# Inputs (Brian 1 –> 2 conversion)

## Poisson Input

Brian 2 provides the same two groups that Brian 1 provided: `PoissonGroup`

and
`PoissonInput`

. The mechanism for inhomogoneous Poisson processes has changed:
instead of providing a Python function of time, you’ll now have to provide a
string expression that is evaluated at every time step. For most use cases, this
should allow a direct translation:

Brian 1 |
Brian 2 |
---|---|

```
rates = lambda t:(1+cos(2*pi*t*1*Hz))*10*Hz
group = PoissonGroup(100, rates=rates)
``` |
```
rates = '(1 + cos(2*pi*t*1*Hz)*10*Hz)'
group = PoissonGroup(100, rates=rates)
``` |

For more complex rate modulations, the expression can refer to
User-provided functions and/or you can replace the `PoissonGroup`

by a general
`NeuronGroup`

with a threshold condition `rand()<rates*dt`

(which allows you
to store per-neuron attributes).

There is currently no direct replacement for the more advanced features of
`PoissonInput`

(`record`

, `freeze`

, `copies`

, `jitter`

, and
`reliability`

keywords), but various workarounds are possible, e.g. by
directly using a `BinomialFunction`

in the equations. For example, you can get
the functionality of the `freeze`

keyword (identical Poisson events for all
neurons) by storing the input in a shared variable and then distribute the input
to all neurons:

Brian 1 |
Brian 2 |
---|---|

```
group = NeuronGroup(10,
'dv/dt = -v/(10*ms) : 1')
input = PoissonInput(group, N=1000, rate=1*Hz,
weight=0.1, state='v',
freeze=True)
``` |
```
group = NeuronGroup(10, '''dv/dt = -v / (10*ms) : 1
shared_input : 1 (shared)''')
poisson_input = BinomialFunction(n=1000, p=1*Hz*group.dt)
group.run_regularly('''shared_input = poisson_input()*0.1
v += shared_input''')
``` |

## Spike generation

`SpikeGeneratorGroup`

provides mostly the same functionality as in Brian 1. In
contrast to Brian 1, there is only one way to specify which neurons spike and
when – you have to provide the index array and the times array as separate
arguments:

Brian 1 |
Brian 2 |
---|---|

```
gen1 = SpikeGeneratorGroup(2, [(0, 0*ms), (1, 1*ms)])
gen2 = SpikeGeneratorGroup(2, [(array([0, 1]), 0*ms),
(array([0, 1]), 1*ms)]
gen3 = SpikeGeneratorGroup(2, (array([0, 1]),
array([0, 1])*ms))
gen4 = SpikeGeneratorGroup(2, array([[0, 0.0],
[1, 0.001]])
``` |
```
gen1 = SpikeGeneratorGroup(2, [0, 1], [0, 1]*ms)
gen2 = SpikeGeneratorGroup(2, [0, 1, 0, 1],
[0, 0, 1, 1]*ms)
gen3 = SpikeGeneratorGroup(2, [0, 1], [0, 1]*ms)
gen4 = SpikeGeneratorGroup(2, [0, 1], [0, 1]*ms)
``` |

Note

For large arrays, make sure to provide a `Quantity`

array (e.g.
`[0, 1, 2]*ms`

) and not a list of `Quantity`

values (e.g.
`[0*ms, 1*ms, 2*ms]`

). A list has first to be translated into an array
which can take a considerable amount of time for a list with many elements.

There is no direct equivalent of the Brian 1 option to use a generator that
updates spike times online. The easiest alternative in Brian 2 is to
pre-calculate the spikes and then use a standard `SpikeGeneratorGroup`

. If this
is not possible (e.g. there are two many spikes to fit in memory), then you can
workaround the restriction by using custom code (see User-provided functions and
Arbitrary Python code (network operations)).

## Arbitrary time-dependent input (`TimedArray`

)

For a detailed description of the `TimedArray`

mechanism in Brian 2, see
Timed arrays.

In Brian 1, timed arrays where special objects that could be assigned to a
state variable and would then be used to update this state variable at every
time step. In Brian 2, a timed array is implemented using the standard
Functions mechanism which has the advantage that more
complex access patterns can be implemented (e.g. by not using `t`

as an
argument, but something like `t - delay`

). This syntax was possible in Brian 1
as well, but was disadvantageous for performance and had other limits (e.g. no
unit support, no linear integration). In Brian 2, these disadvantages no longer
apply and the function syntax is therefore the only available syntax. You can
convert the old-style Brian 1 syntax to Brian 2 as follows:

Warning

The example below does not correctly translate the changed semantics of
`TimedArray`

related to the time. In Brian 1,
`TimedArray([0, 1, 2], dt=10*ms)`

will return `0`

for `t<5*ms`

, `1`

for `5*ms<=t<15*ms`

, and `2`

for `t>=15*ms`

. Brian 2 will return `0`

for `t<10*ms`

, `1`

for `10*ms<=t<20*ms`

, and `2`

for `t>=20*ms`

.

Brian 1 |
Brian 2 |
---|---|

```
# same input for all neurons
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)
``` |
```
# same input for all neurons
I = TimedArray(linspace(0*mV, 20*mV, 100),
dt=10*ms)
eqs = '''
dv/dt = (I(t) - v)/tau : volt
'''
group = NeuronGroup(1, model=eqs,
reset='v = 0*mV',
threshold='v > 15*mV')
``` |

```
# neuron-specific input
eqs = '''
dv/dt = (I - v)/tau : volt
I : volt
'''
group = NeuronGroup(5, model=eqs,
reset=0*mV, threshold=15*mV)
values = (linspace(0*mV, 20*mV, 100)[:, None] *
linspace(0, 1, 5))
group.I = TimedArray(values, dt=10*ms)
``` |
```
# neuron-specific input
values = (linspace(0*mV, 20*mV, 100)[:, None] *
linspace(0, 1, 5))
I = TimedArray(values, dt=10*ms)
eqs = '''
dv/dt = (I(t, i) - v)/tau : volt
'''
group = NeuronGroup(5, model=eqs,
reset='v = 0*mV',
threshold='v > 15*mV')
``` |