Changes for page 03. Building and simulating a simple model

Last modified by adavison on 2022/10/04 13:55

From 10.1 to 10.2 From 16.1 to 17.1

From version 10.2

edited by adavison
on 2021/08/04 16:19

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To version 16.1

edited by annedevismes
on 2021/10/18 10:26

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--XWiki.adavison
++XWiki.annedevismes

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@@ -9,11 +9,11 @@
  == 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.
++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.
++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 ==
@@ -66,13 +66,13 @@
  **Screencast** - blank document in editor
  )))
--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.
++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.
--Let's start by writing a docstring, "Simple network model using PyNN".
++Let's start by writing a docstring "Simple network model using PyNN".
--For now, we're going to use the NEST simulator to simulate this model, so we import the PyNN-for-NEST module.
++For now, we're going to use the NEST simulator to simulate this model; so, we import the PyNN-for-NEST module.
--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.
++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.
  (% class="box infomessage" %)
  (((
@@ -95,7 +95,7 @@
  (% 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)
  )))
--Let's create 100 of these neurons, then we're going to record the membrane voltage, and run a simulation for 100 milliseconds.
++Let's create 100 of these neurons; then, we're going to record the membrane voltage and run a simulation for 100 milliseconds.
  (% class="box infomessage" %)
  (((
@@ -106,37 +106,264 @@
  (% 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)(%%)
  (% style="color:#e74c3c" %)population1 = sim.Population(100, cell_type, label="Population 1")
  population1.record("v")
--sim.run(100.0)
++sim.run(100.0)(%%)
++\\**Run script in terminal**
  )))
  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).
--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.
++(% class="box infomessage" %)
++(((
++**Screencast** - current state of editor
++\\(% style="color:#000000" %)"""Simple network model using PyNN"""
++\\import pyNN.nest as sim(%%)
++(% style="color:#e74c3c" %)from pyNN.utility.plotting import Figure, Panel(%%)
++(% style="color:#000000" %)sim.setup(timestep=0.1)(%%)
++(% 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)(%%)
++(% style="color:#000000" %)population1 = sim.Population(100, cell_type, label="Population 1")
++population1.record("v")
++sim.run(100.0)(%%)
++(% style="color:#e74c3c" %)data_v = population1.get_data().segments[0].filter(name='v')[0]
++Figure(
++    Panel(
++        data_v[:, 0],
++        xticks=True, xlabel="Time (ms)",
++        yticks=True, ylabel="Membrane potential (mV)"
++    ),
++    title="Response of neuron #0",
++    annotations="Simulated with NEST"
++).show()(%%)
++\\**Run script in terminal, show figure**
++)))
--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.
++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.
--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.
++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.
--Now if we run our simulation again, we can see the effect of this heterogeneity in the neuron population.
++(% class="box infomessage" %)
++(((
++**Screencast** - changes in editor
++
--TO BE COMPLETED
++**...**
++(% style="color:#000000" %)Figure(
++    Panel(
++        data_v[:, (% style="color:#e74c3c" %)0:5(% style="color:#000000" %)],
++        xticks=True, xlabel="Time (ms)",
++        yticks=True, ylabel="Membrane potential (mV)"
++    ),
++    title="Response of (% style="color:#e74c3c" %)first five neurons(% style="color:#000000" %)",
++    annotations="Simulated with NEST"
++).show()(%%)
++\\**Run script in terminal, show figure**
++)))
--(% class="wikigeneratedid" id="HSummary28Inthistutorial2CyouhavelearnedtodoX202629" %)
--(% class="small" %)**Summary (In this tutorial, you have learned to do X…)**
++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.
--.
++(% class="box infomessage" %)
++(((
++**Screencast** - changes in editor
