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{\title This is the readme for the model associated with the paper:}}\vieww31740\viewh29000\viewkind1\viewscale149
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\f0\fs24 \cf0 This is the readme for the model associated with the paper:\
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Ghanim Ullah\super *\nosupersub , John R Cressman Jr, Ernest Barreto, and Steven J Schiff.  (2009)\
"The Influence of Sodium and Potassium Dynamics on Excitability, Seizures, \
and the Stability of Persistent States: II. Network and Glia Dynamics". J. Computational Neuroscience, 26:171-183. \
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Abstract:\
In these companion papers, we study how the interrelated dynamics of sodium and potassium affect the excitability of neurons, the occurrence of seizures, and the stability of persistent states of activity. We seek to study these dynamics with respect to the following compartments: neurons, glia, and extracellular space. We are particularly interested in the slower time-scale dynamics that determine overall excitability, and set the stage for transient episodes of persistent oscillations, working memory, or seizures. In this second of two companion papers, we present an ionic current network model composed of populations of Hodgkin-Huxley type excitatory and inhibitory neurons embedded within extracellular space and glia, in order to investigate the role of micro-environmental ionic dynamics on the stability of persistent activity. We show that these networks reproduce seizure-like activity if glial cells fail to maintain the proper micro-environmental conditions surrounding neurons, and produce several experimentally testable predictions to better understand such dynamics. Our work suggests that the stability of persistent states to perturbation is set by glial activity, and that how the response to such perturbations decays or grows may be a critical factor in a variety of disparate transient phenomena such as working memory, burst firing in neonatal brain or spinal cord, up states, seizures, and perhaps spreading depression.\
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Model files provided by the authors.\
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Usage:\
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Programming language: Fortran 90.\
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\cf0 Download the program files from ModelDB and compile the main program file Network.f90 using a Fortran 90 compiler.  \
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Network.f90 couples 100 inhibitory neurons and 100 excitatory neurons where the membrane potential dynamics of these neurons is taken from Gutkin et al. model, 2001, J. Computational Neuroscience, 11, 121-134. \
The synaptic currents here are modified from that given in Gutkin et al., 2001 model. The model also includes dynamic potassium and sodium concentrations that build on the model from companion paper \
John R Cressman Jr, Ghanim Ullah, Jokubas Ziburkus, Steven J Schiff , and Ernest Barreto. (2009) "The Influence of Sodium and Potassium Dynamics on Excitability, Seizures, and the Stability of Persistent States: I. Single Neuron Dynamics". J. Computational Neuroscience, 26:159-170.\
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\cf0 The results from Network.f90 are stored into data files (see comments in Network.f90) that include activity and raster plots for two network types, the membrane potentials, extracellular potassium and intracellular sodium of excitatory and inhibitory neurons.\
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The data files containing the activity of the network are read into another program file called \'93Activity.f90\'94. Activity.f90 simply averages the activity of the network over the desired time window (50 or 100ms). \
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After the simulations for Network.f90 are complete, compile Activity.f90 with a Fortran 90 compiler and plot the result using data visualization package of your interest. This will produce graphs similar to Fig.5 in the paper.\
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\cf0 \super *\nosupersub Contact: \
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\cf0 212 Earth Engineering Science Building, \
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\cf0 The Pennsylvania State University, \
University Park, PA, 16802, USA \
Email: {\field{\*\fldinst{HYPERLINK "mailto:ghanim@psu.edu"}}{\fldrslt \cf2 \ul \ulc2 ghanim.phy@}}\cf2 \ul \ulc2 gmail.com\cf0 \ulnone \
Voice: (814) 865 6951\
Fax: (814) 865 6161\
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