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22	22	== Script ==
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24		-=== Introduce yourself (if video) ===
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	25	+(% class="small" %)**Introduce yourself**
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26	26	Hello, my name is X.
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30	30	For a list of the other tutorials in this series, you can visit ebrains.eu/service/pynn, that's p-y-n-n.
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32		-=== State the learning objectives (In this tutorial, you will learn to do X…) ===
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	34	+(% class="small" %)**State the learning objectives**
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34		-In this tutorial, you will learn the basics of PyNN:
	36	+In this tutorial, you will learn the basics of PyNN: how to build a simple network of integrate-and-fire neurons using PyNN, how to run simulation experiments with this network using different simulators, and how to visualize the data generated by these experiments.
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36		-=== State prerequisites ===
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	39	+(% class="small" %)**State prerequisites**
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38		-.
	41	+To follow this tutorial, you need a basic knowledge of neuroscience (high-school level or greater), basic familiarity with the Python programming language, and you should have already followed our earlier tutorial video which guides you through the installation process.
39	39
40		-=== Description, explanation, and practice ===
	43	+This video covers PyNN 0.10. If you've installed a more recent version of PyNN, you might want to look for an updated version of this video.
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42		-.
	45	+(% class="wikigeneratedid" id="HDescription2Cexplanation2Candpractice" %)
	46	+(% class="small" %)**Description, explanation, and practice**
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44		-=== Summary (In this tutorial, you have learned to do X…) ===
	48	+PyNN is a tool for building models of nervous systems, and parts of nervous systems, at the level of individual neurons and synapses.
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	50	+We'll start off creating a group of 100 neurons, using a really simple model of a neuron, the leaky integrate-and-fire model.
	51	+
	52	+When we inject positive current into this model, either from an electrode or from an excitatory synapse, it increases the voltage across the cell membrane, until the voltage reaches a certain threshold.
	53	+
	54	+At that point, the neuron produces an action potential, also called a spike, and the membrane voltage is reset.
	55	+
	56	+Let's start by writing a docstring, "Simple network model using PyNN".
	57	+
	58	+For now, we're going to use the NEST simulator to simulate this model, so we import the PyNN-for-NEST module.
	59	+
	60	+Like with any numerical model, we need to break time down into small steps, so let's set that up with steps of 0.1 milliseconds.
	61	+
	62	+PyNN comes with a selection of integrate-and-fire models. We're going to use the IF_curr_exp model, where "IF" is for integrate-and-fire, "curr" means that synaptic responses are changes in current, and "exp" means that the shape of the current is a decaying exponential function.
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	64	+
	65	+(% class="wikigeneratedid" id="HSummary28Inthistutorial2CyouhavelearnedtodoX202629" %)
	66	+(% class="small" %)**Summary (In this tutorial, you have learned to do X…)**
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48		-=== Acknowledgements if appropriate ===
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	71	+(% class="small" %)**Acknowledgements if appropriate**
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50	50	.
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52		-=== References to websites (For more information, visit us at…) ===
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	76	+(% class="small" %)**References to websites (For more information, visit us at…)**
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56		-=== Contact information (For questions, contact us at…) ===
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	81	+(% class="small" %)**Contact information (For questions, contact us at…)**
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58	58	.