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The significance of gap junction location in striatal fast spiking interneurons
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: 0000-0002-0550-0739
2007 (English)In: Neurocomputing, ISSN 0925-2312, Vol. 70, no 10-12, 1887-1891 p.Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
2007. Vol. 70, no 10-12, 1887-1891 p.
Keyword [en]
striatum; fast spiking interneurons; gap junctions; synchronisation
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-5809DOI: 10.1016/j.neucom.2006.10.070ISI: 000247215300056Scopus ID: 2-s2.0-34247533233OAI: oai:DiVA.org:kth-5809DiVA: diva2:10313
Note
QC 20100720Available from: 2006-05-29 Created: 2006-05-29 Last updated: 2012-01-08Bibliographically approved
In thesis
1. Information processing in the Striatum: a computational study
Open this publication in new window or tab >>Information processing in the Striatum: a computational study
2006 (English)Licentiate 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.

Place, publisher, year, edition, pages
Stockholm: KTH, 2006. ix, 45 p.
Series
Trita-CSC-A, ISSN 1653-5723 ; 2006:8
Keyword
striatum, fast spiking interneuron, gap junctions, synchronisation, up-state detection, CaMKII, mathematical modelling
National Category
Neurosciences
Identifiers
urn:nbn:se:kth:diva-3999 (URN)91-7178-368-7 (ISBN)
Presentation
2006-06-14, E32, Lindstedsvägen 3, Stockholm, 14:00 (English)
Opponent
Supervisors
Note
QC 20101116Available from: 2006-05-29 Created: 2006-05-29 Last updated: 2010-11-16Bibliographically approved
2. Computer Modelling of Neuronal Interactions in the Striatum
Open this publication in new window or tab >>Computer Modelling of Neuronal Interactions in the Striatum
2009 (English)Doctoral 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.

Place, publisher, year, edition, pages
Stockholm: KTH, 2009. 79 p.
Series
Trita-CSC-A, ISSN 1653-5723 ; 2009:08
Keyword
striatum, fast spiking interneurons, medium spiny projection neurons, gap junctions, interoperability, MUSIC
National Category
Computer Science
Identifiers
urn:nbn:se:kth:diva-10523 (URN)978-91-7415-331-6 (ISBN)
Public defence
2009-06-11, Svedbergssalen (FD5), Roslagstullsbacken 21, Alba Nova, 09:00 (English)
Opponent
Supervisors
Note
QC 20100720Available from: 2009-06-03 Created: 2009-05-20 Last updated: 2010-07-20Bibliographically approved

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