Warning

This is A PREVIEW for NEST 3.0 and NOT an OFFICIAL RELEASE! Some functionality may not be available and information may be incomplete!

# Connection Management¶

Connections between populations of neurons and between neurons and devices for stimulation and recording in NEST are created with the Connect() function. Although each connection is internally represented individually, you can use a single call to Connect() to create many connections at the same time. In the simplest case, the function just takes two NodeCollections containing the source and target nodes that will be connected in an all-to-all fashion.

Each call to the function will establish the connectivity between pre- and post-synaptic populations according to a certain pattern or rule. The desired pattern is specified by simply stating the rule name as third argument to Connect(), or by setting the key rule in the connectivity specification dictionary conn_spec alongside additional rule-specific parameters. All available patterns are described in the section Connection Rules below.

To specify the properties for the individual connections, a synapse specification syn_spec can be given to Connect(). This can also just be the name of the synapse model to be used, an object defining collocated synapses, or a dictionary, with optional parameters for the connections.

The syn_spec is given as the fourth argument to the Connect() function. Parameters like the synaptic weight or delay can be either set values or drawn and combined flexibly from random distributions. More information about synapse models and their parameters can be found in the section Synapse Specification.

The Connect() function can be called in any of the following ways:

Connect(pre, post)
Connect(pre, post, conn_spec)
Connect(pre, post, conn_spec, syn_spec)


pre and post are NodeCollections, defining the nodes of origin (sources) and termination (targets) for the connections to be established.

If conn_spec is not specified, the default connection rule all_to_all will be used. When using a connection specification dictionary containing the rule name and rule-specific parameters, the additional switch allow_autapses (default: True) can be set to allow or disallow self-connections. Likewise, allow_multapses (default: True) can be used to specify if multiple connections between the same pair of neurons are allowed or not.

Note

The switches allow_autapses and allow_multapses are only effective during each single call to Connect(). Calling the function multiple times with the same set of neurons might still lead to violations of these constraints, even though the switches were set to False in each individual call.

The synapse specification syn_spec defaults to the synapse model static_synapse. By using the keyword variant (Connect(pre, post, syn_spec=syn_spec_dict)), conn_spec can be omitted in the call to Connect() and will just take on the default value.

After your connections are established, a quick sanity check is to look up the number of connections in the network, which can be easily done using GetKernelStatus():

print(nest.GetKernelStatus('num_connections'))


Have a look at the Inspecting Connections section further down to get more tips on how to examine the connections in greater detail.

## Connection Rules¶

Connection rules are specified using the conn_spec parameter of Connect(), which can be either just a string naming a connection rule, or a dictionary containing a rule specification. Only connection rules requiring no parameters can be given as strings, for all other rules, a dictionary specifying the rule and its parameters is required. Examples for such parameters might be in- and out-degrees, or the probability for establishing a connection. A description of all connection rules available in NEST is available below.

### all-to-all¶

Each node in A is connected to every node in B. Since all_to_all is the default, the rule doesn’t actually have to be specified.

n, m = 10, 12
A = nest.Create('iaf_psc_alpha', n)
B = nest.Create('iaf_psc_alpha', m)
nest.Connect(A, B, 'all_to_all')
nest.Connect(A, B)  # equivalent


### conngen¶

Availability

This connection rule is only available if NEST was compiled with support for libneurosim.

To allow the generation of connectivity by means of an external library, NEST supports the Connection Generator Interface 1. For more details on this interface, see the Git repository of libneurosim.

In contrast to the other rules for creating connections, this rule relies on a Connection Generator object to describe the connectivity pattern in a library-specific way. The Connection Generator is handed to Connect() under the key cg of the connection specification dictionary and evaluated internally. If the Connection Generator provides values for connection weights and delays, their respective indices can be specified under the key params_map. Alternatively, all synapse parameters can be specified using the synapse specification argument to Connect()

The following listing shows an example for using the Connection-Set Algebra in NEST via the Connection Generator Interface and randomly connects 10% of the neurons from A to the neurons in B, each connection having a weight of 10000.0 pA and a delay of 1.0 ms:

A = nest.Create('iaf_psc_alpha', 100)
B = nest.Create('iaf_psc_alpha', 100)

