Synapses is now the only class for defining synaptic interactions, it replaces Connections, STDP, etc.

Defining synaptic models

The most simple synapse (adding a fixed amount to the target membrane potential on every spike) is described as follows:

w = 1*mV
S = Synapses(P, Q, pre='v += w')

This defines a set of synapses between NeuronGroup P and NeuronGroup Q. If the target group is not specified, it is identical to the source group by default. The pre keyword defines what happens when a presynaptic spike arrives at a synapse. In this case, the constant w is added to variable v. Because v is not defined as a synaptic variable, it is assumed by default that it is a postsynaptic variable, defined in the target NeuronGroup Q. Note that this does not does create synapses (see Creating Synapses), only the synaptic models.

To define more complex models, models can be described as string equations, similar to the models specified in NeuronGroup:

S = Synapses(P, Q, model='w : volt', pre='v += w')

The above specifies a parameter w, i.e. a synapse-specific weight.

Synapses can also specify code that should be executed whenever a postsynaptic spike occurs (keyword post) and a fixed (pre-synaptic) delay for all synapses (keyword delay). See the reference documentation for Synapses for more details.

Model syntax

The model follows exactly the same syntax as for NeuronGroup. There can be parameters (e.g. synaptic variable w above), but there can also be named subexpressions and differential equations, describing the dynamics of synaptic variables. In all cases, synaptic variables are created, one value per synapse. Internally, these are stored as arrays. There are a few specificities:

  • A variable with the _post suffix is looked up in the postsynaptic (target) neuron. That is, v_post means variable v in the postsynaptic neuron.
  • A variable with the _pre suffix is looked up in the presynaptic (source) neuron.
  • A variable not defined as a synaptic variable is considered to be postsynaptic.
  • A variable not defined as a synaptic variable and not defined in the postsynaptic neuron is considered an external constant

For the integration of differential equations, one can use the same keywords as for NeuronGroup.

Event-driven updates

By default, differential equations are integrated in a clock-driven fashion, as for a NeuronGroup. This is potentially very time consuming, because all synapses are updated at every timestep. It is possible to ask Brian 2 to simulate differential equations in an event-driven fashion using the keyword (event-driven). A typical example is pre- and postsynaptic traces in STDP:

         dApre/dt=-Apre/taupre : 1 (event-driven)
         dApost/dt=-Apost/taupost : 1 (event-driven)'''

Here, Brian updates the value of Apre for a given synapse only when this synapse receives a spike, whether it is presynaptic or postsynaptic. More precisely, the variables are updated every time either the pre or post code is called for the synapse, so that the values are always up to date when these codes are executed.

Automatic event-driven updates are only possible for a subset of equations, in particular for one-dimensional linear equations. These equations must also be independent of the other ones, that is, a differential equation that is not event-driven cannot depend on an event-driven equation (since the values are not continuously updated). In other cases, the user can write event-driven code explicitly in the update codes (see below).

Pre and post codes

The pre code is executed at each synapse receiving a presynaptic spike. For example:


adds the value of synaptic variable w to postsynaptic variable v. As for the model equations, the _post (_pre) suffix indicates a postsynaptic (presynaptic) variable, and variables not found in the synaptic variables are considered postsynaptic by default. Internally, the code is executed for all synapses receiving presynaptic spikes during the current timestep. Therefore, the code should be understood as acting on arrays rather than single values. Any sort of code can be executed. For example, the following code defines stochastic synapses, with a synaptic weight w and transmission probability p:

S=Synapses(input,neurons,model="""w : 1
                              p : 1""",

The code means that w is added to v with probability p (note that, internally, rand() is transformed to a instruction that outputs an array of random numbers). The code may also include multiple lines.

As mentioned above, it is possible to write event-driven update code for the synaptic variables. For this, two special variables are provided: t is the current time when the code is executed, and lastupdate is the last time when the synapse was updated (either through pre or post code). An example is short-term plasticity (in fact this could be done automatically with the use of the (event-driven) keyword mentioned above):

           model='''x : 1
                    u : 1
                    w : 1''',

Summed variables

In many cases, the postsynaptic neuron has a variable that represents a sum of variables over all its synapses. This is called a “summed variable”. An example is nonlinear synapses (e.g. NMDA):

neurons = NeuronGroup(1, model="""dv/dt=(gtot-v)/(10*ms) : 1
                                  gtot : 1""")
           model='''dg/dt=-a*g+b*x*(1-g) : 1
                    gtot_post = g : 1  (summed)
                    dx/dt=-c*x : 1
                    w : 1 # synaptic weight

Here, each synapse has a conductance g with nonlinear dynamics. The neuron’s total conductance is gtot. The line stating gtot_post = g : 1  (summed) specifies the link between the two: gtot in the postsynaptic group is the summer over all variables g of the corresponding synapses. What happens during the simulation is that at each time step, presynaptic conductances are summed for each neuron and the result is copied to the variable gtot. Another example is gap junctions:

neurons = NeuronGroup(N, model='''dv/dt=(v0-v+Igap)/tau : 1
                                  Igap : 1''')
S=Synapses(neurons,model='''w:1 # gap junction conductance
                            Igap_post = w*(v_pre-v_post): 1 (summed)''')

Here, Igap is the total gap junction current received by the postsynaptic neuron.

