Part 2: Populations of neurons¶
Introduction¶
In this section we look at creating and parameterising batches of
neurons
, and connecting them. When you have worked through this
material, you will know how to:
create populations of neurons with specific parameters
set model parameters before creation
define models with customised parameters
randomise parameters after creation
make random connections between populations
set up devices to start, stop and save data to file
reset simulations
For more information on the usage of PyNEST, please see the other sections of this primer:
More advanced examples can be found at Example
Networks, or
have a look at at the source directory of your NEST installation in the
subdirectory: pynest/examples/
.
Creating parameterised populations of nodes¶
In the previous section, we introduced the function
Create(model, n=1, params=None)
. Its mandatory argument is the model
name, which determines what type the nodes to be created should be. Its
two optional arguments are n
, which gives the number of nodes to be
created (default: 1) and params
, which is a dictionary giving the
parameters with which the nodes should be initialised. So the most basic
way of creating a batch of identically parameterised neurons is to
exploit the optional arguments of Create()
:
ndict = {"I_e": 200.0, "tau_m": 20.0}
neuronpop = nest.Create("iaf_psc_alpha", 100, params=ndict)
The variable neuronpop
is a NodeCollection representing all the ids of the created
neurons.
Parameterising the neurons at creation is more efficient than using
SetStatus()
after creation, so try to do this wherever possible.
We can also set the parameters of a neuron model before creation,
which allows us to define a simulation more concisely in many cases. If
many individual batches of neurons are to be produced, it is more
convenient to set the defaults of the model, so that all neurons created
from that model will automatically have the same parameters. The
defaults of a model can be queried with GetDefaults(model)
, and set
with SetDefaults(model, params)
, where params
is a dictionary
containing the desired parameter/value pairings. For example:
ndict = {"I_e": 200.0, "tau_m": 20.0}
nest.SetDefaults("iaf_psc_alpha", ndict)
neuronpop1 = nest.Create("iaf_psc_alpha", 100)
neuronpop2 = nest.Create("iaf_psc_alpha", 100)
neuronpop3 = nest.Create("iaf_psc_alpha", 100)
The three populations are now identically parameterised with the usual
model default values for all parameters except I_e and tau_m,
which have the values specified in the dictionary ndict
.
If batches of neurons should be of the same model but using different
parameters, it is handy to use CopyModel(existing, new, params=None)
to make a customised version of a neuron model with its own default
parameters. This function is an effective tool to help you write clearer
simulation scripts, as you can use the name of the model to indicate
what role it plays in the simulation. Set up your customised model in
two steps using SetDefaults()
:
edict = {"I_e": 200.0, "tau_m": 20.0}
nest.CopyModel("iaf_psc_alpha", "exc_iaf_psc_alpha")
nest.SetDefaults("exc_iaf_psc_alpha", edict)
or in one step:
idict = {"I_e": 300.0}
nest.CopyModel("iaf_psc_alpha", "inh_iaf_psc_alpha", params=idict)
Either way, the newly defined models can now be used to generate neuron
populations and will also be returned by the function Models()
.
epop1 = nest.Create("exc_iaf_psc_alpha", 100)
epop2 = nest.Create("exc_iaf_psc_alpha", 100)
ipop1 = nest.Create("inh_iaf_psc_alpha", 30)
ipop2 = nest.Create("inh_iaf_psc_alpha", 30)
It is also possible to create populations with an inhomogeneous set of parameters. You would typically create the complete set of parameters, depending on experimental constraints, and then create all the neurons in one go. To do this, supply a dictionary with lists the same length as the number of neurons (or synapses) created. The dictionary can also contain single values, which will then be applied to each node.
parameter_dict = {"I_e": [200.0, 150.0], "tau_m": 20.0, "V_m": [-77.0, -66.0]}
pop3 = nest.Create("iaf_psc_alpha", 2, params=parameter_dict)
print(pop3.get(["I_e", "tau_m", "V_m"]))
Setting parameters for populations of neurons¶
It is not always the case that we want to set the parameters directly when we are creating
the nodes. Or, we might not want to set the same parameter for all nodes
in the NodeCollection. A classic example of this is when some parameter should
be drawn from a random distribution. As previously stated, you can use a dictionary
of lists to set different values for each node, Create()
,
set()
and SetStatus()
all take this option. If you have a lot of nodes in your NodeCollection,
list comprehension is the way to go:
Vth=-55.