# Create the Connection Generator object
import csa
cg = csa.cset(csa.random(0.1), 10000.0, 1.0)

# Map weight and delay indices to vaules from cg
params_map = {'weight': 0, 'delay': 1}

conn_spec_dict = {'rule': 'conngen', 'cg': cg, 'params_map': params_map}
nest.Connect(A, B, conn_spec_dict)


### fixed indegree¶

The nodes in A are randomly connected with the nodes in B such that each node in B has a fixed indegree of N.

n, m, N = 10, 12, 2
A = nest.Create('iaf_psc_alpha', n)
B = nest.Create('iaf_psc_alpha', m)
conn_spec_dict = {'rule': 'fixed_indegree', 'indegree': N}
nest.Connect(A, B, conn_spec_dict)


### fixed outdegree¶

The nodes in A are randomly connected with the nodes in B such that each node in A has a fixed outdegree of N.

n, m, N = 10, 12, 2
A = nest.Create('iaf_psc_alpha', n)
B = nest.Create('iaf_psc_alpha', m)
conn_spec_dict = {'rule': 'fixed_outdegree', 'outdegree': N}
nest.Connect(A, B, conn_spec_dict)


### fixed total number¶

The nodes in A are randomly connected with the nodes in B such that the total number of connections equals N.

n, m, N = 10, 12, 30
A = nest.Create('iaf_psc_alpha', n)
B = nest.Create('iaf_psc_alpha', m)
conn_spec_dict = {'rule': 'fixed_total_number', 'N': N}
nest.Connect(A, B, conn_spec_dict)


### one-to-one¶

The ith node in A is connected to the ith node in B. The NodeCollections of A and B have to contain the same number of nodes.

n = 10
A = nest.Create('iaf_psc_alpha', n)
B = nest.Create('spike_recorder', n)
nest.Connect(A, B, 'one_to_one')


### pairwise bernoulli¶

For each possible pair of nodes from A and B, a connection is created with probability p.

n, m, p = 10, 12, 0.2
A = nest.Create('iaf_psc_alpha', n)
B = nest.Create('iaf_psc_alpha', m)
conn_spec_dict = {'rule': 'pairwise_bernoulli', 'p': p}
nest.Connect(A, B, conn_spec_dict)


### symmetric pairwise bernoulli¶

For each possible pair of nodes from A and B, a connection is created with probability p from A to B, as well as a connection from B to A (two connections in total). To use this rule, allow_autapses must be False, and make_symmetric must be True.

n, m, p = 10, 12, 0.2
A = nest.Create('iaf_psc_alpha', n)
B = nest.Create('iaf_psc_alpha', m)
conn_spec_dict = {'rule': 'symmetric_pairwise_bernoulli', 'p': p,
'allow_autapses': False, 'make_symmetric': True}
nest.Connect(A, B, conn_spec_dict)


## Synapse Specification¶

The synapse properties can be given as just the name of the desired synapse model as a string, as a dictionary specifying synapse model and parameters, or as a CollocatedSynapse object creating multiple synapses for each source-target pair as detailed in the section on collocated synapses.

n = 10
A = nest.Create('iaf_psc_alpha', n)
B = nest.Create('iaf_psc_alpha', n)
nest.Connect(A, B, syn_spec='static_synapse')

syn_spec_dict = {'synapse_model': 'stdp_synapse',
'weight': 2.5, 'delay': 0.5}
nest.Connect(A, B, syn_spec=syn_spec_dict)


If synapse properties are given as a dictionary, it may include the keys synapse_model (default static_synapse), weight (default 1.0), delay (default 1.0), and receptor_type (default 0, see Receptor Types for details), as well as parameters specific to the chosen synapse model. The specification of all parameters is optional, and unspecified parameters will take on the default values of the chosen synapse model that can be inspected using nest.GetDefaults(synapse_model).

Parameters can be either fixed scalar values, arrays of values, or expressions.

One synapse dictionary can contain an arbitrary combination of parameter types, as long as they are supported by the chosen connection rule.

