Even though AdEx has the capability to adapt, in cases where the input
current or excitatory conductance is very high, for example in case of
a network of excitatory neurons, the firing rates of the AdEx can be
very very high - which is not biologically plausible - since the
exponential term comes in to play too quickly. To remedy this, a
refractory period must be included.
I'm leaving the default refractory period as 0, which is closest to the
original model, but I've added the functionality required to set a
refractory period if users require it.
The following figures will show the differences.
Without a refractory period:
With a 2ms refractory period:
The F-I curve for AdEx without a refractory period - notice the very very high firing rates that the model is capable of in comparison to the TIF with a 2ms refractory period - clearly not biologically plausible. The upper limit here depends on the integration time step in use:
Even though AdEx has the capability to adapt, in cases where the input current or excitatory conductance is very high, for example in case of a network of excitatory neurons, the firing rates of the AdEx can be very very high - which is not biologically plausible - since the exponential term comes in to play too quickly. To remedy this, a refractory period must be included.
I'm leaving the default refractory period as 0, which is closest to the original model, but I've added the functionality required to set a refractory period if users require it.
The following figures will show the differences.
Without a refractory period:
With a 2ms refractory period:
The F-I curve for AdEx without a refractory period - notice the very very high firing rates that the model is capable of in comparison to the TIF with a 2ms refractory period - clearly not biologically plausible. The upper limit here depends on the integration time step in use: