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- Hjorth, Johannes, et al.
(författare)
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The influence of stuttering properties for firing activity in pairs of electrically coupled striatal fast-spiking interneurons
- 2009
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Ingår i: Neuroinformatics 2009. Pilsen, Czech Republic, September 06 - 08, 2009. - : Frontiers Media SA.
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- 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.
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| 2. |
- Klaus, A., et al.
(författare)
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Synchronization effects between striatal fast-spiking interneurons forming networks with different topologies
- 2008
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Ingår i: Frontiers in Neuroinformatics. - : Frontiers Media SA. - 1662-5196.
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- The basal ganglia are involved in executive functions of the forebrain, such as the planning and selection of motor behavior. In the striatum, which is the input stage of the basal ganglia system, fast-spiking interneurons provide an effective feedforward inhibition to the medium-sized spiny projection neurons. Thus, these fast-spiking neurons are able to control the striatal output to later stages in the basal ganglia. Recently, in modeling studies it has been shown that pairs of cells as well as randomly connected networks of electrically coupled fast-spiking cells are able to synchronize their activity. Here we want to investigate the influence of network topology and network size on the synchronization in a simulated network of striatal fast-spiking interneurons. We use a biophysically detailed single-cell model of the fast-spiking interneuron with 127 compartments (Hellgren Kotaleski et al., J Neurophysiology, 95: 331-41, 2006; Hjorth et al., Neurocomputing 70: 1887–1891, 2007), and parallelize the network model of electrically coupled fast-spiking cells using PGENESIS running on a Blue Gene/L supercomputer. General network statistics and synaptic input is constrained by published data from the striatum. Network topology is varied from ’regular’ over ’small-world’ to ’random’ (Watts & Strogatz, Nature 393: 440–442, 1998). Using common statistical measures, we will determine the extent of local and global synchronization for each network topology. Furthermore, we investigate the interactions in the network by means of Ising models (Schneidman et al., Nature 440: 1007–1012, 2006). We are particularly interested in the relation between the ’interaction’ – as obtained by the Ising model – and the underlying network topology; e. g., do directly coupled fast-spiking interneuron pairs synchronize most?So far, the small amount of fast-spiking cells in the striatum (less than 2 %) makes experimental studies on the network level difficult or even impossible. With our study we hope to gain a better understanding of interaction effects in the feedforward inhibitory network of the striatum.
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