### Scalar parameters¶

Scalar parameters must be given with the correct type. The weight for instance must be a float, while the receptor_type has to be of type integer. When a synapse parameter is given as a scalar, the value will be applied to all connections created in the current Connect() call.

n = 10
neuron_dict = {'tau_syn': [0.3, 1.5]}
A = nest.Create('iaf_psc_exp_multisynapse', n, neuron_dict)
B = nest.Create('iaf_psc_exp_multisynapse', n, neuron_dict)
syn_spec_dict ={'synapse_model': 'static_synapse', 'weight': 2.5, 'delay': 0.5, 'receptor_type': 1}
nest.Connect(A, B, syn_spec=syn_spec_dict)


### Array parameters¶

Array parameters can be used with the rules all_to_all, fixed_indegree, fixed_outdegree, fixed_total_number and one_to_one. The arrays can be specified as NumPy arrays or Python lists. As with the scalar parameters, all parameters have to be specified as arrays of the correct type.

#### all-to-all¶

When connecting with rule all_to_all, the array parameter must have dimension len(B) x len(A).

A = nest.Create('iaf_psc_alpha', 3)
B = nest.Create('iaf_psc_alpha', 2)
syn_spec_dict = {'weight': [[1.2, -3.5, 2.5], [0.4, -0.2, 0.7]]}
nest.Connect(A, B, syn_spec=syn_spec_dict)


#### fixed indegree¶

For rule fixed_indegree the array has to be a two-dimensional NumPy array or Python list with shape (len(B), indegree), where indegree is the number of incoming connections per target neuron. This means that the rows describe the target, while the columns represent the connections converging on the target neuron, regardless of the identity of the source neurons.

A = nest.Create('iaf_psc_alpha', 5)
B = nest.Create('iaf_psc_alpha', 3)
conn_spec_dict = {'rule': 'fixed_indegree', 'indegree': 2}
syn_spec_dict = {'weight': [[1.2, -3.5],[0.4, -0.2],[0.6, 2.2]]}
nest.Connect(A, B, conn_spec_dict, syn_spec_dict)


#### fixed outdegree¶

For rule fixed_outdegree the array has to be a two-dimensional NumPy array or Python list with shape (len(pre), outdegree), where outdegree is the number of outgoing connections per source neuron. This means that the rows describe the source, while the columns represent the connections starting from the source neuron regardless of the identity of the target neuron.

A = nest.Create('iaf_psc_alpha', 2)
B = nest.Create('iaf_psc_alpha', 5)
conn_spec_dict = {'rule': 'fixed_outdegree', 'outdegree': 3}
syn_spec_dict = {'weight': [[1.2, -3.5, 0.4], [-0.2, 0.6, 2.2]]}
nest.Connect(A, B, conn_spec_dict, syn_spec_dict)


#### fixed total number¶

For rule fixed_total_number, the array has to be same the length as the number of connections N.

A = nest.Create('iaf_psc_alpha', 3)
B = nest.Create('iaf_psc_alpha', 4)
conn_spec_dict = {'rule': 'fixed_total_number', 'N': 4}
syn_spec_dict = {'weight': [1.2, -3.5, 0.4, -0.2]}
nest.Connect(A, B, conn_spec_dict, syn_spec_dict)


#### one-to-one¶

For rule one_to_one the array must have the same length as there are nodes in A and B.

A = nest.Create('iaf_psc_alpha', 2)
B = nest.Create('spike_recorder', 2)
conn_spec_dict = {'rule': 'one_to_one'}
syn_spec_dict = {'weight': [1.2, -3.5]}
nest.Connect(A, B, conn_spec_dict, syn_spec_dict)


### Expressions as parameters¶

nest.Parameter objects support a flexible specification of synapse parameters through expressions. This includes parameters drawn from random distributions and depending on spatial properties of source and target neurons. Parameters can be combined through mathematical expressions including conditionals, providing for a high degree of flexibility.

