Combining spiking neuronal network model with presynaptic and astrocyte interface models
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1
Tampere University of Technology and BioMediTech, Department of Electronics and Communications Engineering, Finland
Astrocytes have gained an increased interest in neuroscience due to their ability to influence synaptic transmission through gliotransmitters. The effects of gliotransmitters are computationally modeled by various groups. However models integrating astrocytes and their effects in the network level are lacking. Here we introduce a simulation scheme of astrocyte control of single synapses extend that to its effects on neuronal network behavior. A version of Tsodyks-Markram presynaptic model is used as described by De Pittá et al. (2011) and astrocytic effects as described in the same paper. These effects are applied to spiking neuronal network INEX by Lenk (2011). The simulators are combined by modifying values of synaptic strengths (W) in the INEX model according to neurotransmitters released in presynaptic models attached to each synapse (see Figure 1). At an event of spike amount U calcium enters the presynaptic terminal and binds to vesicle sensors u. There is an amount of x neurotransmitter present in the presynapse at any given time. Amount of u*x resources are released. Glutamate affects the value U by modifying parameter alpha. Alpha describes the effect of presynaptic glutamate receptors to release probability. U is changed towards alpha depending on glutamate amounts released by astrocyte. INEX parameter W is used for initial U for each synapse and resources released (RR) as weight for spiking synapse. A linear relationship between neuro-transmitter amounts and their effect to axon hillock computation is assumed. Astrocytes release gliotransmitters according to presynaptic releases which they detect. Release of gliotransmitters follows very simplified calcium dynamics in astrocyte. As in many other models we assume that gliotransmission reduces the strength of the synapse. We simulated a small network with 100 neurons (80 excitatory and 20 inhibitory) with and without astrocyte effect on synapses. When astrocytes were present in the model every astrocyte was linked with single excitatory synapse. Inhibitory synapses had presynaptic dynamics but no astrocyte effect in both cases. Our results show that, in accordance with the theory, network with astrocytes and with high activity gets activity reduced according to astrocytic glutamate releases to single synapses. As astrocytic glutamate is taken up, the activity increases. As a result additional releases by astrocytes reduce the activity again. This leads to periodic bursting of network. In the network without astrocytes two types of behavior can be seen. Some neurons are spiking almost constantly while others follow in short bursts. These bursts are approximately 50% longer than bursts in the astrocytes including network. Thus our results show that astrocytes regulate network activity by regulating individual synapses. The astrocytic glutamate reduces activity and makes it more periodic by comparison. To conclude, we combined a spiking neuronal network model with presynaptic and astrocyte interface models and could observe reduction in the neuronal activity and periodic synchronous bursting when astrocytes were present.
Acknowledgements
This research has been supported by the 3DNeuroN project in the European Union’s Seventh Framework Programme, Future and Emerging Technologies, grant agreement no296590.
Keywords:
computational modeling,
Astrocytes,
presynapse model,
gliotransmitter,
neuronal network model
Conference:
Neuroinformatics 2014, Leiden, Netherlands, 25 Aug - 27 Aug, 2014.
Presentation Type:
Poster, not to be considered for oral presentation
Topic:
Computational neuroscience
Citation:
Räisänen
EA,
Lenk
K and
Hyttinen
JA
(2014). Combining spiking neuronal network model with presynaptic and astrocyte interface models.
Front. Neuroinform.
Conference Abstract:
Neuroinformatics 2014.
doi: 10.3389/conf.fninf.2014.18.00010
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Received:
03 Apr 2014;
Published Online:
04 Jun 2014.
*
Correspondence:
Prof. Jari A Hyttinen, Tampere University of Technology and BioMediTech, Department of Electronics and Communications Engineering, Tampere, Finland, jari.hyttinen@tuni.fi