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Up-State signaling and Coincidence Detection in Striatal Fast Spiking Interneurons Coupled through Gap Junctions
KTH, School of Computer Science and Communication (CSC), Numerical Analysis and Computer Science, NADA.
KTH, School of Computer Science and Communication (CSC), Numerical Analysis and Computer Science, NADA.ORCID iD: 0000-0002-0550-0739
(English)Manuscript (Other academic)
URN: urn:nbn:se:kth:diva-5810OAI: diva2:10314
QC 20101116Available from: 2006-05-29 Created: 2006-05-29 Last updated: 2010-11-16Bibliographically 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.
Trita-CSC-A, ISSN 1653-5723 ; 2006:8
striatum, fast spiking interneuron, gap junctions, synchronisation, up-state detection, CaMKII, mathematical modelling
National Category
urn:nbn:se:kth:diva-3999 (URN)91-7178-368-7 (ISBN)
2006-06-14, E32, Lindstedsvägen 3, Stockholm, 14:00 (English)
QC 20101116Available from: 2006-05-29 Created: 2006-05-29 Last updated: 2010-11-16Bibliographically approved

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Hjorth, JohannesHellgren Kotaleski, Jeanette
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