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9	9
10	10	== Audience ==
11	11
12		-This tutorial is intended for people with at least a basic knowledge of neuroscience (high school level or above) and basic familiarity with the Python programming language. It should also be helpful for people who already have advanced knowledge of neuroscience and neural simulation, who simply wish to learn how to use PyNN, and how it differs from other simulation tools they know.
	12	+This tutorial is intended for people with at least a basic knowledge of neuroscience (high-school level or above) and basic familiarity with the Python programming language. It should also be helpful for people who already have advanced knowledge of neuroscience and neural simulation, who simply wish to learn how to use PyNN and how it differs from other simulation tools they know.
13	13
14	14	== Prerequisites ==
15	15
16		-To follow this tutorial, you need a basic knowledge of neuroscience (high-school level or greater), basic familiarity with the Python programming language, and either a computer with PyNN, NEST, NEURON and Brian 2 installed, or an EBRAINS account and basic familiarity with Jupyter notebooks. If you don't have these tools installed, see one of our previous tutorials which guide you through the installation.
	16	+To follow this tutorial, you need a basic knowledge of neuroscience (high-school level or greater), basic familiarity with the Python programming language, and either a computer with PyNN, NEST, NEURON, and Brian 2 installed or an EBRAINS account and basic familiarity with Jupyter notebooks. If you don't have these tools installed, see one of our previous tutorials which guide you through the installation.
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18	18	== Format ==
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66	66	**Screencast** - blank document in editor
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68	68
69		-In this video, you'll see my editor on the left, and on the right my terminal and my file browser. I'll be writing code in the editor, and then running my scripts in the terminal. You're welcome to follow along~-~--you can pause the video at any time if I'm going too fast~-~--or you can just watch.
	69	+In this video, you'll see my editor on the left and my terminal and my file browser on the right. I'll be writing code in the editor and then running my scripts in the terminal. You're welcome to follow along~-~--you can pause the video at any time if I'm going too fast~-~--or you can just watch.
70	70
71		-Let's start by writing a docstring, "Simple network model using PyNN".
	71	+Let's start by writing a docstring "Simple network model using PyNN".
72	72
73		-For now, we're going to use the NEST simulator to simulate this model, so we import the PyNN-for-NEST module.
	73	+For now, we're going to use the NEST simulator to simulate this model; so, we import the PyNN-for-NEST module.
74	74
75		-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.
	75	+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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77	77	(% class="box infomessage" %)
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95	95	(% style="color:#e74c3c" %)cell_type  = sim.IF_curr_exp(v_rest=-65, v_thresh=-55, v_reset=-65, t_refrac=1, tau_m=10, cm=1, i_offset=0.1)
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97	97
98		-Let's create 100 of these neurons, then we're going to record the membrane voltage, and run a simulation for 100 milliseconds.
	98	+Let's create 100 of these neurons; then, we're going to record the membrane voltage and run a simulation for 100 milliseconds.
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136	136	\\**Run script in terminal, show figure**
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139		-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.
	139	+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.
140	140
141		-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.
	141	+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.
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158	158	\\**Run script in terminal, show figure**
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161		-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.
	161	+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.
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190	190	\\**Run script in terminal, show figure**
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193		-Now if we run our simulation again, we can see the effect of this heterogeneity in the neuron population.
	193	+Now, if we run our simulation again, we can see the effect of this heterogeneity in the neuron population.
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195	195	(% class="box successmessage" %)
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197		-**Slide** showing addition of second population, and of connections between them
	197	+**Slide** showing addition of second population and of connections between them
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201		-So far we have a population of neurons, but there are no connections between them, we don't have a network. Let's add a second population of the same size as the first, but we'll set the offset current to zero, so they don't fire action potentials spontaneously.
	201	+So far, we have a population of neurons, but there are no connections between them, we don't have a network. Let's add a second population of the same size as the first, but we'll set the offset current to zero, so they don't fire action potentials spontaneously.
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213	213	**...**
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216		-Now we want to create synaptic connections between the neurons in Population 1 and those in Population 2. There are lots of different ways these could be connected.
	216	+Now, we want to create synaptic connections between the neurons in Population 1 and those in Population 2. There are lots of different ways these could be connected.
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257		-In PyNN, we call a group of connections between two populations a _Projection_. To create a Projection, we need to specify the presynaptic population, the postsynaptic population, the connection algorithm, and the synapse model. Here we're using the simplest synapse model available in PyNN, for which the synaptic weight is constant over time, there is no plasticity.
	257	+In PyNN, we call a group of connections between two populations a _Projection_. To create a Projection, we need to specify the presynaptic population, the postsynaptic population, the connection algorithm, and the synapse model. Here, we're using the simplest synapse model available in PyNN, for which the synaptic weight is constant over time; there is no plasticity.
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302		-and there we have it, our simple neuronal network of integrate-and-fire neurons, written in PyNN, simulated with NEST. If you prefer to use the NEURON simulator, PyNN makes this very simple, we import the PyNN-for-NEURON module instead.
	302	+and there we have it, our simple neuronal network of integrate-and-fire neurons, written in PyNN, simulated with NEST. If you prefer to use the NEURON simulator, PyNN makes this very simple: we import the PyNN-for-NEURON module instead.
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351	351	**Slide** recap of learning objectives
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353	353
354		-That is the end of this tutorial, in which I've demonstrated how to build a simple network using PyNN, and to simulate it using two different simulators, NEST and NEURON.
	354	+That is the end of this tutorial, in which I've demonstrated how to build a simple network using PyNN and to simulate it using two different simulators, NEST and NEURON.
355	355
356	356	Of course, PyNN allows you to create much more complex networks than this, with more realistic neuron models, synaptic plasticity, spatial structure, and so on. You can also use other simulators, such as Brian or SpiNNaker, and you can run simulations in parallel on clusters or supercomputers.
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363	363	)))
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366		-PyNN has been developed by many different people, with financial support from several different organisations. I'd like to mention in particular the CNRS and the European Commission, through the FACETS, BrainScaleS and Human Brain Project grants.
	366	+PyNN has been developed by many different people, with financial support from several organisations. I'd like to mention in particular the CNRS and the European Commission, through the FACETS, BrainScaleS, and Human Brain Project grants.
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369		-For more information visit neuralensemble.org/PyNN. If you have questions you can contact us through the PyNN Github project, the NeuralEnsemble forum, EBRAINS support, or the EBRAINS Community.
	369	+For more information, visit neuralensemble.org/PyNN. If you have questions you can contact us through the PyNN Github project, the NeuralEnsemble forum, EBRAINS support, or the EBRAINS Community.