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 (NeuDyn) – The pre-synaptic neuron group.

  • post (NeuDyn) – 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, Callable) – The delay length. It should be the value of \(\mathrm{delay\_time / dt}\).

  • g_max (float, ArrayType, 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, ...])

add_aft_update(key, fun)

Add the after update into this node

add_bef_update(key, fun)

Add the before update into this node

add_inp_fun(key, fun)

Add an input function.

check_post_attrs(*attrs)

Check whether post group satisfies the requirement.

check_pre_attrs(*attrs)

Check whether pre group satisfies the requirement.

clear_input(*args, **kwargs)

Empty function of clearing inputs.

cpu()

Move all variable into the CPU device.

cuda()

Move all variables into the GPU device.

get_aft_update(key)

Get the after update of this node by the given key.

get_bef_update(key)

Get the before update of this node by the given key.

get_delay_data(identifier, delay_pos, *indices)

Get delay data according to the provided delay steps.

get_delay_var(name)

get_inp_fun(key)

Get the input function.

get_local_delay(var_name, delay_name)

Get the delay at the given identifier (name).

has_aft_update(key)

Whether this node has the after update of the given key.

has_bef_update(key)

Whether this node has the before update of the given key.

jit_step_run(i, *args, **kwargs)

The jitted step function for running.

load_state(state_dict, **kwargs)

Load states from a dictionary.

load_state_dict(state_dict[, warn, compatible])

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

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])

register_local_delay(var_name, delay_name[, ...])

Register local relay at the given delay time.

reset(*args, **kwargs)

Reset function which reset the whole variables in the model (including its children models).

reset_local_delays([nodes])

Reset local delay variables.

reset_state(*args, **kwargs)

Reset function which resets local states in this model.

save_state(**kwargs)

Save states as a dictionary.

setattr(key, value)

rtype:

None

state_dict(**kwargs)

Returns a dictionary containing a whole state of the module.

step_run(i, *args, **kwargs)

The step run function.

sum_inputs(*args[, init, label])

Summarize all inputs by the defined input functions .cur_inputs.

to(device)

Moves all variables into the given device.

tpu()

Move all variables into the TPU device.

tracing_variable(name, init, shape[, ...])

Initialize the variable which can be traced during computations and transformations.

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([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

g_max

mode

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

name

Name of the model.

supported_modes

Supported computing modes.

cur_inputs