neurodynex3.adex_model package


neurodynex3.adex_model.AdEx module

Implementation of the Adaptive Exponential Integrate-and-Fire model.

See Neuronal Dynamics Chapter 6 Section 1


Simple example to get started


Visualizes the state variables: w-t, v-t and phase-plane w-v

Args: adex_state_monitor (StateMonitor): States of “v” and “w”

neurodynex3.adex_model.AdEx.simulate_AdEx_neuron(tau_m=5. * msecond, R=0.5 * Gohm, v_rest=-70. * mvolt, v_reset=-51. * mvolt, v_rheobase=-50. * mvolt, a=0.5 * nsiemens, b=7. * pamp, v_spike=-30. * mvolt, delta_T=2. * mvolt, tau_w=100. * msecond, I_stim=<brian2.input.timedarray.TimedArray object>, simulation_time=200. * msecond)[source]

Implementation of the AdEx model with a single adaptation variable w.

The Brian2 model equations are:

\[\begin{split}\tau_m \frac{dv}{dt} = -(v-v_{rest}) + \Delta_T \cdot e^{\frac{v-v_{rheobase}}{\Delta_T}} + R I_{stim}(t,i) - R w \\ \tau_w \frac{dw}{dt} = a (v-v_{rest}) - w\end{split}\]
  • tau_m (Quantity) – membrane time scale
  • R (Quantity) – membrane restistance
  • v_rest (Quantity) – resting potential
  • v_reset (Quantity) – reset potential
  • v_rheobase (Quantity) – rheobase threshold
  • a (Quantity) – Adaptation-Voltage coupling
  • b (Quantity) – Spike-triggered adaptation current (=increment of w after each spike)
  • v_spike (Quantity) – voltage threshold for the spike condition
  • delta_T (Quantity) – Sharpness of the exponential term
  • tau_w (Quantity) – Adaptation time constant
  • I_stim (TimedArray) – Input current
  • simulation_time (Quantity) – Duration for which the model is simulated

A b2.StateMonitor for the variables “v” and “w” and a b2.SpikeMonitor

Return type:

(state_monitor, spike_monitor)

Module contents