Evaluating dendritic impact using complex and reduced models of medium spiny neurons
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1
KTH - Royal Institute of Technology, School of Computer Science and Communication, Sweden
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2
University of Edinburgh, School of Informatics, United Kingdom
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3
Karolinska Institute, Department of Neuroscience, Sweden
Current advances in both experimental and theoretical fields have found that synaptic signals are not simply relayed passively to the soma or the axon; instead, dendrites, the main structure to receive synaptic inputs, can act as "computing units", performing arithmetic operations by themselves.
However, to model neurons with active dendrites will lead to dramatically increased computing costs. In contrast, simple point-like artificial neuron models do not capture the full dynamics of individual neurons as they do not take into account dendritic computation. This lost accuracy, on the other hand, might play an important role in the overall dynamics of neural networks.
To bridge this gap between the point-neuron models and very complex neuron models and to better understand how dendritic computation might affect signal integration at more macroscopic levels, we recently developed a biophysically detailed model of medium spiny neuron (MSN) in dorsal striatum with 634 compartments. An early version of this model has been confirmed to reproduce experimental findings [Evans et al. (2012)].
We derived a series of simplified versions of the model with a reduced number of compartments but conserved 3-dimensional morphology. With the complex model and its reduced offsprings, we explore the importance of dendritic morphology and synaptic topology on the input-output relationship of MSNs.
For this purpose, we adopt a novel method by [Chen et al. (2011)], which combines metric space analysis and multidimensional scaling analysis, to quantify the impact of the dendrites. We also apply this method, as well as select techniques from information theory to verify the reduced models' behaviour.
References
1. Evans, R.C.; Morera-Herreras, T.; Cui, Y.; Du, K.; Sheehan, T.; Hellgren Kotaleski, J.; Venance, L.; Blackwell, K.T. (2012). The effects of NMDA subunit composition on calcium influx and spike timing-dependent plasticity in striatal medium spiny neurons. PLoS Computational Biology 8(4)
2. Chen, J.-Y. (2010). A Simulation Study Investigating the Impact of Dendritic Morphology and Synaptic Topology on Neuronal Firing Patterns. Neural Computation 22
Keywords:
Dendrite,
Information Theory,
compartmental models,
Medium Spiny Neuron,
spike train analysis,
metric space,
multidimensional scaling
Conference:
Neuroinformatics 2013, Stockholm, Sweden, 27 Aug - 29 Aug, 2013.
Presentation Type:
Poster
Topic:
Computational neuroscience
Citation:
Lindroos
R,
Pieczkowski
J,
Du
K and
Hellgren Kotaleski
J
(2013). Evaluating dendritic impact using complex and reduced models of medium spiny neurons.
Front. Neuroinform.
Conference Abstract:
Neuroinformatics 2013.
doi: 10.3389/conf.fninf.2013.09.00118
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Received:
08 Apr 2013;
Published Online:
11 Jul 2013.
*
Correspondence:
Mr. Jan Pieczkowski, KTH - Royal Institute of Technology, School of Computer Science and Communication, Stockholm, Sweden, janpi@kth.se