03. Building and simulating a simple model

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tutorial under development

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== Learning objectives ==

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In this tutorial, you will learn 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.

== Audience ==

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.

== Prerequisites ==

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.

== Format ==

This tutorial will be a video combining slides, animations, and screencast elements. The intended duration is 10 minutes.

== Script ==

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**Slide** showing tutorial title, PyNN logo, link to PyNN service page.

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Hello, my name is X.

This video is one of a series of tutorials for PyNN, which is Python software for modelling and simulating spiking neural networks.

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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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**Slide** listing learning objectives

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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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**Slide** listing prerequisites

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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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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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**Slide** showing animation of leaky integrate-and-fire model

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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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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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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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At that point, the neuron produces an action potential, also called a spike, and the membrane voltage is reset.

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**Screencast** - blank document in editor

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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.

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Let's start by writing a docstring, "Simple network model using PyNN".

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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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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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**Screencast** - current state of editor

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\\(% style="color:#e74c3c" %)"""Simple network model using PyNN"""

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\\import pyNN.nest as sim

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sim.setup(timestep=0.1)

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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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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.

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**Screencast** - current state of editor

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\\(% style="color:#000000" %)"""Simple network model using PyNN"""

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\\import pyNN.nest as sim

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sim.setup(timestep=0.1)(%%)

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(% 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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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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**Screencast** - current state of editor

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\\(% style="color:#000000" %)"""Simple network model using PyNN"""

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\\import pyNN.nest as sim

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sim.setup(timestep=0.1)(%%)

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(% style="color:#000000" %)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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(% style="color:#e74c3c" %)population1 = sim.Population(100, cell_type, label="Population 1")

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population1.record("v")

sim.run(100.0)

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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).

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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.

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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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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.

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Now if we run our simulation again, we can see the effect of this heterogeneity in the neuron population.

TO BE COMPLETED

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(% class="small" %)**Summary (In this tutorial, you have learned to do X…)**

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