# 3. AdEx: the Adaptive Exponential Integrate-and-Fire model¶

**Book chapters**

The Adaptive Exponential Integrate-and-Fire model is introduced in Chapter 6 Section 1.

**Python classes**

Use function `AdEx.simulate_AdEx_neuron()`

to run the model for different input currents and different parameters. Get started by running the following script:

```
%matplotlib inline
import brian2 as b2
from neurodynex3.adex_model import AdEx
from neurodynex3.tools import plot_tools, input_factory
current = input_factory.get_step_current(10, 250, 1. * b2.ms, 65.0 * b2.pA)
state_monitor, spike_monitor = AdEx.simulate_AdEx_neuron(I_stim=current, simulation_time=400 * b2.ms)
plot_tools.plot_voltage_and_current_traces(state_monitor, current)
print("nr of spikes: {}".format(spike_monitor.count[0]))
# AdEx.plot_adex_state(state_monitor)
```

## 3.1. Exercise: Adaptation and firing patterns¶

We have implemented an Exponential Integrate-and-Fire model with a single adaptation current `w`

:

### 3.1.1. Question: Firing pattern¶

- When you simulate the model with the default parameters, it produces the voltage trace shown above. Describe that firing pattern. Use the terminology of Fig. 6.1 in Chapter 6.1.
- Call the function
`AdEx.simulate_AdEx_neuron()`

with different parameters and try to create**adapting**,**bursting**and**irregular**firing patterns. Table 6.1 in Chapter 6.2 provides a starting point for your explorations. - In order to better understand the dynamics, it is useful to observe the joint evolution of
`u`

and`w`

in a phase diagram. Use the function`AdEx.plot_adex_state()`

to get more insights. Fig. 6.3 in Chapter 6.2 shows a few trajectories in the phase diagram.

Note

If you want to set a parameter to 0, Brian still expects a unit. Therefore use `a=0*b2.nS`

instead of `a=0`

.

If you do not specify any parameter, the following default values are used:

```
MEMBRANE_TIME_SCALE_tau_m = 5 * b2.ms
MEMBRANE_RESISTANCE_R = 500*b2.Mohm
V_REST = -70.0 * b2.mV
V_RESET = -51.0 * b2.mV
RHEOBASE_THRESHOLD_v_rh = -50.0 * b2.mV
SHARPNESS_delta_T = 2.0 * b2.mV
ADAPTATION_VOLTAGE_COUPLING_a = 0.5 * b2.nS
ADAPTATION_TIME_CONSTANT_tau_w = 100.0 * b2.ms
SPIKE_TRIGGERED_ADAPTATION_INCREMENT_b = 7.0 * b2.pA
```

## 3.2. Exercise: phase plane and nullclines¶

First, try to get some intuition on shape of nullclines by plotting or simply sketching them on a piece of paper and answering the following questions.

- Plot or sketch the
`u`

and`w`

nullclines of the AdEx model (`I(t) = 0`

). - How do the nullclines change with respect to
`a`

? - How do the nullclines change if a constant current
`I(t) = c`

is applied? - What is the interpretation of parameter
`b`

? - How do flow arrows change as
`tau_w`

gets bigger?

### 3.2.1. Question:¶

Can you predict what would be the firing pattern if `a`

is small (in the order of `0.01 nS`

) ? To do so, consider the following 2 conditions:

- A large jump
`b`

and a large time scale`tau_w`

. - A small jump
`b`

and a small time scale`tau_w`

.

Try to simulate the above conditions, to see if your predictions were true.

### 3.2.2. Question:¶

To learn more about the variety of patterns the relatively simple neuron model can reproduce, have a look the following publication: Naud, R., Marcille, N., Clopath, C., Gerstner, W. (2008). Firing patterns in the adaptive exponential integrate-and-fire model. Biological cybernetics, 99(4-5), 335-347.