# Recording during a simulation¶

Recording variables during a simulation is done with “monitor” objects. Specifically, spikes are recorded with SpikeMonitor, the time evolution of variables with StateMonitor and the firing rate of a population of neurons with PopulationRateMonitor.

## Recording spikes¶

To record spikes from a group G simply create a SpikeMonitor via SpikeMonitor(G). After the simulation, you can access the attributes i, t, num_spikes and count of the monitor. The i and t attributes give the array of neuron indices and times of the spikes. For example, if M.i==[0, 2, 1] and M.t==[1*ms, 2*ms, 3*ms] it means that neuron 0 fired a spike at 1 ms, neuron 2 fired a spike at 2 ms, and neuron 1 fired a spike at 3 ms. Alternatively, you can also call the spike_trains method to get a dictionary mapping neuron indices to arrays of spike times, i.e. in the above example, spike_trains = M.spike_trains(); spike_trains[1] would return array([  3.]) * msecond. The num_spikes attribute gives the total number of spikes recorded, and count is an array of the length of the recorded group giving the total number of spikes recorded from each neuron.

Example:

G = NeuronGroup(N, model='...')
M = SpikeMonitor(G)
run(runtime)
plot(M.t/ms, M.i, '.')


If you are only interested in summary statistics but not the individual spikes, you can set the record argument to False. You will then not have access to i and t but you can still get the count and the total number of spikes (num_spikes).

## Recording variables at spike time¶

By default, a SpikeMonitor only records the time of the spike and the index of the neuron that spiked. Sometimes it can be useful to addtionaly record other variables, e.g. the membrane potential for models where the threshold is not at a fixed value. This can be done by providing an extra variables argument, the recorded variable can then be accessed as an attribute of the SpikeMonitor, e.g.:

G = NeuronGroup(10, 'v : 1', threshold='rand()<100*Hz*dt')
G.run_regularly('v = rand()')
M = SpikeMonitor(G, variables=['v'])
run(100*ms)
plot(M.t/ms, M.v, '.')


To conveniently access the values of a recorded variable for a single neuron, the SpikeMonitor.values method can be used that returns a dictionary with the values for each neuron.:

G = NeuronGroup(N, '''dv/dt = (1-v)/(10*ms) : 1
v_th : 1''',
threshold='v > v_th',
# randomly change the threshold after a spike:
reset='''v=0
v_th = clip(v_th + rand()*0.2 - 0.1, 0.1, 0.9)''')
G.v_th = 0.5
spike_mon = SpikeMonitor(G, variables='v')
run(1*second)
v_values = spike_mon.values('v')
print('Threshold crossing values for neuron 0: {}'.format(v_values[0]))
hist(spike_mon.v, np.arange(0, 1, .1))
show()


Note

Spikes are not the only events that can trigger recordings, see Custom events.

## Recording variables continuously¶

To record how a variable evolves over time, use a StateMonitor, e.g. to record the variable v at every time step and plot it for neuron 0:

G = NeuronGroup(...)
M = StateMonitor(G, 'v', record=True)
run(...)
plot(M.t/ms, M.v[0]/mV)


In general, you specify the group, variables and indices you want to record from. You specify the variables with a string or list of strings, and the indices either as an array of indices or True to record all indices (but beware because this may take a lot of memory).

After the simulation, you can access these variables as attributes of the monitor. They are 2D arrays with shape (num_indices, num_times). The special attribute t is an array of length num_times with the corresponding times at which the values were recorded.

Note that you can also use StateMonitor to record from Synapses where the indices are the synapse indices rather than neuron indices.

In this example, we record two variables v and u, and record from indices 0, 10 and 100. Afterwards, we plot the recorded values of v and u from neuron 0:

G = NeuronGroup(...)
M = StateMonitor(G, ('v', 'u'), record=[0, 10, 100])
run(...)
plot(M.t/ms, M.v[0]/mV, label='v')
plot(M.t/ms, M.u[0]/mV, label='u')


There are two subtly different ways to get the values for specific neurons: you can either index the 2D array stored in the attribute with the variable name (as in the example above) or you can index the monitor itself. The former will use an index relative to the recorded neurons (e.g. M.v[1] will return the values for the second recorded neuron which is the neuron with the index 10 whereas M.v[10] would raise an error because only three neurons have been recorded), whereas the latter will use an absolute index corresponding to the recorded group (e.g. M[1].v will raise an error because the neuron with the index 1 has not been recorded and M[10].v will return the values for the neuron with the index 10). If all neurons have been recorded (e.g. with record=True) then both forms give the same result.

