# Input stimuli¶

There are various ways of providing “external” input to a network. Brian does not yet provide all the features of Brian1 in this regard, but there is already a range of options, detailed below.

## Poisson input¶

For generating spikes according to a Poisson point process, `PoissonGroup`

can
be used. It takes a rate or an array of rates (one rate per neuron) as an
argument and can be connected to a `NeuronGroup`

via the usual `Synapses`

mechanism. At the moment, using `PoissonGroup(N, rates)`

is equivalent to
`NeuronGroup(N, 'rates : Hz', threshold='rand()<rates*dt')`

and setting the
group’s `rates`

attribute. The explicit creation of such a `NeuronGroup`

might
be useful if the rates for the neurons are not constant in time, since it allows
using the techniques mentioned below (formulating rates as equations or
referring to a timed array). In the future, the implementation of `PoissonGroup`

will change to a more efficient spike generation mechanism, based on the
calculation of inter-spike intervals. Note that, as can be seen in its equivalent
`NeuronGroup`

formulation, a `PoissonGroup`

does not work for high rates where
more than one spike might fall into a single timestep. Use several units with
lower rates in this case (e.g. use `PoissonGroup(10, 1000*Hz)`

instead of
`PoissonGroup(1, 10000*Hz)`

).

Example use:

```
P = PoissonGroup(100, np.arange(100)*Hz + 10*Hz)
G = NeuronGroup(100, 'dv/dt = -v / (10*ms) : 1')
S = Synapses(P, G, pre='v+=0.1')
S.connect(j='i')
```

For simulations where the `PoissonGroup`

is just used as a source of input to a
neuron (i.e., the individually generated spikes are not important, just their
impact on the target cell), the `PoissonInput`

class provides a more efficient
alternative. Instead of generating spikes, it directly updates a target variable
based on the sum of independent Poisson processes:

```
G = NeuronGroup(100, 'dv/dt = -v / (10*ms) : 1')
P = PoissonInput(G, 'v', 100, 100*Hz, weight=0.1)
```

The `PoissonInput`

class is however more restrictive than `PoissonGroup`

, it
only allows for a constant rate across all neurons (but you can create
several `PoissonInput`

objects, targeting different subgroups). It internally
uses `BinomialFunction`

which will draw a random number each time step, either
from a binomial distribution or from a normal distribution as an approximation
to the binomial distribution if \(n p > 5 \wedge n (1 - p) > 5\), where
\(n\) is the number of inputs and \(p = dt \cdot rate\) the spiking
probability for a single input.

## Spike generation¶

You can also generate an explicit list of spikes given via arrays using
`SpikeGeneratorGroup`

. This object behaves just like a `NeuronGroup`

in that
you can connect it to other groups via a `Synapses`

object, but you specify
three bits of information: `N`

the number of neurons in the group;
`indices`

an array of the indices of the neurons that will fire; and
`times`

an array of the same length as `indices`

with the times that the
neurons will fire a spike. The `indices`

and `times`

arrays are matching,
so for example `indices=[0,2,1]`

and `times=[1*ms,2*ms,3*ms]`

means that
neuron 0 fires at time 1 ms, neuron 2 fires at 2 ms and neuron 1 fires at 3 ms.
Example use:

```
indices = array([0, 2, 1])
times = array([1, 2, 3])*ms
G = SpikeGeneratorGroup(3, indices, times)
```

The spikes that will be generated by `SpikeGeneratorGroup`

can be changed
between runs with the
`set_spikes`

method. This
can be useful if the input to a system should depend on its previous output or
when running multiple trials with different input:

```
inp = SpikeGeneratorGroup(N, indices, times)
G = NeuronGroup(N, '...')
feedforward = Synapses(inp, G, '...', pre='...')
feedforward.connect(j='i')
recurrent = Synapses(G, G, '...', pre='...')
recurrent.connect('i!=j')
spike_mon = SpikeMonitor(G)
# ...
run(runtime)
# Replay the previous output of group G as input into the group
inp.set_spikes(spike_mon.i, spike_mon.t + runtime)
run(runtime)
```

## Explicit equations¶

If the input can be explicitly expressed as a function of time (e.g. a sinusoidal input current), then its description can be directly included in the equations of the respective group:

```
G = NeuronGroup(100, '''dv/dt = (-v + I)/(10*ms) : 1
rates : Hz # each neuron's input has a different rate
size : 1 # and a different amplitude
I = size*sin(2*pi*rates*t) : 1''')
G.rates = '10*Hz + i*Hz'
G.size = '(100-i)/100. + 0.1'
```

## Timed arrays¶

If the time dependence of the input cannot be expressed in the equations in the
way shown above, it is possible to create a `TimedArray`