Vrest=-70.
dVms = {"V_m": [Vrest+(Vth-Vrest)*numpy.random.rand() for x in range(len(epop1))]}
epop1.set(dVms)
Another way to randomize the parameters is by using NEST’s random parameters and distributions. NEST has a number of these parameters which can be used to set the node parameters as well as connection parameters like probability, weights and delays. The parameters can be combined, and they can be used with some mathematical functions provided by NEST. Be aware that the complexity of your parameter might affect the performance of your network.
epop1.set({"V_m": Vrest + nest.random.uniform(0.0, Vth-Vrest)})
Note that we are being rather lax with random numbers here. Really we have to take more care with them, especially if we are using multiple threads or distributing over multiple machines. We will worry about this later.
Generating populations of neurons with deterministic connections¶
In the previous section two neurons were connected using synapse specifications. In this section we extend this example to two populations of ten neurons each.
import nest
pop1 = nest.Create("iaf_psc_alpha", 10)
pop1.set({"I_e": 376.0})
pop2 = nest.Create("iaf_psc_alpha", 10)
multimeter = nest.Create("multimeter", 10)
multimeter.set({"record_from":["V_m"]})
If no connectivity pattern is specified, the populations are connected
via the default rule, namely all_to_all
. Each neuron of pop1
is
connected to every neuron in pop2
, resulting in \(10^2\)
connections.
nest.Connect(pop1, pop2, syn_spec={"weight":20.0})
Alternatively, the neurons can be connected with the one_to_one
rule.
This means that the first neuron in pop1
is connected to the first
neuron in pop2
, the second to the second, etc., creating ten
connections in total.
nest.Connect(pop1, pop2, "one_to_one", syn_spec={"weight":20.0, "delay":1.0})
Finally, the multimeters are connected using the default rule
nest.Connect(multimeter, pop2)
Here we have just used very simple connection schemes. Connectivity
patterns requiring the specification of further parameters, such as
in-degree or connection probabilities, must be defined in a dictionary
containing the key rule
and the key for parameters associated to the
rule. Please see Connection management
for an illustrated guide to the usage of Connect()
, as well as the example below.
Connecting populations with random connections¶
As just mentioned, we often want to look at networks with a sparser connectivity than all-to-all. Here we introduce four connectivity patterns which generate random connections between two populations of neurons.
The connection rule fixed_indegree
allows us to create n
random
connections for each neuron in the target population post
to a
randomly selected neuron from the source population pre
. The
variables weight
and delay
can be left unspecified, in which
case the default weight and delay are used. Alternatively we can set
them in the syn_spec
, so each created connection has the same
weight and delay. Here is an example:
d = 1.0
Je = 2.0
Ke = 20
Ji = -4.0
Ki = 12
conn_dict_ex = {"rule": "fixed_indegree", "indegree": Ke}
conn_dict_in = {"rule": "fixed_indegree", "indegree": Ki}
syn_dict_ex = {"delay": d, "weight": Je}
syn_dict_in = {"delay": d, "weight": Ji}
nest.Connect(epop1, ipop1, conn_dict_ex, syn_dict_ex)
nest.Connect(ipop1, epop1, conn_dict_in, syn_dict_in)
Now each neuron in the target population ipop1
has Ke
incoming
random connections chosen from the source population epop1
with
weight Je
and delay d
, and each neuron in the target population
epop1
has Ki
incoming random connections chosen from the source
population ipop1
with weight Ji
and delay d
.
