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45	45	(% class="wikigeneratedid" id="HDescription2Cexplanation2Candpractice" %)
46	46	(% class="small" %)**Description, explanation, and practice**
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48		-.
	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.
	63	+
	64	+This is where we set the parameters of the model: the resting membrane potential is -65 millivolts, the spike threshold is -55 millivolts, the reset voltage after a spike is again -65 millivolts, the refractory period after a spike is one millisecond, the membrane time constant is 10 milliseconds, and the membrane capacitance is 1 nanofarad. We're also going to inject a constant bias current of 0.1 nanoamps into these neurons, so that we get some action potentials.
	65	+
	66	+Let's create 100 of these neurons, then we're going to record the membrane voltage, and run a simulation for 100 milliseconds.
	67	+
	68	+PyNN has some built-in tools for making simple plots, so let's import those, and plot the membrane voltage of the zeroth neuron in our population (remember Python starts counting at zero).
	69	+
	70	+As you'd expect, the bias current causes the membrane voltage to increase until it reaches threshold~-~--it doesn't increase in a straight line because it's a //leaky// integrate-and-fire neuron~-~--then once it hits the threshold the voltage is reset, and then stays at the same level for a short time~-~--this is the refractory period~-~--before it starts to increase again.
	71	+
	72	+Now, all 100 neurons in our population are identical, so if we plotted the first neuron, the second neuron, ..., we'd get the same trace.
	73	+
	74	+Let's change that. In nature every neuron is a little bit different, so let's set the resting membrane potential and the spike threshold randomly from a Gaussian distribution, and let's plot membrane voltage from _all_ the  neurons.
	75	+
	76	+Now if we run our simulation again, we can see the effect of this heterogeneity in the neuron population.
	77	+
	78	+TO BE COMPLETED
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50	50	(% class="wikigeneratedid" id="HSummary28Inthistutorial2CyouhavelearnedtodoX202629" %)
51	51	(% class="small" %)**Summary (In this tutorial, you have learned to do X…)**
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