brainmodels.synapses.VoltageJump

class brainmodels.synapses.VoltageJump(pre, post, conn, delay=0.0, post_has_ref=False, w=1.0, post_key='V', **kwargs)[source]

Voltage jump synapse model.

Model Descriptions

\[I_{syn} (t) = \sum_{j\in C} w \delta(t-t_j-D)\]

where \(w\) denotes the chemical synaptic strength, \(t_j\) the spiking moment of the presynaptic neuron \(j\), \(C\) the set of neurons connected to the post-synaptic neuron, and \(D\) the transmission delay of chemical synapses. For simplicity, the rise and decay phases of post-synaptic currents are omitted in this model.

Model Examples

Model Parameters

Parameter

Init Value

Unit

Explanation

w

1

mV

The synaptic strength.

__init__(pre, post, conn, delay=0.0, post_has_ref=False, w=1.0, post_key='V', **kwargs)[source]

Methods

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

build_inputs([inputs, show_code])

build_monitors([show_code])

cpu()

cuda()

derivative(a, t)

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.