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Optimal Balance of the Striatal Medium Spiny Neuron Network

Overview of attention for article published in PLoS Computational Biology, April 2013
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Title
Optimal Balance of the Striatal Medium Spiny Neuron Network
Published in
PLoS Computational Biology, April 2013
DOI 10.1371/journal.pcbi.1002954
Pubmed ID
Authors

Adam Ponzi, Jeffery R. Wickens

Abstract

Slowly varying activity in the striatum, the main Basal Ganglia input structure, is important for the learning and execution of movement sequences. Striatal medium spiny neurons (MSNs) form cell assemblies whose population firing rates vary coherently on slow behaviourally relevant timescales. It has been shown that such activity emerges in a model of a local MSN network but only at realistic connectivities of 10 ~ 20% and only when MSN generated inhibitory post-synaptic potentials (IPSPs) are realistically sized. Here we suggest a reason for this. We investigate how MSN network generated population activity interacts with temporally varying cortical driving activity, as would occur in a behavioural task. We find that at unrealistically high connectivity a stable winners-take-all type regime is found where network activity separates into fixed stimulus dependent regularly firing and quiescent components. In this regime only a small number of population firing rate components interact with cortical stimulus variations. Around 15% connectivity a transition to a more dynamically active regime occurs where all cells constantly switch between activity and quiescence. In this low connectivity regime, MSN population components wander randomly and here too are independent of variations in cortical driving. Only in the transition regime do weak changes in cortical driving interact with many population components so that sequential cell assemblies are reproducibly activated for many hundreds of milliseconds after stimulus onset and peri-stimulus time histograms display strong stimulus and temporal specificity. We show that, remarkably, this activity is maximized at striatally realistic connectivities and IPSP sizes. Thus, we suggest the local MSN network has optimal characteristics - it is neither too stable to respond in a dynamically complex temporally extended way to cortical variations, nor is it too unstable to respond in a consistent repeatable way. Rather, it is optimized to generate stimulus dependent activity patterns for long periods after variations in cortical excitation.

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Geographical breakdown

Country Count As %
Germany 3 3%
Chile 1 1%
France 1 1%
Austria 1 1%
Sweden 1 1%
United Kingdom 1 1%
United States 1 1%
Unknown 89 91%

Demographic breakdown

Readers by professional status Count As %
Researcher 28 29%
Student > Ph. D. Student 27 28%
Student > Master 10 10%
Professor > Associate Professor 7 7%
Student > Bachelor 3 3%
Other 9 9%
Unknown 14 14%
Readers by discipline Count As %
Neuroscience 25 26%
Agricultural and Biological Sciences 20 20%
Medicine and Dentistry 7 7%
Computer Science 5 5%
Psychology 5 5%
Other 14 14%
Unknown 22 22%