# Neural models (Brian 1 –> 2 conversion)¶

The syntax for specifying neuron models in a NeuronGroup changed in several details. In general, a string-based syntax (that was already optional in Brian 1) consistently replaces the use of classes (e.g. VariableThreshold) or guessing (e.g. which variable does threshold=50*mV check).

## Threshold and Reset¶

String-based thresholds are now the only possible option and replace all the methods of defining threshold/reset in Brian 1:

Brian 1 Brian 2
group = NeuronGroup(N, 'dv/dt = -v / tau : volt',
threshold=-50*mV,
reset=-70*mV)

group = NeuronGroup(N, 'dv/dt = -v / tau : volt',
threshold='v > -50*mV',
reset='v = -70*mV')

group = NeuronGroup(N, 'dv/dt = -v / tau : volt',
threshold=Threshold(-50*mV, state='v'),
reset=Reset(-70*mV, state='w'))

group = NeuronGroup(N, 'dv/dt = -v / tau : volt',
threshold='v > -50*mV',
reset='v = -70*mV')

group = NeuronGroup(N, '''dv/dt = -v / tau : volt
dvt/dt = -vt / tau : volt
vr : volt''',
threshold=VariableThreshold(state='v',
threshold_state='vt'),
reset=VariableThreshold(state='v',
resetvaluestate='vr'))

group = NeuronGroup(N, '''dv/dt = -v / tau : volt
dvt/dt = -vt / tau : volt
vr : volt''',
threshold='v > vt',
reset='v = vr')

group = NeuronGroup(N, 'rate : Hz',
threshold=PoissonThreshold(state='rate'))

group = NeuronGroup(N, 'rate : Hz',
threshold='rand()<rate*dt')


There’s no direct equivalent for the “functional threshold/reset” mechanism from Brian 1. In simple cases, they can be implemented using the general string expression/statement mechanism (note that in Brian 1, reset=myreset is equivalent to reset=FunReset(myreset)):

Brian 1 Brian 2
def myreset(P,spikes):
P.v_[spikes] = -70*mV+rand(len(spikes))*5*mV

group = NeuronGroup(N, 'dv/dt = -v / tau : volt',
threshold=-50*mV,
reset=myreset)

group = NeuronGroup(N, 'dv/dt = -v / tau : volt',
threshold='v > -50*mV',
reset='-70*mV + rand()*5*mV')

def mythreshold(v):
return (v > -50*mV) & (rand(N) > 0.5)

group = NeuronGroup(N, 'dv/dt = -v / tau : volt',
threshold=SimpleFunThreshold(mythreshold,
state='v'),
reset=-70*mV)

group = NeuronGroup(N, 'dv/dt = -v / tau : volt',
threshold='v > -50*mV and rand() > 0.5',
reset='v = -70*mV')


For more complicated cases, you can use the general mechanism for User-provided functions that Brian 2 provides. The only caveat is that you’d have to provide an implementation of the function in the code generation target language which is by default C++ or Cython. However, in the default Runtime code generation mode, you can chose different code generation targets for different parts of your simulation. You can thus switch the code generation target for the threshold/reset mechanism to numpy while leaving the default target for the rest of the simulation in place. The details of this process and the correct definition of the functions (e.g. global_reset needs a “dummy” return value) are somewhat cumbersome at the moment and we plan to make them more straightforward in the future. Also note that if you use this kind of mechanism extensively, you’ll lose all the performance advantage that Brian 2’s code generation mechanism provides (in addition to not being able to use Standalone code generation mode at all).

