# brainpy.dyn.base.ConNeuGroup#

class brainpy.dyn.base.ConNeuGroup(size, C=1.0, A=0.001, V_th=0.0, V_initializer=Uniform(min_val=- 70, max_val=- 60.0, seed=None), method='exp_auto', name=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 $$g_j=ar{g}_{j} M^x N^y$$ 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

${dx \over dt} = \phi_x {x_\infty (V) - x \over au_x(V)}$

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



rac{d x}{d t}=phi_{x}left(lpha_{x}(1-x)-eta_{x} x ight)

where $$lpha_{x}$$ and $$eta_{x}$$ are rate constants.

New in version 2.1.9.

sizeint, sequence of int

The network size of this neuron group.

method: str

The numerical integration method.

nameoptional, str

The neuron group name.

__init__(size, C=1.0, A=0.001, V_th=0.0, V_initializer=Uniform(min_val=- 70, max_val=- 60.0, seed=None), method='exp_auto', name=None, **channels)[source]#

Methods

 __init__(size[, C, A, V_th, V_initializer, ...]) derivative(V, t) get_delay_data(name, delay_step, *indices) Get delay data according to the provided delay steps. has(**children_cls) The aggressive operation to gather master and children classes. ints([method]) Collect all integrators in this node and the children nodes. load_states(filename[, verbose]) Load the model states. nodes([method, level, include_self]) Collect all children nodes. register_delay(name, delay_step, delay_target) Register delay variable. register_implicit_nodes(nodes) register_implicit_vars(variables) reset() Reset function which reset the whole variables in the model. save_states(filename[, variables]) Save the model states. train_vars([method, level, include_self]) The shortcut for retrieving all trainable variables. unique_name([name, type_]) Get the unique name for this object. update(t, dt) The function to specify the updating rule. vars([method, level, include_self]) Collect all variables in this node and the children nodes.

Attributes

 global_delay_targets global_delay_vars name steps