class brainpy.dyn.CondNeuGroupLTC(size, keep_size=False, C=1.0, A=0.001, V_th=0.0, V_initializer=Uniform(min_val=-70, max_val=-60.0, rng=[ 479025946 4206239744]), noise=None, method='exp_auto', name=None, mode=None, init_var=True, input_var=True, spk_type=None, **channels)[source]#

Base class to model conductance-based neuron group.

The standard formulation for a conductance-based model is given as

\[C_m {dV \over dt} = \sum_jg_j(E - V) + I_{ext}\]

where \(g_j=\bar{g}_{j} M^x N^y\) is the channel conductance, \(E\) is the reversal potential, \(M\) is the activation variable, and \(N\) is the inactivation variable.

\(M\) and \(N\) have the dynamics of

\[{dx \over dt} = \phi_x {x_\infty (V) - x \over \tau_x(V)}\]

where \(x \in [M, N]\), \(\phi_x\) is a temperature-dependent factor, \(x_\infty\) is the steady state, and \(\tau_x\) is the time constant. Equivalently, the above equation can be written as:

\[\frac{d x}{d t}=\phi_{x}\left(\alpha_{x}(1-x)-\beta_{x} x\right)\]

where \(\alpha_{x}\) and \(\beta_{x}\) are rate constants.

New in version 2.1.9: Model the conductance-based neuron model.

  • size (int, sequence of int) – The network size of this neuron group.

  • method (str) – The numerical integration method.

  • name (optional, str) – The neuron group name.


Useful for monitoring inputs.


Reset function which resets local states in this model.

Simply speaking, this function should implement the logic of resetting of local variables in this node.

See for details.


The function to specify the updating rule.