Brian 1 Brian 2
def single_threshold(v):
# Only let a single neuron spike
crossed_threshold = np.nonzero(v > -50*mV)[0]
should_spike = np.zeros(len(P), dtype=np.bool)
if len(crossed_threshold):
choose = np.random.randint(len(crossed_threshold))
should_spike[crossed_threshold[choose]] = True
return should_spike

def global_reset(P, spikes):
# Reset everything
if len(spikes):
P.v_[:] = -70*mV

neurons = NeuronGroup(N, 'dv/dt = -v / tau : volt',
threshold=SimpleFunThreshold(single_threshold,
state='v'),
reset=global_reset)

@check_units(v=volt, result=bool)
def single_threshold(v):
pass # ... (identical to Brian 1)

@check_units(spikes=1, result=1)
def global_reset(spikes):
# Reset everything
if len(spikes):
neurons.v_[:] = -0.070

neurons = NeuronGroup(N, 'dv/dt = -v / tau : volt',
threshold='single_threshold(v)',
reset='dummy = global_reset(i)')
# Set the code generation target for threshold/reset only:
neuron.thresholder['spike'].codeobj_class = NumpyCodeObject
neuron.resetter['spike'].codeobj_class = NumpyCodeObject


For an example how to translate EmpiricalThreshold, see the section on “Refractoriness” below.

## Refractoriness¶

For a detailed description of Brian 2’s refractoriness mechanism see Refractoriness.

In Brian 1, refractoriness was tightly linked with the reset mechanism and some combinations of refractoriness and reset were not allowed. The standard refractory mechanism had two effects during the refractoriness: it prevented the refractory cell from spiking and it clamped a state variable (normally the membrane potential of the cell). In Brian 2, refractoriness is independent of reset and the two effects are specified separately: the refractory keyword specifies the time (or an expression evaluating to a time) during which the cell does not spike, and the (unless refractory) flag marks one or more variables to be clamped during the refractory period. To correctly translate the standard refractory mechanism from Brian 1, you’ll therefore need to specify both:

Brian 1 Brian 2
group = NeuronGroup(N, 'dv/dt = (I - v)/tau : volt',
threshold=-50*mV,
reset=-70*mV,
refractory=3*ms)

group = NeuronGroup(N, 'dv/dt = (I - v)/tau : volt (unless refractory)',
threshold='v > -50*mV',
reset='v = -70*mV',
refractory=3*ms)


More complex refractoriness mechanisms based on SimpleCustomRefractoriness and CustomRefractoriness can be translatated using string expressions or user-defined functions, see the remarks in the preceding section on “Threshold and Reset”.

Brian 2 no longer has an equivalent to the EmpiricalThreshold class (which detects at the first threshold crossing but ignores all following threshold crossings for a certain time after that). However, the standard refractoriness mechanism can be used to implement the same behaviour, since it does not reset/clamp any value if not explicitly asked for it (which would be fatal for Hodgkin-Huxley type models):

Brian 1 Brian 2
group = NeuronGroup(N,'''
dv/dt = (I_L - I_Na - I_K + I)/Cm : volt
...''',
threshold=EmpiricalThreshold(threshold=20*mV,
refractory=1*ms,
state='v'))

group = NeuronGroup(N,'''
dv/dt = (I_L - I_Na - I_K + I)/Cm : volt
...''',
threshold='v > -20*mV',
refractory=1*ms)


## Subgroups¶

The class NeuronGroup in Brian 2 does no longer provide a subgroup method, the only way to construct subgroups is therefore the slicing syntax (that works in the same way as in Brian 1):

Brian 1 Brian 2
group = NeuronGroup(4000, ...)
group_exc = group.subgroup(3200)
group_inh = group.subgroup(800)

group = NeuronGroup(4000, ...)
group_exc = group[:3200]
group_inh = group[3200:]


For a description of Brian 2’s mechanism to link variables between groups, see Linked variables.

Linked variables need to be explicitly annotated with the (linked) flag in Brian 2:

Brian 1 Brian 2
group1 = NeuronGroup(N,
'dv/dt = -v / tau : volt')
group2 = NeuronGroup(N,
'''dv/dt = (-v + w) / tau : volt
w : volt''')

group1 = NeuronGroup(N,