balanced excitatory-inhibitory synaptic identities using confocal microscopy, electrophysiological recordings, calcium imaging and mRNA analysis. These simple and robust protocols offer the opportunity for single-cell to multi-level analysis of patient hiPSC-derived cortical excitatory- inhibitory networks; thereby creating advanced tools to study disease mechanisms underlying neurodevelopmental disorders.

  • 15. Planert, Henrike
    et al.
    Szydlowski, Susanne N.
    Hjorth, Johannes
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Grillner, Sten
    Silberberg, Gilad
    Dynamics of Synaptic Transmission between Fast-Spiking Interneurons and Striatal Projection Neurons of the Direct and Indirect Pathways2010In: Journal of Neuroscience, ISSN 0270-6474, E-ISSN 1529-2401, Vol. 30, no 9, p. 3499-3507Article in journal (Refereed)
    Abstract [en]

    The intrastriatal microcircuit is a predominantly inhibitory GABAergic network comprised of a majority of projection neurons [medium spiny neurons (MSNs)] and a minority of interneurons. The connectivity within this microcircuit is divided into two main categories: lateral connectivity between MSNs, and inhibition mediated by interneurons, in particular fast spiking (FS) cells. To understand the operation of striatum, it is essential to have a good description of the dynamic properties of these respective pathways and how they affect different types of striatal projection neurons. We recorded from neuronal pairs, triplets, and quadruplets in slices of rat and mouse striatum and analyzed the dynamics of synaptic transmission between MSNs and FS cells. Retrograde fluorescent labeling and transgenic EGFP (enhanced green fluorescent protein) mice were used to distinguish between MSNs of the direct (striatonigral) and indirect (striatopallidal) pathways. Presynaptic neurons were stimulated with trains of action potentials, and activity-dependent depression and facilitation of synaptic efficacy was recorded from postsynaptic neurons. We found that FS cells provide a strong and homogeneously depressing inhibition of both striatonigral and striatopallidal MSN types. Moreover, individual FS cells are connected to MSNs of both types. In contrast, both MSN types receive sparse and variable, depressing and facilitating synaptic transmission from nearby MSNs. The connection probability was higher for pairs with presynaptic striatopallidal MSNs; however, the variability in synaptic dynamics did not depend on the types of interconnected MSNs. The differences between the two inhibitory pathways were clear in both species and at different developmental stages. Our findings show that the two intrastriatal inhibitory pathways have fundamentally different dynamic properties that are, however, similarly applied to both direct and indirect striatal projections.

  • 16.
    Sandström, Malin
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Hjorth, Johannes
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Lansner, Anders
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Hellgren Kotaleski, Jeanette
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    The impact of the distribution of isoforms on CaMKII activation2006In: Neurocomputing, ISSN 0925-2312, Vol. 69, no 10-12, p. 1010-1013Article in journal (Refereed)
    Abstract [en]

    We have developed a computational model of the regulation of alpha- and beta-CaMKII activity, in order to examine (i) the importance of neighbour subunit interactions and (ii) the effect the higher CaMCa4 affinity of beta-CaMKII has on the holoenzyme activity in different configurations with the same alpha: beta ratio. The model consists of a deterministic biochemical network coupled to stochastic activation of CaMKII The results suggest that CaMKII holoenzyme activity is non-linear and dependent on the holoenzyme configuration of isoforms. This is especially pronounced in situations with a high-dephosphorylation rate of CaMKII.

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