Synapses (Brian 1 –> 2 conversion)¶

Converting Brian 1’s Connection class¶

In Brian 2, the Synapses class is the only class to model synaptic connections, you will therefore have to convert all uses of Brian 1’s Connection class. The Connection class increases a post-synaptic variable by a certain amount (the “synaptic weight”) each time a pre-synaptic spike arrives. This has to be explicitly specified when using the Synapses class, the equivalent to the basic Connection usage is:

Brian 1 Brian 2
conn = Connection(source, target, 'ge')

conn = Synapses(source, target, 'w : siemens',
on_pre='ge += w')


Note that he variable w, which stores the synaptic weight, has to have the same units as the post-synaptic variable (in this case: ge) that it increases.

Creating synapses and setting weights¶

With the Connection class, creating a synapse and setting its weight is a single process whereas with the Synapses class those two steps are separate. There is no direct equivalent to the convenience functions connect_full, connect_random and connect_one_to_one, but you can easily implement the same functionality with the general mechanism of Synapses.connect():

Brian 1 Brian 2
conn1 = Connection(source, target, 'ge')
conn1[3, 5] = 3*nS

conn1 = Synapses(source, target, 'w: siemens',
on_pre='ge += w')
conn1.connect(i=3, j=5)
conn1.w[3, 5] = 3*nS  # (or conn1.w = 3*nS)

conn2 = Connection(source, target, 'ge')
conn2.connect_full(source, target, 5*nS)

conn2 = ... # see above
conn2.connect()
conn2.w = 5*nS

conn3 = Connection(source, target, 'ge')
conn3.connect_random(source, target,
sparseness=0.02,
weight=2*ns)

conn3 = ... # see above
conn3.connect(p=0.02)
conn3.w = 2*nS

conn4 = Connection(source, target, 'ge')
conn4.connect_one_to_one(source, target,
weight=4*nS)

conn4 = ... # see above
conn4.connect(j='i')
conn4.w = 4*nS

conn5 = IdentityConnection(source, target,
weight=3*nS)

conn5 = Synapses(source, target,
'w : siemens (shared)')
conn5.w = 3*nS


Weight matrices¶

Brian 2’s Synapses class does not support setting the weights of a neuron with a weight matrix. However, Synapses.connect() creates the synapses in a predictable order (first all synapses for the first pre-synaptic cell, then all synapses for the second pre-synaptic cell, etc.), so a reshaped “flat” weight matrix can be used:

Brian 1 Brian 2
# len(source) == 20, len(target) == 30
conn6 = Connection(source, target, 'ge')
W = rand(20, 30)*nS
conn6.connect(source, target, weight=W)

# len(source) == 20, len(target) == 30
conn6 = Synapses(source, target, 'w: siemens',
on_pre='ge += w')
W = rand(20, 30)*nS
conn6.connect()
conn6.w = W.flatten()


However note that if your weight matrix can be described mathematically (e.g. random as in the example above), then you should not create a weight matrix in the first place but use Brian 2’s mechanism to set variables based on mathematical expressions (in the above case: conn5.w = 'rand()'). Especially for big connection matrices this will have better performance, since it will be executed in generated code. You should only resort to explicit weight matrices when there is no alternative (e.g. to load weights from previous simulations).

In Brian 1, you can restrict the functions connect, connect_random, etc. to subgroups. Again, there is no direct equivalent to this in Brian 2, but the general string syntax allows you to make connections conditional on logical statements that refer to pre-/post-synaptic indices and can therefore also used to restrict the connection to a subgroup of cells. When you set the synaptic weights, you can however use subgroups to restrict the subset of weights you want to set.

Brian 1 Brian 2
conn7 = Connection(source, target, 'ge')
conn7.connect_full(source[:5], target[5:10], 5*nS)

conn7 = Synapses(source, target, 'w: siemens',
on_pre='ge += w')
conn7.connect('i < 5 and j >=5 and j <10')
# Alternative (more efficient):
# conn7.connect(j='k in range(5, 10) if i < 5')
conn7.w[source[:5], target[5:10]] = 5*nS


Connections defined by functions¶

Brian 1 allowed you to pass in a function as the value for the weight argument in a connect call (and also for the sparseness argument in connect_random). You should be able to replace such use cases by the the general, string-expression based method:

Brian 1 Brian 2
conn8 = Connection(source, target, 'ge')
conn8.connect_full(source, target,
weight=lambda i,j:(1+cos(i-j))*2*nS)

conn8 = Synapses(source, target, 'w: siemens',
on_pre='ge += w')
conn8.connect()
conn8.w = '(1 + cos(i - j))*2*nS'

conn9 = Connection(source, target, 'ge')
conn9.connect_random(source, target,
sparseness=0.02,
weight=lambda:rand()*nS)

conn9 = ... # see above
conn9.connect(p=0.02)
conn9.w = 'rand()*nS'

conn10 = Connection(source, target, 'ge')
conn10.connect_random(source, target,
sparseness=lambda i,j:exp(-abs(i-j)*.1),
weight=2*ns)

conn10 = ... # see above
conn10.connect(p='exp(-abs(i - j)*.1)')
conn10.w = 2*nS


Delays¶

The specification of delays changed in several aspects from Brian 1 to Brian 2: In Brian 1, delays where homogeneous by default, and heterogeneous delays had to be marked by delay=True, together with the specification of the maximum delay. In Brian 2, homogeneous delays are the default and you do not have to state the maximum delay. Brian 1’s syntax of specifying a pair of values to get randomly distributed delays in that range is no longer supported, instead use Brian 2’s standard string syntax:

Brian 1 Brian 2
conn11 = Connection(source, target, 'ge', delay=True,
max_delay=5*ms)
conn11.connect_full(source, target, weight=3*nS,
delay=(0*ms, 5*ms))

conn11 = Synapses(source, target, 'w : siemens',
on_pre='ge += w')
conn11.connect()
conn11.w = 3*nS
conn11.delay = 'rand()*5*ms'


Modulation¶

In Brian 2, there’s no need for the modulation keyword that Brian 1 offered, you can describe the modulation as part of the on_pre action:

Brian 1 Brian 2
conn12 = Connection(source, target, 'ge',
modulation='u')

conn12 = Synapses(source, target, 'w : siemens',
on_pre='ge += w * u_pre')


Structure¶

There’s no equivalen for Brian 1’s structure keyword in Brian 2, synapses are always stored in a sparse data structure. There is currently no support for changing synapses at run time (i.e. the “dynamic” structure of Brian 1).

