Some specific types of neuron groups are available to provide inputs to a network.

Poisson inputs

Poisson spike trains can be generated as follows:


Here 100 neurons are defined, which emit spikes independently according to Poisson processes with rates 10 Hz. To have different rates across the group, initialise with an array of rates:


Inhomogeneous Poisson processes can be defined by passing a function of time that returns the rates:

group=PoissonGroup(100,rates=lambda t:(1+cos(t))*10*Hz)


group=PoissonGroup(100,rates=lambda t:(1+cos(t))*r0)

There is another class for Poisson inputs: PoissonInput, which updates the state variable of a NeuronGroup dynamically without storing in memory all the Poisson events. It can be used like this:

input = PoissonInput(group, N=N, rate=rate, weight=w, state='I')

In this case, the variable I represents the sum of N independent Poisson spike inputs with rate rate, where each individual synaptic event increases the variable I by w. Several PoissonInput objects can be created for a given NeuronGroup, in which case all the independent inputs are linearly superimposed.

Other features of the PoissonInput class include the following (see the reference):
  • record the individual Poisson events (record=True keyword),
  • having identical Poisson events for all neurons, instead of having independent copies for every neuron (freeze=True keyword)
  • copying every Poisson input a specified number of times (copies=p keyword). This is equivalent of specifying weight=p*w, except that those copies can be randomly shifted (jitter keyword), or can be unreliable to model synapse unreliability (reliability keyword). The latter case corresponds to a Binomial synaptic weight.

Correlated inputs

Generation of correlated spike trains is partially implemented, using algorithms from the the following paper: Brette, R. (2009) Generation of correlated spike trains, Neural Computation 21(1): 188-215. Currently, only the method with Cox processes (or doubly stochastic processes, first method in the paper) is fully implemented.

Doubly stochastic processes

To generate correlated spike trains with identical rates and homogeneous exponential correlations, use the class HomogeneousCorrelatedSpikeTrains:


where r is the rate, c is the total correlation strength and tauc is the correlation time constant. The cross-covariance functions are (c*r/tauc)*exp(-|s|/tauc). To generate correlated spike trains with arbitrary rates r(i) and cross-covariance functions c(i,j)*exp(-|s|/tauc), use the class CorrelatedSpikeTrains:


where rates is the vector of rates r(i), C is the correlation matrix (which must be symmetrical) and tauc is the correlation time constant. Note that distortions are introduced with strong correlations and short correlation time constants. For short time constants, the mixture method is more appropriate (see the paper above). The two classes HomogeneousCorrelatedSpikeTrains and CorrelatedSpikeTrains define neuron groups, which can be directly used with Connection objects.

Mixture method

The mixture method to generate correlated spike trains is only partially implemented and the interface may change in future releases. Currently, one can use the function mixture_process() to generate spike trains:


where nu is the vector of rates of the source spike trains, P is the mixture matrix (entries between 0 and 1), tauc is the correlation time constant, t is the duration. It returns a list of (neuron_number,spike_time), which can be passed to SpikeGeneratorGroup. This method is appropriate for short time constants and is explained in the paper mentioned above.

Input spike trains

A set of spike trains can be explicitly defined as list of pairs (i,t) (meaning neuron i fires at time t), which used to initialise a SpikeGeneratorGroup:

spiketimes=[(0,1*ms), (1,2*ms)]

The neuron 0 fires at time 1 ms and neuron 1 fires at time 2 ms (there are 5 neurons, but 3 of them never spike). One may also pass a generator instead of a list (in that case the pairs should be ordered in time).

Gaussian spike packets

There is a subclass of SpikeGeneratorGroup for generating spikes with a Gaussian distribution:


Here 10 spikes are produced, with spike times distributed according a Gaussian distribution with mean 10 ms and standard deviation 3 ms.

Direct input

Inputs may also be defined by accessing directly the state variables of a neuron group. The standard way to do this is to insert parameters in the equations:

eqs = '''
dv/dt = (I-v)/tau : volt
I : volt
group = NeuronGroup(100, model=eqs, reset=0*mV, threshold=15*mV)
group.I = linspace(0*mV, 20*mV, 100)

Here the value of the parameter I for each neuron is provided at initialisation time (evenly distributed between 0 mV and 20 mV).

Time varying inputs

It is possible to change the value of I every timestep by using a user-defined operation (see next section). Alternatively, you can use a TimedArray to specify the values the variable will have at each time interval, for example:

eqs = '''
dv/dt = (I-v)/tau : volt
I : volt
group = NeuronGroup(1, model=eqs, reset=0*mV, threshold=15*mV)
group.I = TimedArray(linspace(0*mV, 20*mV, 100), dt=10*ms)

Here I will have value 0*mV for t between 0 and 10*ms`, ``0.2*mV between 10*ms and 20*ms, and so on. A more intuitive syntax is:

I = TimedArray(linspace(0*mV, 20*mV, 100), dt=10*ms)
eqs = '''
dv/dt = (I(t) * volt - v)/tau : volt
group = NeuronGroup(1, model=eqs, reset=0*mV, threshold=15*mV)

Note however that the more efficient exact linear differential equations solver won’t be used in this case because I(t) could be any function, so the previous mechanism is often preferable. Additionally, be aware that the call to I(t) does return a value without units (as units cannot be stored in arrays), therefore you have to explicitly multiply it with the respective unit.

Linked variables

Another option is to link the variable of one group to the variables of another group using linked_var(), for example:

G = NeuronGroup(...)
H = NeuronGroup(...)
G.V = linked_var(H, 'W')

In this scenario, the variable V in group G will always be updated with the values from variable W in group H. The groups G and H must be the same size (although subgroups can be used if they are not the same size).