The following parameters and functionalities are provided:

• Random parameters

• Spatial parameters

• Spatially distributed parameters

• Mathematical functions

• Clipping, redrawing, and conditional parameters

For more information, check out the guide on parametrization or the documentation on the different PyNEST APIs.

n = 10
A = nest.Create('iaf_psc_alpha', n)
B = nest.Create('iaf_psc_alpha', n)
syn_spec_dict = {
'synapse_model': 'stdp_synapse',
'weight': 2.5,
'delay': nest.random.uniform(min=0.8, max=2.5),
'alpha': nest.math.redraw(nest.random.normal(mean=5.0, std=1.0), min=0.5, max=10000.)
}
nest.Connect(A, B, syn_spec=syn_spec_dict)


In this example, the default connection rule all_to_all is used and connections will be using synapse model stdp_synapse. All synapses are created with a static weight of 2.5 and a delay that is uniformly distributed in [0.8, 2.5]. The parameter alpha is drawn from a normal distribution with mean 5.0 and standard deviation 1.0; values below 0.5 and above 10000 are excluded by re-drawing if they should occur. Thus, the actual distribution is a slightly distorted Gaussian.

If the synapse type is supposed to have a unique name and still use distributed parameters, it needs to be defined in two steps:

n = 10
A = nest.Create('iaf_psc_alpha', n)
B = nest.Create('iaf_psc_alpha', n)
nest.CopyModel('stdp_synapse', 'excitatory', {'weight':2.5})
syn_dict = {
'synapse_model': 'excitatory',
'weight': 2.5,
'delay': nest.random.uniform(min=0.8, max=2.5),
'alpha': nest.math.redraw(nest.random.normal(mean=5.0, std=1.0), min=0.5, max=10000.)
}
nest.Connect(A, B, syn_spec=syn_dict)


For further information on the available distributions see Random numbers in NEST.

### Collocated synapses¶

Some modeling applications require multiple connections between the same pairs of nodes. An example of this could be a network, where each pre-synaptic neuron connects with a static synapse to a modulatory receptor on the post-synaptic neuron and with a plastic synapse to a normal NMDA-type receptor.

This type of connectivity is especially hard to realize when using randomized connection rules, as the chosen pairs that are actually connected are only known internally, and have to be retrieved manually after the call to Connect() returns.

To ease the setup of such connectivity patterns, NEST supports a concept called collocated synapses. This allows the creation of several connections between chosen pairs of neurons (possibly with different synapse types or parameters) in a single call to nest.Connect().

To create collocated synapses, the synapse specification consists of an object of type CollocatedSynapses, whose constructor takes synapse specification dictionaries as arguments and applies the given dictionaries to each source-target pair internally.

nodes = nest.Create('iaf_psc_alpha', 3)
syn_spec = nest.CollocatedSynapses({'weight': 4.0, 'delay': 1.5},
{'synapse_model': 'stdp_synapse'},
{'synapse_model': 'stdp_synapse', 'alpha': 3.0})
nest.Connect(nodes, nodes, conn_spec='one_to_one', syn_spec=syn_spec)
print(nest.GetConnections().alpha)


The example above will create 9 connections in total because there are 3 neurons times 3 synapse specifications in the CollocatedSynapses object, and the connection rule one_to_one is used.

>>> print(nest.GetKernelStatus('num_connections'))
9


In more detail, the connections have the following properties:

• 3 are of type static_synapse with weight 4.0 and delay 1.5

• 3 are of type stdp_synapse with the default value for alpha

• 3 are of type stdp_synapse with an alpha of 3.0.

If you want to connect with different receptor types, you can do the following:

A = nest.Create('iaf_psc_exp_multisynapse', 7)
B = nest.Create('iaf_psc_exp_multisynapse', 7, {'tau_syn': [0.1 + i for i in range(7)]})
syn_spec_dict = nest.CollocatedSynapses({'weight': 5.0, 'receptor_type': 2},
{'weight': 1.5, 'receptor_type': 7})
nest.Connect(A, B, 'one_to_one', syn_spec_dict)
print(nest.GetConnections().get())


You can see how many synapse parameters you have by calling len() on your CollocatedSynapses object:

>>> len(syn_spec)
2


## Spatially-structured networks¶

Nodes in NEST can be created so that they have a position in two- or three-dimensional space. To take full advantage of the arrangement of nodes, connection parameters can be based on the nodes’ positions or their spatial relation to each other. See Spatially-structured networks for the full information about how to create and connect such networks.

