# brainmodels.synapses.GABAa

class brainmodels.synapses.GABAa(pre, post, conn, delay=0.0, g_max=0.04, E=- 80.0, alpha=0.53, beta=0.18, T=1.0, T_duration=1.0, method='exponential_euler', **kwargs)[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

Model Parameters

 Parameter Init Value Unit Explanation delay 0 ms The decay length of the pre-synaptic spikes. g_max 0.04 µmho(µS) Maximum synapse conductance. E -80 mV Reversal potential of synapse. alpha 0.53 Activating rate constant of G protein catalyzed by activated GABAb receptor. beta 0.18 De-activating rate constant of G protein. T 1 mM Transmitter concentration when synapse is triggered by a pre-synaptic spike. T_duration 1 ms Transmitter concentration duration time after being triggered.

Model Variables

 Member name Initial values Explanation g 0 Synapse gating variable. pre_spike False The history of pre-synaptic neuron spikes. spike_arrival_time -1e7 The arrival time of the pre-synaptic neuron spike.

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, delay=0.0, g_max=0.04, E=- 80.0, alpha=0.53, beta=0.18, T=1.0, T_duration=1.0, method='exponential_euler', **kwargs)[source]

Methods

 __init__(pre, post, conn[, delay, g_max, E, ...]) build_inputs([inputs, show_code]) build_monitors([show_code]) cpu() cuda() derivative(g, t, TT) ints([method]) Collect all integrators in this node and the children nodes. jax_update(_t, _dt) load_states(filename[, verbose, check]) Load the model states. nodes([method, _paths]) Collect all children nodes. numpy_update(_t, _dt) register_constant_delay(key, size, delay[, ...]) Register a constant delay. run(duration[, dt, report, inputs, extra_func]) The running function. save_states(filename[, all_vars]) Save the model states. step(t_and_dt, **kwargs) to(devices) tpu() train_vars([method]) The shortcut for retrieving all trainable variables. unique_name([name, type]) Get the unique name for this object. update(*args, **kwargs) The function to specify the updating rule. vars([method]) Collect all variables in this node and the children nodes.

Attributes

 implicit_nodes Used to wrap the implicit children nodes which cannot be accessed by self.xxx implicit_vars Used to wrap the implicit variables which cannot be accessed by self.xxx target_backend Used to specify the target backend which the model to run.