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.
To record spikes from a group G simply create a SpikeMonitor via SpikeMonitor(G). After the simulation, you can access the attributes i, t, it, 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. 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. Finally, the it attribute is just the pair (i, t) for convenience.
G = NeuronGroup(...) M = SpikeMonitor(G) run(...) plot(M.t/ms, M.i, '.')
To record how a variable evolves over time, use a StateMonitor. To use this, 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.
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/mV, label='v') plot(M.t/ms, M.u/mV, label='u')
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)