Note that for plotting all recorded values at once, you have to transpose the variable values:

plot(M.t/ms, M.v.T/mV)


Note

In contrast to Brian 1, the values are recorded at the beginning of a time step and not at the end (you can set the when argument when creating a StateMonitor, details about scheduling can be found here: Custom progress reporting).

## Recording population rates¶

To record the time-varying firing rate of a population of neurons use PopulationRateMonitor. After the simulation the monitor will have two attributes t and rate, the latter giving the firing rate at each time step corresponding to the time in t. For example:

G = NeuronGroup(...)
M = PopulationRateMonitor(G)
run(...)
plot(M.t/ms, M.rate/Hz)


To get a smoother version of the rate, use PopulationRateMonitor.smooth_rate.

The following topics are not essential for beginners.

## Getting all data¶

Note that all monitors are implement as “groups”, so you can get all the stored values in a monitor with the get_states method, which can be useful to dump all recorded data to disk, for example:

import pickle
group = NeuronGroup(...)
state_mon = StateMonitor(group, 'v', record=...)
run(...)
data = state_mon.get_states(['t', 'v'])
with open('state_mon.pickle', 'w') as f:
pickle.dump(data, f)


## Recording values for a subset of the run¶

Monitors can be created and deleted between runs, e.g. to ignore the first second of your simulation in your recordings you can do:

# Set up network without monitor
run(1*second)
state_mon = StateMonitor(....)
run(...)  # Continue run and record with the StateMonitor


Alternatively, you can set the monitor’s active attribute as explained in the Scheduling section.

## Freeing up memory in long recordings¶

Creating and deleting monitors can also be useful to free memory during a long recording. The following will do a simulation run, dump the monitor data to disk, delete the monitor and finally continue the run with a new monitor:

import pickle
# Set up network
state_mon = StateMonitor(...)
run(...)  # a long run
data = state_mon.get_states(...)
with open('first_part.data', 'w') as f:
pickle.dump(data, f)
del state_mon
del data
state_mon = StateMonitor(...)
run(...)  # another long run


Note that this technique cannot be applied in standalone mode.

## Recording random subsets of neurons¶

In large networks, you might only be interested in the activity of a random subset of neurons. While you can specify a record argument for a StateMonitor that allows you to select a subset of neurons, this is not possible for SpikeMonitor/EventMonitor and PopulationRateMonitor. However, Brian allows you to record with these monitors from a subset of neurons by using a subgroup:

group = NeuronGroup(1000, ...)
spike_mon = SpikeMonitor(group[:100])  # only record first 100 neurons


It might seem like a restriction that such a subgroup has to be contiguous, but the order of neurons in a group does not have any meaning as such; in a randomly ordered group of neurons, any contiguous group of neurons can be considered a random subset. If some aspects of your model do depend on the position of the neuron in a group (e.g. a ring model, where neurons are connected based on their distance in the ring, or a model where initial values or parameters span a range of values in a regular fashion), then this requires an extra step: instead of using the order of neurons in the group directly, or depending on the neuron index i, create a new, shuffled, index variable as part of the model definition and then depend on this index instead:

group = NeuronGroup(10000, '''....
index : integer (constant)''')
indices = group.i[:]
np.random.shuffle(indices)
group.index = indices
# Then use 'index' in string expressions or use it as an index array
# for initial values/parameters defined as numpy arrays


If this solution is not feasible for some reason, there is another approach that works for a SpikeMonitor/EventMonitor. You can add an additional flag to each neuron, stating whether it should be recorded or not. Then, you define a new custom event that is identical to the event you are interested in, but additionally requires the flag to be set. E.g. to only record the spikes of neurons with the to_record attribute set:

group = NeuronGroup(..., '''...
to_record : boolean (constant)''',
threshold='...', reset='...',
events={'recorded_spike': '... and to_record'})
group.to_record = ...
mon_events = EventMonitor(group, 'recorded_spike')


Note that this solution will evaluate the threshold condition for each neuron twice, and is therefore slightly less efficient. There’s one additional caveat: you’ll have to manually include and not_refractory in your events definition if your neuron uses refractoriness. This is done automatically for the threshold condition, but not for any user-defined events.