The connectivity rule fixed_outdegree
works in analogous fashion,
with n
connections (keyword outdegree) being randomly selected
from the target population post
for each neuron in the source
population pre
. For reasons of efficiency, particularly when
simulating in a distributed fashion, it is better to use
fixed_indegree
if possible.
Another connectivity pattern available is fixed_total_number
. Here
n
connections (keyword N
) are created by randomly drawing source
neurons from the populations pre
and target neurons from the
population post
.
When choosing the connectivity rule pairwise_bernoulli
connections
are generated by iterating through all possible source-target pairs and
creating each connection with the probability p
(keyword p
).
In addition to the rule specific parameters indegree, outdegree,
N
and p
, the conn_spec
can contain the keywords allow_autapses
and allow_multapses
(set to False
or True
) allowing or forbidding
self-connections and multiple connections between two neurons,
respectively.
Note that for all connectivity rules, it is perfectly legitimate to have
the same population simultaneously in the role of pre
and post
.
For more information on connecting neurons, please read the
documentation of the Connect()
function and consult the guide at
Connection management.
Specifying the behaviour of devices¶
All devices implement a basic timing capacity; the parameter start
(default 0) determines the beginning of the device’s activity and the
parameter stop
(default: \(∞\)) its end. These values are taken
relative to the value of origin
(default: 0). For example, the
following example creates a poisson_generator
which is only active
between 100 and 150ms:
pg = nest.Create("poisson_generator")
pg.set({"start": 100.0, "stop": 150.0})
This functionality is useful for setting up experimental protocols with stimuli that start and stop at particular times.
So far we have accessed the data recorded by devices directly, by
extracting the value of events. However, for larger or longer
simulations, we may prefer to write the data to file for later
analysis instead. All recording devices allow the specification of
where data is stored over the parameter record_to
, which is set to
the name of the recording backend to use. To dump recorded data to a
file, set /ascii
, to print to the screen, use /screen
and to
hold the data in memory, set /memory
, which is also the default
for all recording devices. The following code sets up a multimeter
to record data to a named file:
recdict = {"record_to" : "ascii", "label" : "epop_mp"}
mm1 = nest.Create("multimeter", params=recdict)
If no name for the file is specified using the label
parameter, NEST
will generate its own using the name of the device, and its id. If the
simulation is multithreaded or distributed, multiple files will be
created, one for each process and/or thread. For more information on how
to customise the behaviour and output format of recording devices,
please read the documentation for Recording devices.
Resetting simulations¶
It often occurs that we need to reset a simulation. For example, if you
are developing a script, then you may need to run it from the
ipython
console multiple times before you are happy with its
behaviour. In this case, it is useful to use the function
ResetKernel()
. This gets rid of all nodes you have created, any
customised models you created, and resets the internal clock to 0.
The other main use of resetting is when you need to run a simulation in a loop, for example to test different parameter settings. In this case there is typically no need to throw out the whole network and create and connect everything, it is enough to re-parameterise the network. A good strategy here is to create and connect your network outside the loop, and then carry out the parametrisation, simulation and data collection steps within the loop.
Command overview¶
These are the new functions we introduced for the examples in this section.
Getting and setting basic settings and parameters of NEST¶
nest.kernel_status
Obtain parameters of the simulation kernel. Returns:
Parameter dictionary if called without argument
Single parameter value if called with single parameter name
List of parameter values if called with list of parameter names
Models¶
GetDefaults(model)
Return a dictionary with the default parameters of the given
model
, specified by a string.SetDefaults(model, params)
Set the default parameters of the given
model
to the values specified in theparams
dictionary.CopyModel(existing, new, params=None)
Create a
new
model by copying anexisting
one. Default parameters can be given asparams
, or else are taken fromexisting
.
Simulation control¶
-
Reset the simulation kernel. This will destroy the network as well as all custom models created with
CopyModel()
. The parameters of built-in models are reset to their defaults. Calling this function is equivalent to restarting NEST.