## Connecting sparse matrices with array indexing¶

Oftentimes, you will find yourself in a situation, where you want to base your connectivity on actual data instead of rules. A common scenario is that you have a (sometimes sparse) connection matrix coming from an experiment or from a graph algorithm. Let’s assume you have a weight matrix of the form:

$\begin{split}W = \begin{bmatrix} w_{11} & w_{21} & \cdots & w_{n1} \\ w_{12} & w_{22} & \cdots & w_{n2} \\ \vdots & \vdots & \ddots & \vdots \\ w_{1m} & w_{2m} & \cdots & w_{nm} \\ \end{bmatrix}\end{split}$

where $$w_{ij}$$ is the weight of the connection with pre-synaptic node $$i$$ and post-synaptic node $$j$$. In all generality, we can assume that some weights are zero, indicating that there is no connection at all.

As there is no support for creating connections from the whole matrix directly, we will instead just iterate the pre-synaptic neurons and connect one column at a time. We assume that there are $$n$$ pre-synaptic nodes in the NodeCollection A and $$m$$ post-synaptic nodes in B. We also assume that we have our weight matrix given as a two-dimensional NumPy array W, with $$n$$ columns and $$m$$ rows.

W = np.array([[0.5,  0.0, 1.5],
[1.3,  0.2, 0.0],
[0.0, 1.25, 1.3]])

A = nest.Create('iaf_psc_alpha', 3)
B = nest.Create('iaf_psc_alpha', 3)

for i, pre in enumerate(A):
# Extract the weights column.
weights = W[:, i]

# To only connect pairs with a nonzero weight, we use array
# indexing to extract the weights and post-synaptic neurons.
nonzero_indices = numpy.where(weights != 0)[0]
weights = weights[nonzero_indices]
post = B[nonzero_indices]

# Generate an array of node IDs for the column of the weight
# matrix, with length based on the number of nonzero
# elements. The array's dtype must be an integer.
pre_array = numpy.ones(len(nonzero_indices), dtype=numpy.int64) * pre.get('global_id')

# nest.Connect() automatically converts post to a NumPy array
# because pre_array contains multiple identical node IDs. When
# also specifying a one_to_one connection rule, the arrays of
# node IDs can then be connected.
nest.Connect(pre_array, post, conn_spec='one_to_one', syn_spec={'weight': weights})


## Receptor Types¶

Conceptually, each connection in NEST terminates at a receptor on the target node. The exact meaning of such a receptor depends on the concrete type of that node. In a multi-compartment neuron, for instance, the different compartments could be addressed as different receptors, while another neuron model might make sets of different synaptic parameters available for each receptor. Please refer to the model documentation for details.

In order to connect a pre-synaptic node to a certain receptor on a post-synaptic node, the integer ID of the target receptor can be supplied under the key receptor_type in the syn_spec dictionary during the call to Connect(). If unspecified, the receptor will take on its default value of 0. If you request a receptor that is not available in the target node, this will result in a runtime error.

To illustrate the concept of receptors in more detail, the following example shows how to connect several iaf_psc_alpha neurons to the different compartments of a multi-compartment integrate-and-fire neuron (iaf_cond_alpha_mc) that are represented by different receptors.

A1 = nest.Create('iaf_psc_alpha')
A2 = nest.Create('iaf_psc_alpha')
A3 = nest.Create('iaf_psc_alpha')
A4 = nest.Create('iaf_psc_alpha')
B = nest.Create('iaf_cond_alpha_mc')

receptors = nest.GetDefaults('iaf_cond_alpha_mc')['receptor_types']
print(receptors)
{'soma_exc': 1,
'soma_inh': 2,
'soma_curr': 7,
'proximal_exc': 3
'proximal_inh': 4,
'proximal_curr': 8,
'distal_exc': 5,
'distal_inh': 6,
'distal_curr': 9,}

nest.Connect(A1, B, syn_spec={'receptor_type': receptors['distal_inh']})
nest.Connect(A2, B, syn_spec={'receptor_type': receptors['proximal_inh']})
nest.Connect(A3, B, syn_spec={'receptor_type': receptors['proximal_exc']})
nest.Connect(A4, B, syn_spec={'receptor_type': receptors['soma_inh']})


In the example above, we retrieve a map of available receptors and their IDs by extracting the receptor_types property from the model defaults. This functionality is, however, only available for models with a predefined number of receptors, while models with a variable number of receptors usually don’t provide such an enumeration.