++\\(% style="color:#000000" %)"""Simple network model using PyNN"""
++\\import pyNN.nest as sim(%%)
++(% style="color:#000000" %)from pyNN.utility.plotting import Figure, Panel(%%)
++(% style="color:#e74c3c" %)from pyNN.random import RandomDistribution(%%)
++(% style="color:#000000" %)sim.setup(timestep=0.1)(%%)
++(% style="color:#000000" %)cell_type  = sim.IF_curr_exp(
++   (% style="color:#e74c3c" %) v_rest=RandomDistribution('normal', {'mu': -65.0, 'sigma': 1.0}),
++    v_thresh=RandomDistribution('normal', {'mu': -55.0, 'sigma': 1.0}),
++    v_reset=RandomDistribution('normal', {'mu': -65.0, 'sigma': 1.0}), (%%)
++(% style="color:#000000" %)    t_refrac=1, tau_m=10, cm=1, i_offset=0.1)(%%)
++
--(% class="wikigeneratedid" id="HAcknowledgementsifappropriate" %)
--(% class="small" %)**Acknowledgements if appropriate**
++**...**
++
--.
++(% style="color:#000000" %)Figure(
++    Panel(
++        data_v[:, 0:5],
++        xticks=True, xlabel="Time (ms)",
++        yticks=True, ylabel="Membrane potential (mV)"
++    ),
++    title="Response of first five neurons (% style="color:#e74c3c" %)with heterogeneous parameters(% style="color:#000000" %)",
++    annotations="Simulated with NEST"
++).show()(%%)
++\\**Run script in terminal, show figure**
++)))
--(% class="wikigeneratedid" id="HReferencestowebsites28Formoreinformation2Cvisitusat202629" %)
--(% class="small" %)**References to websites (For more information, visit us at…)**
++Now, if we run our simulation again, we can see the effect of this heterogeneity in the neuron population.
--.
++(% class="box successmessage" %)
++(((
++**Slide** showing addition of second population and of connections between them
++)))
--(% class="wikigeneratedid" id="HContactinformation28Forquestions2Ccontactusat202629" %)
--(% class="small" %)**Contact information (For questions, contact us at…)**
++(% class="wikigeneratedid" %)
++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.
--.
++(% class="box infomessage" %)
++(((
++**Screencast** - changes in editor
++\\**...**
++(% style="color:#000000" %)population1 = sim.Population(100, cell_type, label="Population 1")(%%)
++(% style="color:#e74c3c" %)population2 = sim.Population(100, cell_type, label="Population 2")
++population2.set(i_offset=0)(%%)
++(% style="color:#000000" %)population1.record("v")(%%)
++(% style="color:#e74c3c" %)population2.record("v")(%%)
++(% style="color:#000000" %)sim.run(100.0)(%%)
++**...**
++)))
++
++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.
++
++(% class="box successmessage" %)
++(((
++**Slide** showing all-to-all connections
++)))
++
++We could connect all neurons in Population 1 to all those in Population 2.
++
++(% class="box successmessage" %)
++(((
++**Slide** showing random connections
++)))
++
++We could connect the populations randomly, in several different ways.
++
++(% class="box successmessage" %)
++(((
++**Slide** showing distance-dependent connections
++)))
++
++(% class="wikigeneratedid" %)
++We could connect the populations randomly, but with a probability of connection that depends on the distance between the neurons.
++
++(% class="box successmessage" %)
++(((
++**Slide** showing explicit lists of connections
++)))
++
++(% class="wikigeneratedid" %)
++Or we could connect the neurons in a very specific manner, based on an explicit list of connections.
++
++(% class="wikigeneratedid" %)
++Just as PyNN provides a variety of neuron models, so it comes with a range of connection algorithms built in. You can also add your own connection methods.
++
++(% class="box successmessage" %)
++(((
++**Slide** showing addition of second population, and of connections between them, labelled as a Projection.
++)))
++
++(% class="wikigeneratedid" %)
++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.
++
++(% class="box infomessage" %)
++(((
++**Screencast** - changes in editor
++
++
++**...**
++(% style="color:#000000" %)population2.record("v")(%%)
++(% style="color:#e74c3c" %)connection_algorithm = sim.FixedProbabilityConnector(p=0.5)
++synapse_type = sim.StaticSynapse(weight=0.5, delay=0.5)
++connections = sim.Projection(population1, population2, connection_algorithm, synapse_type)(%%)
++(% style="color:#000000" %)sim.run(100.0)(%%)
++**...**
++)))
++
