Last modified by adavison on 2022/10/04 13:55
From version 9.5
edited by adavison
on 2021/08/04 15:08
Change comment: There is no comment for this version
To version 9.2
edited by adavison
on 2021/08/04 14:35
Change comment: There is no comment for this version

Summary

Details

Page properties
Content
... ... @@ -38,30 +38,13 @@
38 38  (% class="wikigeneratedid" id="HStateprerequisites" %)
39 39  (% class="small" %)**State prerequisites**
40 40  
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.
41 +.
42 42  
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.
44 -
45 45  (% class="wikigeneratedid" id="HDescription2Cexplanation2Candpractice" %)
46 46  (% class="small" %)**Description, explanation, and practice**
47 47  
48 -PyNN is a tool for building models of nervous systems, and parts of nervous systems, at the level of individual neurons and synapses.
46 +.
49 49  
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 -
65 65  (% class="wikigeneratedid" id="HSummary28Inthistutorial2CyouhavelearnedtodoX202629" %)
66 66  (% class="small" %)**Summary (In this tutorial, you have learned to do X…)**
67 67