An example for the latter are the *_multisynapse neuron models that support multiple individual synaptic time constants for the different receptors. In these models, the number of available receptors is not predefined, but determined only by the length of the tau_syn vector that is supplied to the model instance. The following example shows the setup and connection of such a model in more detail:

A = nest.Create('iaf_psc_alpha')
B = nest.Create('iaf_psc_exp_multisynapse', params={'tau_syn': [0.1, 0.2, 0.3]})

print(B.n_synapses)   # This will print 3, as we set 3 different tau_syns

nest.Connect(A, B, syn_spec={'receptor_type': 2})


## Synapse Types¶

NEST provides a number of built-in synapse models that can be used during connection setup. The default model is the static_synapse, whose only parameters weight and delay do not change over time. Other synapse types model effects like learning and adaptation in the form of long-term or short-term plasticity. A list of available synapse models is accessible via the command nest.Models('synapses'). A list of available synapse models and more detailed information about each of them can be found in the model directory.

Note

Not all nodes can be connected via all available synapse types. The events a synapse type is able to transmit is documented in the Transmits section of the model documentation.

All synapses store their parameters on a per-connection basis. However, each of the built-in models is registered with the simulation kernel in a number of different ways that slightly modify the available properties of the connections instantiated from the model. The different variants are indicated by specific suffixes:

_lbl

denotes labeled synapses that have an additional parameter synapse_label (type: int), which can be set to a user-defined value. In a common application this label is used to store an additional projection identifier. Please note that using this synapse variant may drive up the memory requirements of your simulations significantly, as the label is stored on a per-synapse basis.

_hpc

denotes synapses for high-performance computing scenarios, which have minimal memory requirements by using thread-local target node indices internally. Use this version if you are running very large simulations.

_hom

denotes homogeneous synapses that store certain parameters like weight and delay only once for all synapses of the same type and can thus be used to save memory.

The default parameter values of a synapse model can be inspected using the command nest.GetDefaults(), which only takes the name of the synapse model as an argument and returns a dictionary. Likewise, the function nest.SetDefaults() takes the name of a synapse type and a parameter dictionary as arguments and will modify the defaults of the given model.

print(nest.GetDefaults('static_synapse'))

{'delay': 1.0,
'has_delay': True,
'num_connections': 0,
'receptor_type': 0,
'requires_symmetric': False,
'sizeof': 32,
'synapse_model': 'static_synapse',
'weight': 1.0,
'weight_recorder': ()}

nest.SetDefaults('static_synapse', {'weight': 2.5})


To further customize the process of creating synapses, it is often useful to have the same basic synapse model available with different parametizations. To this end, nest.CopyModel() can be used to create custom synapse types from already existing synapse types. In the simplest case, it takes the names of the existing model and the copied type to be created. The optional argument params allows to directly customize the new type during the copy operation. If omitted, the defaults of the copied model are taken.

nest.CopyModel('static_synapse', 'inhibitory', {'weight': -2.5})
nest.Connect(A, B, syn_spec='inhibitory')


## Inspecting Connections¶

In order to assert that the instantiated network model actually looks like what was intended, it is oftentimes useful to inspect the connections in the network. For this, NEST provides the function

nest.GetConnections(source=None, target=None, synapse_model=None, synapse_label=None)


This function returns a SynapseCollection object that contains the identifiers for connections that match the given filters. source and target are given as NodeCollections, synapse_model is the name of the model as a string and synapse_label is an integer identifier. Any combination of these parameters is permitted. If nest.GetConnections() is called without parameters it returns all connections in the network.

Internally, each connection in the SynapseCollection is represented by the following five entries: source node ID, target node ID, thread ID of the target, numeric synapse ID, and port.

