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Computer Modelling of Neuronal Interactions in the Striatum
KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
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 [en]
striatum, fast spiking interneurons, medium spiny projection neurons, gap junctions, interoperability, MUSIC
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-10523ISBN: 978-91-7415-331-6 (print)OAI: oai:DiVA.org:kth-10523DiVA: diva2:218751
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
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, E-ISSN 1872-8286, 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: 2017-12-14Bibliographically approved
2. Synchronization Effects in Networks of Striatal Fast Spiking Interneurons - Role of Gap Junctions
Open this publication in new window or tab >>Synchronization Effects in Networks of Striatal Fast Spiking Interneurons - Role of Gap Junctions
2008 (English)In: ADVANCES IN COGNITIVE NEURODYNAMICS, PROCEEDINGS / [ed] Wang R, Gu F, Shen E, TOTOWA: HUMANA PRESS INC , 2008, 63-66 p.Conference paper, Published 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.

Place, publisher, year, edition, pages
TOTOWA: HUMANA PRESS INC, 2008
Series
ADVANCES IN COGNITIVE NEURODYNAMICS, PROCEEDINGS
Keyword
Fast spiking interneurons, gap junctions, synchronization, striatum, computational modeling
National Category
Computer Science
Identifiers
urn:nbn:se:kth:diva-14149 (URN)10.1007/978-1-4020-8387-7_13 (DOI)000262360700013 ()978-1-4020-8386-0 (ISBN)
Conference
1st International Conference on Cognitive Neurodynamics Shanghai, PEOPLES R CHINA, NOV 17-21, 2007
Note
QC 20100720Available from: 2010-07-20 Created: 2010-07-20 Last updated: 2011-03-21Bibliographically approved
3. Gap Junctions between Striatal Fast-Spiking Interneurons Regulate Spiking Activity and Synchronization as a Function of Cortical Activity
Open this publication in new window or tab >>Gap Junctions between Striatal Fast-Spiking Interneurons Regulate Spiking Activity and Synchronization as a Function of Cortical Activity
2009 (English)In: Journal of Neuroscience, ISSN 0270-6474, E-ISSN 1529-2401, Vol. 29, no 16, 5276-5286 p.Article in journal (Refereed) Published
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.

Keyword
timing-dependent plasticity; electrical synapses; basal ganglia; gabaergic interneurons; spiny neurons; corticostriatal synapses; synaptic plasticity; projection neurons; in-vitro; states
National Category
Computer Science
Identifiers
urn:nbn:se:kth:diva-14150 (URN)10.1523/JNEUROSCI.6031-08.2009 (DOI)000265450300026 ()2-s2.0-65549166921 (Scopus ID)
Note
QC 20100720Available from: 2010-07-20 Created: 2010-07-20 Last updated: 2017-12-12Bibliographically approved
4. The influence of stuttering properties for firing activity in pairs of electrically coupled striatal fast-spiking interneurons
Open this publication in new window or tab >>The influence of stuttering properties for firing activity in pairs of electrically coupled striatal fast-spiking interneurons
2009 (English)In: Neuroinformatics 2009. Pilsen, Czech Republic, September 06 - 08,  2009, 2009Conference paper, Published 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.

National Category
Computer Science
Identifiers
urn:nbn:se:kth:diva-14151 (URN)10.3389/conf.neuro.11.2009.08.102 (DOI)
Conference
Neuroinformatics 2009. Pilsen, Czech Republic, September 06 - 08, 2009
Note
QC 20100720Available from: 2010-07-20 Created: 2010-07-20 Last updated: 2012-01-08Bibliographically approved
5. Run-Time Interoperability Between Neuronal Network Simulators Based on the MUSIC Framework
Open this publication in new window or tab >>Run-Time Interoperability Between Neuronal Network Simulators Based on the MUSIC Framework
Show others...
2010 (English)In: Neuroinformatics, ISSN 1539-2791, E-ISSN 1559-0089, Vol. 8, no 1, 43-60 p.Article in journal (Refereed) Published
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.

Keyword
MUSIC, Large-scale simulation, Computer simulation, Computational neuroscience, Neuronal network models, Inter-operability, MPI, Parallel processing
National Category
Bioinformatics (Computational Biology)
Identifiers
urn:nbn:se:kth:diva-10487 (URN)10.1007/s12021-010-9064-z (DOI)000276344300006 ()20195795 (PubMedID)2-s2.0-77953106373 (Scopus ID)
Funder
Swedish e‐Science Research Center
Note
Uppdaterad till artikel 20100709 QC 20100709Available from: 2009-05-18 Created: 2009-05-18 Last updated: 2017-12-13Bibliographically approved

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