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.