brainpy.synapses.GABAa#

class brainpy.synapses.GABAa(pre, post, conn, output=COBA, stp=None, comp_method='dense', g_max=0.04, delay_step=None, alpha=0.53, beta=0.18, T=1.0, T_duration=1.0, method='exp_auto', name=None, mode=None, stop_spike_gradient=False)[source]#

GABAa synapse model.

Model Descriptions

GABAa synapse model has the same equation with the AMPA synapse,

\[\begin{split}\frac{d g}{d t}&=\alpha[T](1-g) - \beta g \\ I_{syn}&= - g_{max} g (V - E)\end{split}\]

but with the difference of:

  • Reversal potential of synapse \(E\) is usually low, typically -80. mV

  • Activating rate constant \(\alpha=0.53\)

  • De-activating rate constant \(\beta=0.18\)

  • Transmitter concentration \([T]=1\,\mu ho(\mu S)\) when synapse is triggered by a pre-synaptic spike, with the duration of 1. ms.

Model Examples

Parameters:

  • pre (NeuGroup) – The pre-synaptic neuron group.

  • post (NeuGroup) – The post-synaptic neuron group.

  • conn (optional, ArrayType, dict of (str, ndarray), TwoEndConnector) – The synaptic connections.

  • comp_method (str) – The connection type used for model speed optimization. It can be sparse and dense. The default is dense.

  • delay_step (int, ArrayType, Initializer, Callable) – The delay length. It should be the value of \(\mathrm{delay\_time / dt}\).

  • g_max (float, ArrayType, Initializer, Callable) – The synaptic strength (the maximum conductance). Default is 1.

  • alpha (float, ArrayType) – Binding constant. Default 0.062

  • beta (float, ArrayType) – Unbinding constant. Default 3.57

  • T (float, ArrayType) – Transmitter concentration when synapse is triggered by a pre-synaptic spike.. Default 1 [mM].

  • T_duration (float, ArrayType) – Transmitter concentration duration time after being triggered. Default 1 [ms]

  • name (str) – The name of this synaptic projection.

  • method (str) – The numerical integration methods.

References

__init__(pre, post, conn, output=COBA, stp=None, comp_method='dense', g_max=0.04, delay_step=None, alpha=0.53, beta=0.18, T=1.0, T_duration=1.0, method='exp_auto', name=None, mode=None, stop_spike_gradient=False)[source]#

Methods

__init__(pre, post, conn[, output, stp, ...])

check_post_attrs(*attrs)

Check whether post group satisfies the requirement.

check_pre_attrs(*attrs)

Check whether pre group satisfies the requirement.

clear_input()

cpu()

Move all variable into the CPU device.

cuda()

Move all variables into the GPU device.

dg(g, t, TT)

get_delay_data(identifier, delay_step, *indices)

Get delay data according to the provided delay steps.

load_state_dict(state_dict[, warn, compatible])

Copy parameters and buffers from state_dict into this module and its descendants.

load_states(filename[, verbose])

Load the model states.

nodes([method, level, include_self])

Collect all children nodes.

register_delay(identifier, delay_step, ...)

Register delay variable.

register_implicit_nodes(*nodes[, node_cls])

register_implicit_vars(*variables[, var_cls])

reset(*args, **kwargs)

Reset function which reset the whole variables in the model.

reset_local_delays([nodes])

Reset local delay variables.

reset_state([batch_size])

Reset function which reset the states in the model.

save_states(filename[, variables])

Save the model states.

state_dict()

Returns a dictionary containing a whole state of the module.

to(device)

Moves all variables into the given device.

tpu()

Move all variables into the TPU device.

train_vars([method, level, include_self])

The shortcut for retrieving all trainable variables.

tree_flatten()

Flattens the object as a PyTree.

tree_unflatten(aux, dynamic_values)

Unflatten the data to construct an object of this class.

unique_name([name, type_])

Get the unique name for this object.

update(tdi[, pre_spike])

The function to specify the updating rule.

update_local_delays([nodes])

Update local delay variables.

vars([method, level, include_self, ...])

Collect all variables in this node and the children nodes.

Attributes

global_delay_data

Global delay data, which stores the delay variables and corresponding delay targets.

mode

Mode of the model, which is useful to control the multiple behaviors of the model.

name

Name of the model.