Converting Brian 1’s Synapses class¶

Brian 2’s Synapses class works for the most part like the class of the same name in Brian 1. There are however some differences in details, listed below:

Synaptic models¶

The basic syntax to define a synaptic model is unchanged, but the keywords pre and post have been renamed to on_pre and on_post, respectively.

Brian 1 Brian 2
stdp_syn = Synapses(inputs, neurons, model='''
w:1
dApre/dt = -Apre/taupre : 1 (event-driven)
dApost/dt = -Apost/taupost : 1 (event-driven)''',
pre='''ge + =w
Apre += delta_Apre
w = clip(w + Apost, 0, gmax)''',
post='''Apost += delta_Apost
w = clip(w + Apre, 0, gmax)''')

stdp_syn = Synapses(inputs, neurons, model='''
w:1
dApre/dt = -Apre/taupre : 1 (event-driven)
dApost/dt = -Apost/taupost : 1 (event-driven)''',
on_pre='''ge + =w
Apre += delta_Apre
w = clip(w + Apost, 0, gmax)''',
on_post='''Apost += delta_Apost
w = clip(w + Apre, 0, gmax)''')


Lumped variables (summed variables)¶

The syntax to define lumped variables (we use the term “summed variables” in Brian 2) has been changed: instead of assigning the synaptic variable to the neuronal variable you’ll have to include the summed variable in the synaptic equations with the flag (summed):

Brian 1 Brian 2
# a non-linear synapse (e.g. NMDA)
neurons = NeuronGroup(1, model='''
dv/dt = (gtot - v)/(10*ms) : 1
gtot : 1''')
syn = Synapses(inputs, neurons,
model='''
dg/dt = -a*g+b*x*(1-g) : 1
dx/dt = -c*x : 1
w : 1 # synaptic weight''',
pre='x += w')
neurons.gtot=S.g

# a non-linear synapse (e.g. NMDA)
neurons = NeuronGroup(1, model='''
dv/dt = (gtot - v)/(10*ms) : 1
gtot : 1''')
syn = Synapses(inputs, neurons,
model='''
dg/dt = -a*g+b*x*(1-g) : 1
dx/dt = -c*x : 1
w : 1 # synaptic weight
gtot_post = g : 1 (summed)''',
on_pre='x += w')


Creating synapses¶

In Brian 1, synapses were created by assigning True or an integer (the number of synapses) to an indexed Synapses object. In Brian 2, all synapse creation goes through the Synapses.connect() function. For examples how to create more complex connection patterns, see the section on translating Connections objects above.

Brian 1 Brian 2
syn = Synapses(...)
# single synapse
syn[3, 5] = True

syn = Synapses(...)
# single synapse
syn.connect(i=3, j=5)

# all-to-all connections
syn[:, :] = True

# all-to-all connections
syn.connect()

# all to neuron number 1
syn[:, 1] = True

# all to neuron number 1
syn.connect(j='1')

# multiple synapses
syn[4, 7] = 3

# multiple synapses
syn.connect(i=4, j=7, n=3)

# connection probability 2%
syn[:, :] = 0.02

# connection probability 2%
syn.connect(p=0.02)


Multiple pathways¶

As Brian 1, Brian 2 supports multiple pre- or post-synaptic pathways, with separate pre-/post-codes and delays. In Brian 1, you have to specify the pathways as tuples and can then later access them individually by using their index. In Brian 2, you specify the pathways as a dictionary, i.e. by giving them individual names which you can then later use to access them (the default pathways are called pre and post):

Brian 1 Brian 2
S = Synapses(...,
pre=('ge + =w',
'''w = clip(w + Apost, 0, inf)
Apre += delta_Apre'''),
post='''Apost += delta_Apost
w = clip(w + Apre, 0, inf)''')

S[:, :] = True
S.delay[1][:, :] = 3*ms # delayed trace

S = Synapses(...,
pre={'pre_transmission':
'ge += w',
'pre_plasticity':
'''w = clip(w + Apost, 0, inf)
Apre += delta_Apre'''},
post='''Apost += delta_Apost
w = clip(w + Apre, 0, inf)''')

S.connect()
S.pre_plasticity.delay[:, :] = 3*ms # delayed trace


Monitoring synaptic variables¶

Both in Brian 1 and Brian 2, you can record the values of synaptic variables with a StateMonitor. You no longer have to call an explicit indexing function, but you can directly provide an appropriately indexed Synapses object. You can now also use the same technique to index the StateMonitor object to get the recorded values, see the respective section in the Synapses documentation for details.

Brian 1 Brian 2
syn = Synapses(...)
# record all synapse targetting neuron 3
indices = syn.synapse_index((slice(None), 3))
mon = StateMonitor(S, 'w', record=indices)

syn = Synapses(...)
# record all synapse targetting neuron 3
mon = StateMonitor(S, 'w', record=S[:, 3])