++(% class="wikigeneratedid" %)
++Finally, let's update our figure, by adding a second panel to show the responses of Population 2.
++
++(% class="box infomessage" %)
++(((
++**Screencast** - changes in editor
++\\**...**
++(% style="color:#000000" %)sim.run(100.0)(%%)
++(% style="color:#e74c3c" %)data1_v(% style="color:#000000" %) = population1.get_data().segments[0].filter(name='v')[0](%%)
++(% style="color:#e74c3c" %)data2_v = population2.get_data().segments[0].filter(name='v')[0](%%)
++(% style="color:#000000" %)Figure(
++    Panel(
++        (% style="color:#e74c3c" %)data1_v(% style="color:#000000" %)[:, 0:5],
++        xticks=True, (% style="color:#e74c3c" %)--xlabel="Time (ms)",--(%%)
++(% style="color:#000000" %)        yticks=True, ylabel="Membrane potential (mV)"
++    ),
++    (% style="color:#e74c3c" %)Panel(
++        data2_v[:, 0:5],
++        xticks=True, xlabel="Time (ms)",
++        yticks=True"
++    ),(%%)
++(% style="color:#000000" %)    title="Response of (% style="color:#e74c3c" %)simple network(% style="color:#000000" %)",
++    annotations="Simulated with NEST"
++).show()
++
++**Run script in terminal, show figure**
++)))
++
++(% class="wikigeneratedid" %)
++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.
++
++(% class="box infomessage" %)
++(((
++**Screencast** - final state of editor
++\\(% style="color:#000000" %)"""Simple network model using PyNN"""
++\\import pyNN.(% style="color:#e74c3c" %)neuron(% style="color:#000000" %) as sim(%%)
++(% style="color:#000000" %)from pyNN.utility.plotting import Figure, Panel(%%)
++(% style="color:#000000" %)from pyNN.random import RandomDistribution(%%)
++(% style="color:#000000" %)sim.setup(timestep=0.1)(%%)
++(% style="color:#000000" %)cell_type  = sim.IF_curr_exp(
++   (% style="color:#e74c3c" %) (% style="color:#000000" %)v_rest=RandomDistribution('normal', {'mu': -65.0, 'sigma': 1.0}),
++    v_thresh=RandomDistribution('normal', {'mu': -55.0, 'sigma': 1.0}),
++    v_reset=RandomDistribution('normal', {'mu': -65.0, 'sigma': 1.0}), (%%)
++(% style="color:#000000" %)    t_refrac=1, tau_m=10, cm=1, i_offset=0.1)(%%)
++(% style="color:#000000" %)population1 = sim.Population(100, cell_type, label="Population 1")(%%)
++(% style="color:#000000" %)population2 = sim.Population(100, cell_type, label="Population 2")
++population2.set(i_offset=0)
++population1.record("v")
++population2.record("v")(%%)
++(% style="color:#000000" %)connection_algorithm = sim.FixedProbabilityConnector(p=0.5)
++synapse_type = sim.StaticSynapse(weight=0.5, delay=0.5)
++connections = sim.Projection(population1, population2, connection_algorithm, synapse_type)(%%)
++(% style="color:#000000" %)sim.run(100.0)(%%)
++(% style="color:#000000" %)data1_v = population1.get_data().segments[0].filter(name='v')[0]
++data2_v = population2.get_data().segments[0].filter(name='v')[0]
++Figure(
++    Panel(
++        data1_v[:, 0:5],
++        xticks=True,
++        yticks=True, ylabel="Membrane potential (mV)"
++    ),
++    Panel(
++        data2_v[:, 0:5],
++        xticks=True, xlabel="Time (ms)",
++        yticks=True"
++    ),(%%)
++(% style="color:#000000" %)    title="Response of simple network",
++    annotations="Simulated with (% style="color:#e74c3c" %)NEURON(% style="color:#000000" %)"
++).show()
++
++**Run script in terminal, show figure**
++)))
++
++(% class="wikigeneratedid" %)
++As you would hope, NEST and NEURON give essentially identical results.
++
++(% class="box successmessage" %)
++(((
++**Slide** recap of learning objectives
++)))
++
++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.
++
++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.
++
++We will be releasing a series of tutorials, throughout the rest of 2021 and 2022, to introduce these more advanced features of PyNN, so keep an eye on the EBRAINS website.
++
++(% class="box successmessage" %)
++(((
++**Slide** acknowledgements, contact information
++)))
++
++(% class="wikigeneratedid" %)
++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.
++
++(% class="wikigeneratedid" %)
++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.

Changes for page 03. Building and simulating a simple model

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PyNN: resources