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41	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.
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	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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43	43	(% class="wikigeneratedid" id="HDescription2Cexplanation2Candpractice" %)
44	44	(% class="small" %)**Description, explanation, and practice**
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	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.
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	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.
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	54	+At that point, the neuron produces an action potential, also called a spike, and the membrane voltage is reset.
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	56	+Let's start by writing a docstring, "Simple network model using PyNN".
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	58	+For now, we're going to use the NEST simulator to simulate this model, so we import the PyNN-for-NEST module.
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	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.
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	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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48	48	(% class="wikigeneratedid" id="HSummary28Inthistutorial2CyouhavelearnedtodoX202629" %)
49	49	(% class="small" %)**Summary (In this tutorial, you have learned to do X…)**
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