Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Information processing in the Striatum: a computational study
KTH, School of Computer Science and Communication (CSC), Numerical Analysis and Computer Science, NADA.
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 [en]
striatum, fast spiking interneuron, gap junctions, synchronisation, up-state detection, CaMKII, mathematical modelling
National Category
Neurosciences
Identifiers
URN: urn:nbn:se:kth:diva-3999ISBN: 91-7178-368-7 (print)OAI: oai:DiVA.org:kth-3999DiVA: diva2:10316
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
List of papers
1. The significance of gap junction location in striatal fast spiking interneurons
Open this publication in new window or tab >>The significance of gap junction location in striatal fast spiking interneurons
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.

Keyword
striatum; fast spiking interneurons; gap junctions; synchronisation
National Category
Computer Science
Identifiers
urn:nbn:se:kth:diva-5809 (URN)10.1016/j.neucom.2006.10.070 (DOI)000247215300056 ()2-s2.0-34247533233 (Scopus ID)
Note
QC 20100720Available from: 2006-05-29 Created: 2006-05-29 Last updated: 2012-01-08Bibliographically approved
2. Up-State signaling and Coincidence Detection in Striatal Fast Spiking Interneurons Coupled through Gap Junctions
Open this publication in new window or tab >>Up-State signaling and Coincidence Detection in Striatal Fast Spiking Interneurons Coupled through Gap Junctions
(English)Manuscript (Other academic)
Identifiers
urn:nbn:se:kth:diva-5810 (URN)
Note
QC 20101116Available from: 2006-05-29 Created: 2006-05-29 Last updated: 2010-11-16Bibliographically approved
3. The impact of the distribution of isoforms on CaMKII activation
Open this publication in new window or tab >>The impact of the distribution of isoforms on CaMKII activation
2006 (English)In: Neurocomputing, ISSN 0925-2312, Vol. 69, no 10-12, 1010-1013 p.Article in journal (Refereed) Published
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.

Keyword
CaMKII; Plasticity; Computer modelling; Stochastic model
National Category
Information Science
Identifiers
urn:nbn:se:kth:diva-7227 (URN)10.1016/j.neucom.2005.12.035 (DOI)000237873900004 ()2-s2.0-33646520327 (Scopus ID)
Note
Hjorth, Johannes: Lic (Manuskript) QC 20100723Available from: 2007-05-30 Created: 2007-05-30 Last updated: 2012-01-08Bibliographically approved

Open Access in DiVA

fulltext(5807 kB)518 downloads
File information
File name FULLTEXT01.pdfFile size 5807 kBChecksum MD5
6c85d9c43d2552f88c05cf36b6502bd97fb39e1bd4bc563053d06c3e6df27d4da295fb3f
Type fulltextMimetype application/pdf

Search in DiVA

By author/editor
Hjorth, Johannes
By organisation
Numerical Analysis and Computer Science, NADA
Neurosciences

Search outside of DiVA

GoogleGoogle Scholar
Total: 518 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

isbn
urn-nbn

Altmetric score

isbn
urn-nbn
Total: 614 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf