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, on_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 on_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 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', on_pre='v += w')

The above specifies a parameter w, i.e. a synapse-specific weight. Note that to avoid confusion, synaptic variables cannot have the same name as a pre- or post-synaptic variables.

Synapses can also specify code that should be executed whenever a postsynaptic spike occurs (keyword on_post) and a fixed (pre-synaptic) delay for all synapses (keyword delay).

As shown above, variable names that are not referring to a synaptic variable are automatically understood to be post-synaptic variables. To explicitly specify that a variable should be from a pre- or post-synaptic neuron, append the suffix _pre or _post. An alternative but equivalent formulation of the on_pre statement above would therefore be v_post += w.

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.

Brian also automatically defines a number of synaptic variables that can be used in equations, on_pre and on_post statements, as well as when assigning to other synaptic variables:


The index of the pre-synaptic source of a synapse.


The index of the post-synaptic target of a synapse.


The total number of synapses.


The total number of synapses connected to the post-synaptic target of a synapse.


The total number of synapses outgoing from the pre-synaptic source of a synapse.


The last time this synapse has applied an on_pre or on_post statement. There is normally no need to refer to this variable explicitly, it is used to implement Event-driven updates (see below). It is only defined when event-driven equations are used.

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 and Brian will therefore emit a warning. If you are sure about integrating the equations at every timestep (e.g. because you want to record the values continuously), then you should specify the flag (clock-driven), which will silence the warning. To ask Brian 2 to simulate differential equations in an event-driven fashion use the flag (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 on_pre or on_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 on_pre code is executed at each synapse receiving a presynaptic spike. For example:


adds the value of synaptic variable w to postsynaptic variable v. 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(neuron_input,neurons,model="""w : 1
                              p : 1""",

The code means that w is added to v with probability p. The code may also include multiple lines.

Similarly, the on_post code is executed at each synapse where the postsynaptic neuron has fired a spike.

Creating synapses

Creating a Synapses instance does not create synapses, it only specifies their dynamics. The following command creates a synapse between neuron 5 in the source group and neuron 10 in the target group:

S.connect(i=5, j=10)

Multiple synaptic connections can be created in a single statement:

S.connect(i=[1, 2], j=[3, 4])
S.connect(i=numpy.arange(10), j=1)

The first statement connects all neuron pairs. The second statement creates synapses between neurons 1 and 3, and between neurons 2 and 4. 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 by giving (as a string) the condition for a pair of neurons i and j to be connected by a synapse, e.g. you could connect neurons that are not very far apart with:


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 user-defined parameters x and y, storing their location, the following statement connects all cells in a 250 um radius:

S.connect(condition='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:


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(condition='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 computed from the user-defined parameters x and y, storing their location.


You can specify a mapping from i to any function f(i), e.g. the simplest way to give a 1-to-1 connection would be:


This mapping can also use a restricting condition with if, e.g. to connect neurons 0, 2, 4, 6, … to neurons 0, 1, 2, 3, … you could write:

S.connect(j='int(i/2) if i % 2 == 0')

The connections above describe the target indices j as a function of the source indices i. You can also apply the syntax in the other direction, i.e. describe source indices i as a function of target indices j. For a 1-to-1 connection, this does not change anything in most cases:


Note that there is a subtle difference between the two descriptions if the two groups do not have the same size: if the source group has fewer neurons than the target group, then using j='i' is possible (there is a target neuron for each source neuron), but i='j' would raise an error; the opposite is true if the source group is bigger than the target group.

The second example from above (neurons 0, 2, 4, … to neurons 0, 1, 2, …) can be adapted for the other direction, as well, and is possibly more intuitive in this case:


Accessing synaptic variables

Synaptic variables can be accessed in a similar way as NeuronGroup variables. They can be indexed with two indexes, corresponding to the indexes of pre and postsynaptic neurons, or with string expressions (referring to i and j as the pre-/post-synaptic indices, or to other state variables of the synapse or the connected neurons). Note that setting a synaptic variable always refers to the synapses that currently exist, i.e. you have to set them after the relevant Synapses.connect call.

Here are a few examples:

S.w[2, 5] = 1*nS
S.w[1, :] = 2*nS
S.w = 1*nS # all synapses assigned
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

Assignments can also refer to pre-defined variables, e.g. to normalize synaptic weights. For example, after the following assignment the sum of weights of all synapses that a neuron receives is identical to 1, regardless of the number of synapses it receives:

syn.w = '1.0/N_incoming'

Note that it is also possible to index synaptic variables with a single index (integer, slice, or array), but in this case synaptic indices have to be provided.

The N_incoming and N_outgoing variables give access to the total number of incoming/outgoing synapses for a neuron, but this access is given for each synapse. This is necessary to apply it to individual synapses as in the statement to normalize synaptic weights mentioned above. To access these values per neuron instead, N_incoming_post and N_outgoing_pre can be used. Note that synaptic equations or on_pre/on_post statements should always refer to N_incoming and N_outgoing without pre/post suffix.

Here’s a little example illustrating the use of these variables:

>>> group1 = NeuronGroup(3, '')
>>> group2 = NeuronGroup(3, '')
>>> syn = Synapses(group1, group2)
>>> syn.connect(i=[0, 0, 1, 2], j=[1, 2, 2, 2])
>>> print(syn.N_outgoing_pre)  # for each presynaptic neuron
[2 1 1]
>>> print(syn.N_outgoing[:])  # same numbers, but indexed by synapse
[2 2 1 1]
>>> print(syn.N_incoming_post)
[0 1 3]
>>> print(syn.N_incoming[:])
[1 3 3 3]

Note that N_incoming_post and N_outgoing_pre can contain zeros for neurons that do not have any incoming respectively outgoing synapses. In contrast, N_incoming and N_outgoing will never contain zeros, because unconnected neurons are not represented in the list of synapses.


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), the delay of the postsynaptic pathway can be accessed as post.delay.

The delay variable(s) can be set and accessed in the same way as other synaptic variables. The same semantics as for other synaptic variables apply, which means in particular that the delay is only set for the synapses that have been already created with Synapses.connect. If you want to set a global delay for all synapses of a Synapses object, you can directly specify that delay as part of the Synapses initializer:

synapses = Synapses(sources, targets, '...', on_pre='...', delay=1*ms)

When you use this syntax, you can still change the delay afterwards by setting synapses.delay, but you can only set it to another scalar value. If you need different delays across synapses, do not use this syntax but instead set the delay variable as any other synaptic variable (see above).

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. More convenient is to directly index the Synapses object, Brian will automatically calculate the indices for you in this case:

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

You can also record a synaptic variable for all synapses by passing record=True.

The recorded traces can then be accessed in the usual way, again with the possibility to index the Synapses object:

plot(M.t / ms, M[S[0]].w / nS)  # first synapse
plot(M.t / ms, M[S[0, :]].w / nS)  # all synapses originating from neuron 0
plot(M.t / ms, M[S['w>0*nS']].w / nS)  # all synapses with non-zero weights (at this time)

Note (for users of Brian’s advanced standalone mode only): the use of the Synapses object for indexing and record=True only work in the default runtime modes. In standalone mode (see Standalone code generation), the synapses have not yet been created at this point, so Brian cannot calculate the indices.

The following topics are not essential for beginners.

Synaptic connection/weight matrices

Brian does not directly support specifying synapses by using a matrix, you always have to use a “sparse” format, where each connection is defined by its source and target indices. However, you can easily convert between the two formats. Assuming you have a connection matrix \(C\) of size \(N \times M\), where \(N\) is the number of presynaptic cells, and \(M\) the number of postsynaptic cells, with each entry being 1 for a connection, and 0 otherwise. You can convert this matrix to arrays of source and target indices, which you can then provide to Brian’s connect function:

C = ...  # The connection matrix as a numpy array of 0's and 1's
sources, targets = C.nonzero()
synapses = Synapses(...)
synapses.connect(i=sources, j=targets)

Similarly, you can transform the flat array of values stored in a synapse into a matrix form. For example, to get a matrix with all the weight values w, with NaN values where no synapse exists:

synapses = Synapses(source_group, target_group,
                       w : 1  # synaptic weight''', ...)
# ...
# Run e.g. a simulation with plasticity that changes the weights
# Create a matrix to store the weights and fill it with NaN
W = np.full((len(source_group), len(target_group)), np.nan)
# Insert the values from the Synapses object
W[synapses.i[:], synapses.j[:]] = synapses.w[:]

You can also set synapses given a fully connected weight matrix (as a 2D numpy array W):

synapses.w[:] = W.flatten()

This works because the internal ordering of synapses is exactly the same as for a flattened matrix.

Creating synapses with the generator syntax

The most general way of specifying a connection is using the generator syntax, e.g. to connect neuron i to all neurons j with 0<=j<=i:

S.connect(j='k for k in range(0, i+1)')

There are several parts to this syntax. The general form is:

j='EXPR for VAR in RANGE if COND'


i='EXPR for VAR in RANGE if COND'

Here EXPR can be any integer-valued expression. VAR is the name of the iteration variable (any name you like can be specified here). The if COND part is optional and lets you give an additional condition that has to be true for the synapse to be created. Finally, RANGE can be either:

  1. a Python range, e.g. range(N) is the integers from 0 to N-1, range(A, B) is the integers from A to B-1, range(low, high, step) is the integers from low to high-1 with steps of size step;

  2. a random sample sample(N, p=0.1) gives a random sample of integers from 0 to N-1 with 10% probability of each integer appearing in the sample. This can have extra arguments like range, e.g. sample(low, high, step, p=0.1) will give each integer in range(low, high, step) with probability 10%;

  3. a random sample sample(N, size=10) with a fixed size, in this example 10 values chosen (without replacement) from the integers from 0 to N-1. As for the random sample based on a probability, the sample expression can take additional arguments to sample from a restricted range.

If you try to create an invalid synapse (i.e. connecting neurons that are outside the correct range) then you will get an error, e.g. you might like to try to do this to connect each neuron to its neighbours:

S.connect(j='i+(-1)**k for k in range(2)')

However this won’t work at for i=0 it gives j=-1 which is invalid. There is an option to just skip any synapses that are outside the valid range:

S.connect(j='i+(-1)**k for k in range(2)', skip_if_invalid=True)

You can also use this argument to deal with random samples of incorrect size, i.e. a negative size or a size bigger than the total population size. With skip_if_invalid=True, no error will be raised and a size of 0 or the population size will be used.

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''')
S = Synapses(neuron_input, neurons,
             model='''dg/dt=-a*g+b*x*(1-g) : 1
                      gtot_post = g : 1  (summed)
                      dx/dt=-c*x : 1
                      w : 1 # synaptic weight''', on_pre='x+=w')

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.

Note that you cannot target the same post-synaptic variable from more than one Synapses object. To work around this restriction, use multiple post-synaptic variables that ar then summed up:

neurons = NeuronGroup(1, model='''dv/dt=(gtot-v)/(10*ms) : 1
                                  gtot = gtot1 + gtot2: 1
                                  gtot1 : 1
                                  gtot2 : 1''')
S1 = Synapses(neuron_input, neurons,
              model='''dg/dt=-a1*g+b1*x*(1-g) : 1
                       gtot1_post = g : 1  (summed)
                       dx/dt=-c1*x : 1
                       w : 1 # synaptic weight
                    ''', on_pre='x+=w')
S2 = Synapses(neuron_input, neurons,
              model='''dg/dt=-a2*g+b2*x*(1-g) : 1
                       gtot2_post = g : 1  (summed)
                       dx/dt=-c2*x : 1
                       w : 1 # synaptic weight
                    ''', on_pre='x+=w')

Creating multi-synapses

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

S.connect(i=numpy.arange(10), j=1, n=3)

This is useful for example if one wants to have multiple synapses with different delays. To distinguish multiple variables connecting the same pair of neurons in synaptic expressions and statements, you can create a variable storing the synapse index with the multisynaptic_index keyword:

syn = Synapses(source_group, target_group, model='w : 1', on_pre='v += w',
syn.connect(i=numpy.arange(10), j=1, n=3)
syn.delay = '1*ms + synapse_number*2*ms'

This index can then be used to set/get synapse-specific values:

S.delay = '(synapse_number + 1)*ms)'  # Set delays between 1 and 10ms
S.w['synapse_number<5'] = 0.5
S.w['synapse_number>=5'] = 1

It also enables three-dimensional indexing, the following statement has the same effect as the last one above:

S.w[:, :, 5:] = 1

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, e.g. for a STDP rule that shifted in time. To do this, specify a dictionary of pathway names and codes:

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

This creates two pathways with the given names (in fact, specifying on_pre=code is just a shorter syntax for on_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

As mentioned above, pre pathways are generally executed before post pathways. The order of execution of several pre (or post) pathways with the same delay is however arbitrary, and simply based on the alphabetical ordering of their names (i.e. pre_plasticity will be executed before pre_transmission). To explicitly specify the order, set the order attribute of the pathway, e.g.:

S.pre_transmission.order = -2

will make sure that the pre_transmission code is executed before the pre_plasticity code in each time step.

Multiple pathways can also be useful for abstract models of synaptic currents, e.g. modelling them as rectangular currents:

synapses = Synapses(...,
                    on_pre={'up': 'I_syn_post += 1*nA',
                            'down': 'I_syn_post -= 1*nA'},
                    delay={'up': 0*ms, 'down': 5*ms}  # 5ms-wide rectangular current

Numerical integration

Differential equation flags

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


Declaring a subexpression as (constant over dt) means that it will be evaluated each timestep for all synapses, potentially a very costly operation.

Explicit event-driven updates

As mentioned above, it is possible to write event-driven update code for the synaptic variables. This can also be done manually, by defining the variable lastupdate and referring to the predefined variable t (current time). Here’s an example for short-term plasticity:

           model='''x : 1
                    u : 1
                    w : 1
                    lastupdate : second''',
                  lastupdate = t''')

By default, the pre pathway is executed before the post pathway (both are executed in the 'synapses' scheduling slot, but the pre pathway has the order attribute -1, wheras the post pathway has order 1. See Scheduling for more details).

Note that using the automatic event-driven approach from above is usually preferable, see Example: example_1_COBA for an event-driven implementation of short-term plasticity.

Technical notes

How connection arguments are interpreted

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 uniform random number between 0 and 1 < p(i, j):
Create n(i, j) synapses for (i, j)

With the generator syntax j='EXPR for VAR in RANGE if COND' (where the RANGE can be a full range or a random sample as described above), the interpretation is:

For every i:
for every VAR in RANGE:
j = EXPR
if COND:
Create n(i, j) synapses for (i, j)

Note that the arguments in RANGE can only depend on i and the values of presynaptic variables. Similarly, the expression for j, EXPR can depend on i, presynaptic variables, and on the iteration variable VAR. The condition COND can depend on anything (presynaptic and postsynaptic variables).

The generator syntax expressing i as a function of j is interpreted in the same way:

For every j:
for every VAR in RANGE:
i = EXPR
if COND:
Create n(i, j) synapses for (i, j)

Here, RANGE can only depend on j and postsynaptic variables, and EXPR can only depend on j, postsynaptic variables, and on the iteration variable VAR.

With the 1-to-1 mapping syntax j='EXPR' the interpretation is:

For every i:
j = EXPR
Create n(i, j) synapses for (i, j)

And finally, i='EXPR' is interpreted as:

For every j:
i = EXPR
Create n(i, j) synapses for (i, j)

Efficiency considerations

If you are connecting a single pair of neurons, the direct form connect(i=5, j=10) is the most efficient. However, if you are connecting a number of neurons, it will usually be more efficient to construct an array of i and j values and have a single connect(i=i, j=j) call.

For large connections, you should use one of the string based syntaxes where possible as this will generate compiled low-level code that will be typically much faster than equivalent Python code.

If you are expecting a majority of pairs of neurons to be connected, then using the condition-based syntax is optimal, e.g. connect(condition='i!=j'). However, if relatively few neurons are being connected then the 1-to-1 mapping or generator syntax will be better. For 1-to-1, connect(j='i') will always be faster than connect(condition='i==j') because the latter has to evaluate all N**2 pairs (i, j) and check if the condition is true, whereas the former only has to do O(N) operations.

One tricky problem is how to efficiently generate connectivity with a probability p(i, j) that depends on both i and j, since this requires N*N computations even if the expected number of synapses is proportional to N. Some tricks for getting around this are shown in Example: efficient_gaussian_connectivity.