Creating synapses

Creating a Synapses instance does not create synapses, it only specifies their dynamics. The following command creates a synapse between neuron i in the source group and neuron j in the target group:

S.connect(i, j)

It is possible to create several synapses for a given pair of neurons:

S.connect(i, j, n=3)

This is useful for example if one wants to have multiple synapses with different delays. Multiple synaptic connections can be created in a single statement:

S.connect([1, 2], [1, 2])
S.connect(numpy.arange(10), 1)

The first statement connects all neuron pairs. The second statement creates synapses between neurons 1 and 1, and between neurons 2 and 2. The third statement creates synapses between the first ten neurons in the source group and neuron 1 in the target group.

One can also create synapses using code:


The code is a boolean statement that should return True when a synapse must be created, where i is the presynaptic neuron index and j is the postsynaptic neuron index (special variables). Here the first statement creates one-to-one connections, the second statement creates connections with a ring structure (N is the number of neurons, assumed to defined elsewhere by the user). This way of creating synapses is generally preferred.

The string expressions can also refer to pre- or postsynaptic variables. This can be useful for example for spatial connectivity: assuming that the pre- and postsynaptic groups have parameters x and y, storing their location, the folloing statement connects all cells in a 250 um radius:

S.connect('sqrt((x_pre-x_post)**2 + (y_pre-y_post)**2) < 250*umeter')

Synapse creation can also be probabilistic by providing a p argument, providing the connection probability for each pair of synapses:

S.connect(True, p=0.1)

This connects all neuron pairs with a probability of 10%. Probabilities can also be given as expressions, for example to implement a connection probability that depends on distance:

S.connect('i != j',
          p='p_max*exp(-(x_pre-x_post)**2+(y_pre-y_post)**2) / (2*(125*umeter)**2)')

If this statement is applied to a Synapses object that connects a group to itself, it prevents self-connections (i != j) and connects cells with a probability that is modulated according to a 2-dimensional Gaussian of the distance between the cells.

If conditions for connecting neurons are combined with both the n (number of synapses to create) and the p (probability of a synapse) keywords, they are interpreted in the following way:

For every pair i, j:
if condition(i, j) is fulfilled:
Evaluate p(i, j)
If p(i, j) < uniform random number between 0 and 1:
Create n(i, j) synapses for (i, j)

Accessing synaptic variables

Synaptic variables can be accessed in a similar way as NeuronGroup variables. They can indexed with two indexes, corresponding to the indexes of pre and postsynaptic neurons, and optionally with a third index in the case of multiple synapses. Here are a few examples:

S.w[2, 5] = 1*nS
S.w[1, :] = 2*nS
S.w = 1*nS # all synapses assigned
w0 = S.w[2, 3, 1] # second synapse for connection 2->3
S.w[2, 3] = (1*nS, 2*nS)
S.w[group1, group2] = "(1+cos(i-j))*2*nS"
S.w[:, :] = 'rand()*nS'
S.w['abs(x_pre-x_post) < 250*umetre'] = 1*nS


There is a special synaptic variable that is automatically created: delay. It is the propagation delay from the presynaptic neuron to the synapse, i.e., the presynaptic delay. This is just a convenience syntax for accessing the delay stored in the presynaptic pathway: pre.delay. When there is a postsynaptic code (keyword post), this delay can be accessed as post.delay.

The delay variable(s) can be set and accessed in the same way as other synaptic varaibles.

Multiple pathways

It is possible to have multiple pathways with different update codes from the same presynaptic neuron group. This may be interesting in cases when different operations must be applied at different times for the same presynaptic spike. To do this, specify a dictionary of pathway names and codes:

pre={'pre_transmission': 'ge+=w',
     'pre_plasticity': '''w=clip(w+Apost,0,inf)

This creates two pathways with the given names (in fact, specifying pre=code is just a shorter syntax for pre={'pre': code}) through which the delay variables can be accessed. The following statement, for example, sets the delay of the synapse between the first neurons of the source and target groups in the pre_plasticity pathway:

S.pre_plasticity.delay[0,0] = 3*ms

Monitoring synaptic variables

A StateMonitor object can be used to monitor synaptic variables. For example, the following statement creates a monitor for variable w for the synapses 0 and 1:

M = StateMonitor(S,'w',record=[0,1])

Note that these are synapse indices, not neuron indices. These can be obtained via the indices attribute that can be indexed in the same way as synaptic state variables, for example:

s = S.indices[0, :]  # all synapses originating from neuron 0
s = S.indices['i != j']  # all synapses excluding autapses
s = S.indices['w > 0']  # all synapses with non-zero weights (at this time)

The recorded traces can then be accessed in the usual way, for example:

plot(M.t / ms, M[0].w / nS)