brainpy.dyn.synapses.GABAa#

class brainpy.dyn.synapses.GABAa(pre, post, conn, conn_type='dense', g_max=0.04, delay_step=None, E=- 80.0, alpha=0.53, beta=0.18, T=1.0, T_duration=1.0, method='exp_auto', name=None)[source]#

GABAa conductance-based 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, ndarray, JaxArray, dict of (str, ndarray), TwoEndConnector) – The synaptic connections.

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

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

  • E (float, JaxArray, ndarray) – The reversal potential for the synaptic current. [mV]

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

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

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

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

  • T_duration (float, JaxArray, ndarray) – 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

1

Destexhe, Alain, and Denis Paré. “Impact of network activity on the integrative properties of neocortical pyramidal neurons in vivo.” Journal of neurophysiology 81.4 (1999): 1531-1547.

__init__(pre, post, conn, conn_type='dense', g_max=0.04, delay_step=None, E=- 80.0, alpha=0.53, beta=0.18, T=1.0, T_duration=1.0, method='exp_auto', name=None)[source]#

Methods

__init__(pre, post, conn[, conn_type, ...])

check_post_attrs(*attrs)

Check whether post group satisfies the requirement.

check_pre_attrs(*attrs)

Check whether pre group satisfies the requirement.

dg(g, t, TT)

get_delay_data(name, delay_step, *indices)

Get delay data according to the provided delay steps.

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.

reset_delay(name, delay_target)

Reset the delay variable.

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.

update_delay(name, delay_data)

Update the delay according to the delay data.

vars([method, level, include_self])

Collect all variables in this node and the children nodes.

Attributes

global_delay_vars

name

steps