AdExIFLTC

AdExIFLTC#

class brainpy.dyn.AdExIFLTC(size, sharding=None, keep_size=False, mode=None, name=None, spk_fun=InvSquareGrad(alpha=100.0), spk_dtype=None, spk_reset='soft', detach_spk=False, method='exp_auto', init_var=True, scaling=None, V_rest=-65.0, V_reset=-68.0, V_th=-55.0, V_T=-59.9, delta_T=3.48, a=1.0, b=1.0, tau=10.0, tau_w=30.0, R=1.0, V_initializer=ZeroInit, w_initializer=ZeroInit, noise=None)[source]#

Adaptive exponential integrate-and-fire neuron model with liquid time-constant.

Model Descriptions

The adaptive exponential integrate-and-fire model, also called AdEx, is a spiking neuron model with two variables [1] [2].

\[\begin{split}\begin{aligned} \tau_m\frac{d V}{d t} &= - (V-V_{rest}) + \Delta_T e^{\frac{V-V_T}{\Delta_T}} - Rw + RI(t), \\ \tau_w \frac{d w}{d t} &=a(V-V_{rest}) - w \end{aligned}\end{split}\]

once the membrane potential reaches the spike threshold,

\[\begin{split}V \rightarrow V_{reset}, \\ w \rightarrow w+b.\end{split}\]

The first equation describes the dynamics of the membrane potential and includes an activation term with an exponential voltage dependence. Voltage is coupled to a second equation which describes adaptation. Both variables are reset if an action potential has been triggered. The combination of adaptation and exponential voltage dependence gives rise to the name Adaptive Exponential Integrate-and-Fire model.

The adaptive exponential integrate-and-fire model is capable of describing known neuronal firing patterns, e.g., adapting, bursting, delayed spike initiation, initial bursting, fast spiking, and regular spiking.

References

Examples

An example usage:

import brainpy as bp

neu = bp.dyn.AdExIFLTC(2)

# section input with wiener process
inp1 = bp.inputs.wiener_process(500., n=1, t_start=100., t_end=400.).flatten()
inputs = bp.inputs.section_input([0., 22., 0.], [100., 300., 100.]) + inp1

runner = bp.DSRunner(neu, monitors=['V'])
runner.run(inputs=inputs)

bp.visualize.line_plot(runner.mon['ts'], runner.mon['V'], plot_ids=(0, 1), show=True)

Model Examples

Examples for different firing patterns

Model Parameters

Parameter	Init Value	Unit	Explanation
V_rest	-65	mV	Resting potential.
V_reset	-68	mV	Reset potential after spike.
V_th	-30	mV	Threshold potential of spike and reset.
V_T	-59.9	mV	Threshold potential of generating action potential.
delta_T	3.48		Spike slope factor.
a	1		The sensitivity of the recovery variable \(u\) to the sub-threshold fluctuations of the membrane potential \(v\)
b	1		The increment of \(w\) produced by a spike.
R	1		Membrane resistance.
tau	10	ms	Membrane time constant. Compute by R * C.
tau_w	30	ms	Time constant of the adaptation current.
tau_ref		ms	Refractory time.

Model Variables

Variables name	Initial Value	Explanation
V	0	Membrane potential.
w	0	Adaptation current.
input	0	External and synaptic input current.
spike	False	Flag to mark whether the neuron is spiking.
refractory	False	Flag to mark whether the neuron is in refractory period.
t_last_spike	-1e7	Last spike time stamp.

update(x=None)[source]#: The function to specify the updating rule.

AdExIFLTC

Contents

AdExIFLTC#