. Such an objects acts
as a function of time where the values at given time points are given
explicitly. This can be especially useful to describe non-continuous
stimulation. For example, the following code defines a `TimedArray`

where
stimulus blocks consist of a constant current of random strength for 30ms,
followed by no stimulus for 20ms. Note that in this particular example,
numerical integration can use exact methods, since it can assume that the
`TimedArray`

is a constant function of time during a single integration time
step. Also note that the semantics of `TimedArray`

changed slightly compared
to Brian1: for `TimedArray([x1, x2, ...], dt=my_dt)`

, the value `x1`

will be
returned for all `0<=t<my_dt`

, `x2`

for `my_dt<=t<2*my_dt`

etc., whereas
Brian1 returned `x1`

for `0<=t<0.5*my_dt`

,
`x2`

for `0.5*my_dt<=t<1.5*my_dt`

, etc.

```
stimulus = TimedArray(np.hstack([[c, c, c, 0, 0]
for c in np.random.rand(1000)]),
dt=10*ms)
G = NeuronGroup(100, 'dv/dt = (-v + stimulus(t))/(10*ms) : 1',
threshold='v>1', reset='v=0')
G.v = '0.5*rand()' # different initial values for the neurons
```

`TimedArray`

can take a one-dimensional value array (as above) and therefore
return the same value for all neurons or it can take a two-dimensional array
with time as the first and (neuron/synapse/...-)index as the second dimension.

In the following, this is used to implement shared noise between neurons, all the “even neurons” get the first noise instantiation, all the “odd neurons” get the second:

```
runtime = 1*second
stimulus = TimedArray(np.random.rand(int(runtime/defaultclock.dt), 2),
dt=defaultclock.dt)
G = NeuronGroup(100, 'dv/dt = (-v + stimulus(t, i % 2))/(10*ms) : 1',
threshold='v>1', reset='v=0')
```

## Regular operations¶

An alternative to specifying a stimulus in advance is to run explicitly
specified code at certain points during a simulation. This can be
achieved with `run_regularly()`

.
One can think of these statements as
equivalent to reset statements but executed unconditionally (i.e. for all
neurons) and possibly on a different clock than the rest of the group. The
following code changes the stimulus strength of half of the neurons (randomly
chosen) to a new random value every 50ms. Note that the statement uses logical
expressions to have the values only updated for the chosen subset of neurons
(where the newly introduced auxiliary variable `change`

equals 1):

```
G = NeuronGroup(100, '''dv/dt = (-v + I)/(10*ms) : 1
I : 1 # one stimulus per neuron''')
G.run_regularly('''change = int(rand() < 0.5)
I = change*(rand()*2) + (1-change)*I''',
dt=50*ms)
```

## Arbitrary Python code (network operations)¶

If none of the above techniques is general enough to fulfill the requirements
of a simulation, Brian allows you to write a `NetworkOperation`

, an arbitrary
Python function that is executed every time step (possible on a different clock
than the rest of the simulation). This function can do arbitrary operations,
use conditional statements etc. and it will be executed as it is (i.e. as pure
Python code even if weave code generation is active). Note that one cannot use
network operations in combination with the C++ standalone mode. Network
operations are particularly useful when some condition or calculation depends
on operations across neurons, which is currently not possible to express in
abstract code. The following code switches input on for a randomly chosen single
neuron every 50 ms:

```
G = NeuronGroup(10, '''dv/dt = (-v + active*I)/(10*ms) : 1
I = sin(2*pi*100*Hz*t) : 1 (shared) #single input
active : 1 # will be set in the network operation''')
@network_operation(dt=50*ms)
def update_active():
index = np.random.randint(10) # index for the active neuron
G.active_ = 0 # the underscore switches off unit checking
G.active_[index] = 1
```

Note that the network operation (in the above example: `update_active`

) has
to be included in the `Network`

object if one is constructed explicitly.

Only functions with zero or one arguments can be used as a `NetworkOperation`

.
If the function has one argument then it will be passed the current time `t`

:

```
@network_operation(dt=1*ms)
def update_input(t):
if t>50*ms and t<100*ms:
pass # do something
```

Note that this is preferable to accessing `defaultclock.t`

from within the
function – if the network operation is not running on the `defaultclock`

itself, then that value is not guaranteed to be correct.

Instance methods can be used as network operations as well, however in this case
they have to be constructed explicitly, the `network_operation()`

decorator
cannot be used:

```
class Simulation(object):
def __init__(self, data):
self.data = data
self.group = NeuronGroup(...)
self.network_op = NetworkOperation(self.update_func, dt=10*ms)
self.network = Network(self.group, self.network_op)
def update_func(self):
pass # do something
def run(self, runtime):
self.network.run(runtime)
```