The result of nest.GetConnections() can be further processed by giving it as an argument to nest.GetStatus(), or, better yet, by using the get() function on the SynapseCollection directly. Both ways will yield a dictionary with the parameters of the connections that match the filter criterions given to nest.GetConnections():

A = nest.Create('iaf_psc_alpha', 2)
B = nest.Create('iaf_psc_alpha')
nest.Connect(A, B)
conn = nest.GetConnections()
print(conn.get())

{'delay': [1.0, 1.0],
'port': [0, 1],
'receptor': [0, 0],
'sizeof': [32, 32],
'source': [1, 2],
'synapse_id': [18, 18],
'synapse_model': ['static_synapse', 'static_synapse'],
'target': [3, 3],
'weight': [1.0, 1.0]}


The get() function of a SynapseCollection can optionally also take a string or list of strings to only retrieve specific parameters. This is useful if you do not want to inspect the entire synapse dictionary:

>>>  conn.get('weight')
[1.0, 1.0]

>>>  conn.get(['source', 'target'])
{'source': [1, 2], 'target': [3, 3]}


Another way of retrieving specific parameters is by getting them directly from the SynapseCollection using the dot-notation:

>>>  conn.delay
[1.0, 1.0]


For spatially distributed networks, you can access the distance between the source-target pairs by querying distance on your SynapseCollection.

>>>  spatial_conn.distance
(0.47140452079103173,
0.33333333333333337,
0.4714045207910317,
0.33333333333333337,
3.925231146709438e-17,
0.33333333333333326,
0.4714045207910317,
0.33333333333333326,
0.47140452079103157)


You can further examine the SynapseCollection by checking the length of it or by printing it to the terminal. The printout will be in the form of a table that lists source and target node IDs, synapse model, weight and delay:

>>>  len(conn)
2
>>>  print(conn)
source   target   synapse model   weight   delay
-------- -------- --------------- -------- -------
1        3  static_synapse    1.000   1.000
2        3  static_synapse    1.000   1.000


A SynapseCollection can be indexed or sliced, if you only want to inspect a subset of the connections contained in it:

>>>  print(conn[0:2:2])
source   target   synapse model   weight   delay
-------- -------- --------------- -------- -------
1        3  static_synapse    1.000   1.000


Last, but not least, SynapseCollection can be iterated, to retrieve one connection at a time:

>>>  for c in conn:
>>>      print(c.source)
1
2


## Modifying Existing Connections¶

To modify the parameters of an existing connection, you first have to obtain handles to them using nest.GetConnections(). These handles can then be given as arguments to the nest.SetStatus() function, or by using the set() function on the SynapseCollection directly:

n1 = nest.Create('iaf_psc_alpha', 2)
n2 = nest.Create('iaf_psc_alpha', 2)
nest.Connect(n1, n2)

conn = nest.GetConnections()
conn.set(weight=2.0)

print(conn.get())

{'delay': [1.0, 1.0, 1.0, 1.0],
'port': [0, 1, 2, 3],
'receptor': [0, 0, 0, 0],
'sizeof': [32, 32, 32, 32],
'source': [1, 1, 2, 2],
'synapse_id': [18, 18, 18, 18],
'synapse_model': ['static_synapse', 'static_synapse', 'static_synapse', 'static_synapse'],
'target': [3, 4, 3, 4],
'weight': [2.0, 2.0, 2.0, 2.0]}


To update a single parameter of a connection or a set of connections, you can call the set() function of the SynapseCollection with the keyword argument parameter_name. The value for this argument can be a single value, a list, or a nest.Parameter. If a single value is given, the value is set on all connections. If you use a list to set the parameter, the list needs to be the same length as there are connections in the SynapseCollection.

>>>  conn.set(weight=[4.0, 4.5, 5.0, 5.5])


Similar to how you retrieve several parameters at once with the get() function explained above, you can also set multiple parameters at once using set(parameter_dictionary). Again, the values of the dictionary can be a single value, a list, or a nest.Parameter.

>>>  conn.set({'weight': [1.5, 2.0, 2.5, 3.0], 'delay': 2.0})


Finally, you can also directly set parameters on a SynapseCollection using the dot-notation:

>>>  conn.weight = 5.
>>>  conn.weight
[5.0, 5.0, 5.0, 5.0]
>>>  conn.delay = [5.1, 5.2, 5.3, 5.4]
>>>  conn.delay
[5.1, 5.2, 5.3, 5.4]


Note that some parameters like source and target are read-only and cannot be set. The documentation of a specific synapse model will point out which parameters can be set and which are read-only.

## References¶

1

Djurfeldt M, Davison AP and Eppler JM (2014). Efficient generation of connectivity in neuronal networks from simulator-independent descriptions. Front. Neuroinform. https://doi.org/10.3389/fninf.2014.00043